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KERALA
TECHNOLOGICAL
UNIVERSITY
Master of Technology
Curriculum, Syllabus and Course Plan
Cluster
:
01
Branch
:
Mechanical Engineering
Stream
:
Financial Engineering
Year
:
2015
No. of Credits
:
67
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Name
Duration
(hours)
Credits
End Semester
Examination
Marks
L-T-P
3-0-0
40
60
3
3
3-1-0
40
60
3
4
3-1-0
40
60
3
4
Financial Reporting and Analysis
3-0-0
40
60
3
3
Elective I
3-0-0
40
60
3
3
A
01MA6017
B
01ME6601
C
01ME6603
Statistics for Financial Engineering
Applications
Financial Time Series Analysis
D
01ME6605
E
Internal Marks
Course Number
Examination Slot
SEMESTER 1
Probability and Stochastic Processes
S
01ME6999
Research Methodology
0-2-0
100
2
T
01ME6691
SeminarI
0-0-2
100
2
U
01ME6693
Data AnalysisLaboratory
0-0-2
100
1
15-4-4
500
TOTAL
TOTAL CONTACT HOURS
TOTAL CREDITS
:
:
300
-
23
22
Elective I
01ME6611
Corporate Finance and Portfolio Management
01ME6613
Numerical Methods for Finance
01ME6615
Object Oriented Programming for Financial Engineers
Cluster: 1
Branch: Mechanical Engineering
2
Stream: Financial Engineering
22
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
SEMESTER 2
Course Number
Marks
Duration
(hours)
Credits
A
01ME6602
Optimization in Finance
3-1-0
40
60
3
4
B
01ME6604
Equity and Fixed Income
3-0-0
40
60
3
3
C
D
E
01ME6406
System Analysis and Design
Elective II
Elective III
3-0-0
3-0-0
3-0-0
40
40
40
60
60
60
3
3
3
3
3
3
V
01ME6692
Mini Project
0-0-4
100
2
U
01ME6694
Optimization and Simulation
Laboratory
0-0-2
100
1
TOTAL
15-1-6
400
Name
:
:
TOTAL CONTACT HOURS
TOTAL CREDITS
L-T-P
Internal Marks
Examination Slot
End Semester
Examination
300
-
22
19
Elective II
01ME6612
Derivatives and Alternative Investments
01ME6414
Data Analytics using R and Python
01ME6616
Financial Markets
Elective III
01ME6618
Quantitative Trading Strategies
01ME6422
Enterprise Resource Planning
01ME6624
Big Data Analytics
Cluster: 1
Branch: Mechanical Engineering
3
Stream: Financial Engineering
19
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Name
Duration
(hours)
Credits
End Semester
Examination
Marks
L-T-P
Internal Marks
Course Number
Examination Slot
SEMESTER 3
A
Elective IV
3-0-0
40
60
3
3
B
Elective V
3-0-0
40
60
3
3
T
01ME7691
Seminar II
0-0-2
100
2
W
01ME7693
Project (Phase 1)
0-0-12
50
6
TOTAL
6-0-14
230
TOTAL CONTACT HOURS
TOTAL CREDITS
:
:
120
20
14
Elective IV
01ME7611
Asset Pricing
01ME7613
Financial Business Intelligence
01ME7415
Heuristic Solution Methods
Elective V
01ME7617
Predictive Modeling
01ME7419
Managerial Economics
01ME7621
Financial Modeling
Cluster: 1
Branch: Mechanical Engineering
4
Stream: Financial Engineering
-
14
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
W 01ME7694
Project (Phase 2)
0-0-23
70
30
TOTAL
0-0-23
70
30
TOTAL CONTACT HOURS
TOTAL CREDITS
:
:
12
-
23
12
TOTAL NUMBER OF CREDITS: 67
Cluster: 1
Branch: Mechanical Engineering
5
Credit
Duration
(hours)
L-T-P
End Semester
Examination
Marks
Name
Internal Marks
Course Number
Examination Slot
SEMESTER 4
Stream: Financial Engineering
12
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
SEMESTER - I
Syllabus and Course Plan
Cluster: 1
Branch: Mechanical Engineering
6
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Course No.
01MA6017
Course Name
PROBABILITY AND
STOCHASTIC PROCESSES
L-T-P
Credits
Year of Introduction
3-0-0
3
2015
Course Objectives
The objective of this course is to reinforce basic ideas of probability distributions they may already
have learned, from a modern point of view. The basic ideas of stochastic processes are also
introduced, preparing the students with the necessary tools for its diverse applications in applied
sciences and engineering. This course provides a strong background of some basic mathematical
methods which will be essential for higher studies and research in engineering
Syllabus
Techniques for theorem proving, Principle of mathematical induction, principle of complete
Multiple random variables, Conditional distributions, limit theorems, Discrete time Markov chains,
Continuous time Markov chains, Poisson Process, Renewal process, Brownian motion.
Expected Outcome
On completion of the course, the students will have acquired knowledge and practical skills in the
modeling and analysis of probabilistic and stochastic systems which has applications in diverse
areas of engineering. This will also prepare them with some of the most important mathematical
tools essential for higher studies and research.
References
1. Saeed Ghahramani , ” Fundamentals of Probability with Stochastic process”, Pearson.
2. V G Kulkarni, “Introduction to Modeling and Analysis of Stochastic Systems”,
Springer
3. S.M.Ross, ”Introduction to probability models”, Elsevier
I
Multiple random variables: Joint and Marginal distributions,
4
Independence of random variables, Covariance, Correlation
3
Cluster: 1
Branch: Mechanical Engineering
7
Stream: Financial Engineering
End-Semester
Examination
Contents
% of Marks in
Hours Allotted
Module
COURSE PLAN
15
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Conditional probability distributions and Conditional expectations.
II
Distributions of sum of two random variables.
Limit theorems: Central limit theorem and Law of large numbers
(without proof).
4
15
3
FIRST INTERNAL EXAM
Stochastic process and their classifications
III
Discrete time Markov chains: Transition probability matrix, ChapmanKolmogorov Equation, classification of states, Ergodic chains, Steady
7
15
State Probabilities. First passage times, computation of expected first
passage times.
Continuous - time Markov chains: Transition probability matrix,
Chapman, Kolmogorov Equations, transition rates and rate matrix and
IV
4
15
generator matrix.
Steady State Probabilities and flow balance equations, Birth - death
processes, First passage times.
3
SECOND INTERNAL EXAM
V
Poisson processes- Inter-arrival distribution, Reproductive properties.
Renewal processes-basic properties
Renewal Reward process, Limit theorems(without proof)
VI
First
Brownian motion with drift, Geometric Brownian motion(ideas and
4
20
3
END SEMESTER EXAM
Cluster: 1
Branch: Mechanical Engineering
8
20
3
Standard Brownian motion (Wiener processes), basic properties,
passage times of standard Brownian motion
computations without proof)
4
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Course No.
01ME6601
Course Name
STATISTICS FOR
FINANCIAL ENGINEERING
APPLICATIONS
L-T-P
Credits
Year of Introduction
3-1-0
4
2015
Course Objectives
1. To provide an introduction to statistical techniques and their applications in the context of
finance, business and management problems.
2. To utilize single and multi-variable measures to make financial decisions.
3. To perform and interpret elementary statistical procedures (such as confidence intervals
and hypothesis tests).
4. To develop financial decision making and analytical skills.
Syllabus
Data collection, classification and tabulation – Exploratory data analysis - Measures of Central
Tendency – Measures of Dispersion – Sampling and Sampling Distributions – Estimation and
Confidence Intervals – Hypothesis Testing – Non Parametric Tests – Analysis of Variance –
Correlation Analysis – Copulas - Regression Analysis – Introduction to Multivariate Analysis –
Utility of ‘R’.
Expected Outcome
1.
2.
The student will be able to apply techniques for analyzing and interpreting data to real-world
datasets relevant to varied fields of finance and business.
The student will be able to critically evaluate financial reports presenting statistical data and
translate and communicate the results of statistical analyses.
References
1. P. E. Green, D. S. Tull, G. Albaum, “Research for Marketing decisions”, Prentice- hall of
India Pvt. Ltd
2. Thomas C. Kinnear, James R. Taylor, “Marketing Research: An Applied approach”,
McGraw-Hill Inc
3. A. B. Bowker and G. J. Liberman, “Engineering Statistics”, Asia, 1972.
4. F. E. Brown, “Marketing Research: A structure for decision making”,Addison-Wesley
publishing Co., California.
5. J.K. Sharma, “Business Statistics”, Pearson Education.
6. R. Panneerselvam, “Research Methodology”, Prentice Hall India.
7. Amir D Aczel and Jayavel Sounderpandian, “Complete Business Statistics”, Tata
McGraw-Hill
8. Richard I Levin and David S Rubin, “Statistics for Management”, Pearson Education
9. Hair et al., “Multivariate Data Analysis”, Pearson Education
10. David Ruppert and David S Matteson, “Statistics and Data Analysis for Financial
Engineering, Springer
Cluster: 1
Branch: Mechanical Engineering
9
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
11. Rene Carmona, “Statistical Analysis of Financial Data in R”, Second Edition, Springer.
I
II
III
IV
V
VI
Data Collection, Classification and Tabulation: Need for data, Types
of Data, Scale of measurement, Sources of data, Methods of data
classification, Tabulation of data, Presentation of data, Exploratory data
analysis- Histograms and Kernel Density Estimation, Normal and half
normal probability plots, Q-Q plots, Box plots.
Measures of Central Tendency: Significance, Classification - Arithmetic
mean (Grouped and Ungrouped data), Geometric mean, Harmonic
mean, Median, Mode, Quartiles, Deciles and Percentiles.
Measures of Dispersion: Significance, Classification – Range,
Interquartile range, Mean Absolute Deviation, Variance and Standard
deviation, Coefficient of variation, Chebyshev’s theorem, Skewness,
Moments and Kurtosis, Jarque -Bera test of normality
FIRST INTERNAL EXAM
Sampling and Sampling Distributions: Population parameters and
Sample statistics, Sampling methods, Sampling distribution of sample
mean, Sampling distribution of sample proportions.
Estimation and Confidence Intervals: Point estimation, Confidence
Interval estimation – Interval estimation of population means (σ known
and σ unknown).
Hypothesis Testing: Procedure, Hypothesis testing for population
parameters with large samples and small samples. Hypothesis testing
based on F- Distribution.
SECOND INTERNAL EXAM
Non Parametric tests: One sample tests- Chi-square tests, K-S Test,
Two Sample tests – Sign test, Median test, Mann-Whitney U-test, KSamples test – Median test, Kruskal-Walis test.
Design of Experiments: Analysis of Variance, One way and two-way
ANOVA, Factorial design, 2n Factorial experiment, Yate’s algorithm.
Correlation Analysis: Karl Pearson’s correlation, Spearman’s rank
correlation, Auto correlation, Copulas (overview).
Cluster: 1
Branch: Mechanical Engineering
10
5
End-Semester
Examination
Contents
% of Marks in
Hours Allotted
Module
COURSE PLAN
10
5
15
6
5
10
4
15
7
6
25
7
3
Stream: Financial Engineering
25
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Regression Analysis: Simple and Multiple Regression models,
Determination of regression coefficients, Coefficient of determination,
Adjusted R2, Significance test of Regression model
Introduction to Multivariate Analysis: Overview of Discriminant
Analysis, Factor Analysis, Cluster Analysis, Multidimensional scaling
and Conjoint Analysis
Utility of ‘R’ in statistical analysis of Financial Data – An overview
END SEMESTER EXAM
Course No.
01ME6603
Course Name
FINANCIAL TIME SERIES
ANALYSIS
6
2
L-T-P
Credits
Year of Introduction
3-1-0
4
2015
Course Objectives
1. To introduce a variety of statistical models for time series and main methods for analyzing
these models.
Syllabus
Characteristics of Financial Time Series; Linear Time Series Analysis; Conditional Heteroscedastic
Models; Nonlinear Models; Continuous-Time Models; Multivariate Time Series Analysis; Factor
Models.
Expected Outcome
At the end of the course, the student should be able to
1. Choose an appropriate time series model for a given set of data
2. Compute forecasts for a variety of linear and nonlinear methods and models.
References
1. Ruey S. Tsay, “Analysis of Financial Time Series”, John Wiley & Sons
2. A C Harvey, “Time Series Models”,Pearson; 2/e.
Cluster: 1
Branch: Mechanical Engineering
11
Stream: Financial Engineering
End-Semester
Examination
Contents
% of Marks in
Hours Allotted
Module
COURSE PLAN
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Financial data and their properties; Characteristics of Financial Time
Series; Linear Time Series Analysis-Simple AR models, MA models,
I
ARMA models; Unit-Root Non-stationarity; Seasonal Models;
Regression Models with Time Series Errors; Consistent Covariance
9
15
9
15
10
15
10
15
9
20
9
20
Matrix Estimation ;Long-Memory Models; Applications. Case studies of
Linear Time Series.
Asset Volatility and Volatility Models: Conditional Heteroscedastic
Models- ARCH model, GARCH model, GARCH-M Model, Exponential
GARCH Model, Threshold GARCH Model, CHARMA Model, Random
II
Coefficient Autoregressive Models, Stochastic Volatility Model, LongMemory Stochastic Volatility Model, Applications. Applications of
Volatility Models.
FIRST INTERNAL EXAM
III
Nonlinear Models- Bilinear Model, Threshold Autoregressive (TAR)
Model, Smooth Transition AR (STAR) Model, Markov Switching Model,
Nonparametric Methods, Functional Coefficient AR Model, Nonlinear
Additive AR Model, Nonlinear State-Space Model, Neural Networks;
Nonlinearity Tests, Modeling, Forecasting, Applications. HighFrequency Financial Data Analysis.
Continuous-Time Models- Wiener Process, Generalized Wiener Process,
Ito Process, Ito’s Lemma, Distributions of Stock Prices and Log Returns,
IV
Derivation of Black–Scholes Differential Equation, Black–Scholes Pricing
Formulas, Extension of Ito’s Lemma, Stochastic Integral, Jump Diffusion
Models. Value at Risk.
SECOND INTERNAL EXAM
Multivariate Linear Time Series; Weak Stationarity and CrossCorrelation Matrices, Vector Autoregressive Models, Vector Moving-
V
Average Models, Vector ARMA Models, Unit-Root Nonstationarity and
Cointegration, Cointegrated VAR Models, Threshold Cointegration and
Arbitrage, Pairs Trading.
Factor Models; Principal Component Analysis. Multivariate Volatility
Models- Multivariate GARCH Models, GARCH Models for Bivariate
VI
Returns, Higher Dimensional Volatility Models, Factor–Volatility
Models, Copula based models, Multivariate t Distribution. State-Space
Models and Kalman Filter. Markov Chain Monte Carlo Methods with
Applications
Cluster: 1
Branch: Mechanical Engineering
12
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
END SEMESTER EXAM
Course No.
01ME6605
Course Name
FINANCIAL REPORTING
AND ANALYSIS
L-T-P
Credits
Year of Introduction
3-0-0
3
2015
Course Objectives
1. The course aims to provide students with an introduction to financial statements,
their analyses and applications in financial performance measurement.
Syllabus
Financial Statement Analysis: Scope, Major financial statements and other information sources;
Financial Reporting Standards; Income Statements and Balance Sheet; Cash Flow Statements;
Financial Analysis Techniques; Inventories; Long Lived Assets; Income Taxes; Applications.
Expected Outcome
After successful completion of the course, the students will be able to:
1. Describe the roles of financial reporting and analysis
2. Describe the roles of key financial statements in evaluating a company’s performance and
financial position.
3. Describe the components of income statements, Balance Sheet and Cash Flow statements and
to use them to evaluate a company’s financial performance.
4. Describe the tools and techniques used in financial analysis, including their uses and
limitations.
5. Apply the tools and techniques to evaluate past and future financial performance, credit risk,
equity investments etc.
References
1. Benninga and Sarig, “Corporate Finance: A Valuation Approach”, McGraw-Hill Series
in Finance.
2. Pinto, Jerald E.,Elaine Henry, Thomas R. Robinson, and John D. Stowe, “Equity Asset
Valuation”, 2nd edition. Hoboken, NJ: John Wiley & Sons, 2010.
3. Van Greuning, Hennie, and Sonja Brajovic Bratanovic, “Analyzing and Managing
Cluster: 1
Branch: Mechanical Engineering
13
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Banking Risk: A Framework for Assessing Corporate Governance and Financial
Risk”, Washington, DC: World Bank, 2003.
4. International Auditing and Assurance Standards Board (IAASB),“Handbook of
International Quality Control, Auditing, Review, Other Assurance, and Related
Services Pronouncements”, Standard 200, available at www.ifac.org/IAASB.
5. Stowe, J.D., T.R. Robinson, J.E. Pinto, and D.W.McLeavey,“Analysis of Equity
Investments: Valuation”, Charlottesville, VA:. CFA Institute, 2002.
Cluster: 1
Branch: Mechanical Engineering
14
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
I
II
III
IV
V
Financial Statement Analysis: Scope, Major financial statements and
other information sources; Financial Statement Analysis framework.
Financial Reporting Mechanics: Accounts and financial statements;
Accounting Process; Accruals and valuation adjustments; Accounting
Systems.
Financial Reporting Standards: Standard Setting Bodies and Regulatory
Authorities; International Financial Reporting Standards Framework;
Effective Financial Reporting.
Income Statements- Components and format of the Income Statement;
Revenue Recognition; Expense Recognition; Non-recurring items and
non-operating items; Earnings per Share; Analysis of the Income
Statement.
Balance Sheet- Components and format of the Balance Sheet: Current
Assets and Current Liabilities, Non-current Assets, Non-current
Liabilities, Equity; Analysis of the Balance Sheet.
FIRST INTERNAL EXAM
Cash Flow Statements- Components and format of Cash Flow
Statements; Linkages of the Cash Flow Statement with the Income
Statement and Balance Sheet; Analysis of Cash Flow StatementsEvaluation of the Sources and uses of Cash, Common size analysis of
the statement of Cash Flows, Free Cash Flow to the Firm and Free Cash
Flow to Equity, Cash Flow Ratios.
Financial Analysis Techniques: Financial Analysis Process; Analytical
Tools and Techniques; Common Ratios used in Financial AnalysisActivity Ratios, Liquidity Ratios, Solvency Ratios, Profitability Ratios;
Integrated Financial Ratio Analysis; Equity Analysis; Credit Analysis;
Business and Geographic Segments-Segment reporting Requirements,
Segment Ratios; Model Building and Forecasting.
SECOND INTERNAL EXAM
Inventories: Cost of Inventories; Inventory Valuation Methods;
Measurement of Inventory Value; Evaluation of Inventory
Management-Inventory ratios.
Long Lived Assets: Acquisition of Long Lived Assets; Depreciation and
Amortisation of Long Lived Assets; The Revaluation model;
Impairment of Assets; De-recognition; Investment Property.
Cluster: 1
Branch: Mechanical Engineering
15
End-Semester
Examination
Contents
% of Marks in
Hours Allotted
Module
COURSE PLAN
4
15
3
4
15
3
7
15
7
15
4
20
3
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
VI
Income Taxes: Accounting Profit and Taxable Income; Recognition and
Measurement of Current and Deferred Tax. Non-current Liabilities.
Financial Reporting Quality; Accounting Shenanigans on the Cash Flow
Statement.
Applications of Financial Statement Analysis: Evaluating Past Financial
Performance, Projecting Future Financial Performance, Assessing Credit
Risk, Screening for Potential Equity Investments, Adjustments to
Reported Financials.
END SEMESTER EXAM
Cluster: 1
Branch: Mechanical Engineering
16
4
20
3
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Course No.
01ME6611
Course Name
CORPORATE FINANCE AND
PORTFOLIO MANAGEMENT
L-T-P
Credits
Year of Introduction
3-0-0
3
2015
Course Objectives
1. The course aims to provide students with an introduction to corporate finance and portfolio
management process.
Syllabus
Capital Budgeting; Cost of Capital; Measures of Leverage; DividendsandShareRepurchases;
Working-CapitalManagement; CorporateGovernance; Portfolio Management; Portfolio Risk
and Return; Portfolio Planning and Construction; Mergers and Acquisitions.
Expected Outcome
After successful completion of the course, the students will be able to:
1. Analyse capital budgeting problems
2. Estimate a company's cost of capital and evaluate working capital, and corporate governance
policies
3. Describe the mechanics of dividends and share repurchases, portfolio approach to
investment and portfolio management.
References
1. Calverley, John P., Alan M. Meder, Brian D. Singer, and Renato Staub, “Capital
Market Expectations” Managing Investment Portfolios; A Dynamic Process. 3rd ed.
Wliey, 2007.
2. Sharpe William F., Peng Chen, Jerald E. Pinto, and Dennis W. McLeavey "Asset
Allocation.", 2007.
3. Ibbotson Stocks, Bonds, Bills, and Inflation (SBBI) Classic Yearbook. 2009. Chicago,
IL: Morningstar.
4. Dimson, Elroy, Paul Marsh, and Mike Staunton, “Credit Suisse Global Investment
Returns Sourcebook”, Zurich, Switzerland: Credit Suisse Research Institute, 2009.
5. Taleb, Nassim N.,“The Black Swan: The Impact of the Highly Improbable”.New
York:Random House Inc., 2007.
Cluster: 1
Branch: Mechanical Engineering
17
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Hours Allotted
I
Capital Budgeting: Basic Principles of Capital Budgeting;
Investment DecisionCriteria-Net Present Value, Internal Rate of
Return, Payback Period, Discounted Payback Period, Average
Accounting Rate of Return, Profitability Index, NPV Profile, The
Multiple IRR Problem and the NoIRR; Cash Flow Projections;
Project Analysis and Evaluation.
6
Cost of Capital: Costs of the Different Sources of Capital- Cost of
Debt, Cost of Preferred Stock, Cost of Common Equity; Cost of
Capital Estimation-Estimating Beta and Determining a Project
Beta, Country Risk, Marginal Cost of Capital Schedule, Flotation
Costs.
II
15
4
Measures of Leverage: Leverage; Business Risk and Financial Risk.
Capital Structure-Capital Structure Decision, Practical Issues in
Capital Structure Policy.
Dividends and Share Repurchases: Dividends-Forms, Payment
Chronology, Dividend Policy and Company Value, Factors Affecting
Dividend Policy, Payout Policies, Analysis of Dividend Safety; Share
Repurchases- Methods, Effects of Repurchases, Valuation
Equivalence of Cash Dividends and Share Repurchases.
End-Semester
Examination
Contents
% of Marks in
Module
COURSE PLAN
15
4
FIRST INTERNAL EXAM
III
IV
Working Capital Management: Managing and Measuring
Liquidity, Managing the Cash Position, Investing Short-Term
Funds, Managing Accounts Receivable, Managing Inventory,
Managing Accounts Payable, Managing Short-Term Financing.
6
Corporate Governance: Importance, Definitions, Corporate
Governance
ConsiderationsThe
Board,
Management,
Shareowner Rights; Forms of Business and Conflicts of Interest,
Sources of Conflict, Corporate Governance Evaluation.
4
Portfolio Management: Overview; Portfolio Perspective on Investing;
Investment Clients; Steps in the Portfolio Management Process; Pooled
Investments. Portfolio Concepts- Mean-Variance Analysis, Multifactor
Models, Active Portfolio Management.
Cluster: 1
Branch: Mechanical Engineering
18
15
20
4
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Cluster: 1
Branch: Mechanical Engineering
19
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
V
VI
SECOND INTERNAL EXAM
Portfolio Risk and Return: Investment Characteristics of Assets; Risk
Aversion and Portfolio Selection; Portfolio Risk; Efficient Frontier and
Investor's Optimal Portfolio; Capital Market Theory; Pricing of Risk and
Computation of Expected Return; The Capital Asset Pricing
Model(CAPM).
8
Portfolio Planning and Construction: Portfolio Planning- The
Investment Policy Statement, Major Components of an IPS, Gathering
Client Information; Portfolio Construction- Capital Market
Expectations, The Strategic Asset Allocation, Steps Toward an Actual
Portfolio, Additional Portfolio Organizing Principles.
3
Mergers and Acquisitions: Definitions and Classifications, Motives for
Merger, Transaction Characteristics, Takeovers, Regulation, Merger
Analysis, Corporate Restructuring.
3
15
END SEMESTER EXAM
Cluster: 1
Branch: Mechanical Engineering
20
20
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Course No.
01ME6613
Course Name
NUMERICAL METHODS
FOR FINANCE
L-T-P
Credits
Year of Introduction
3-0-0
3
2015
Course Objectives
1. To introduce a general survey of significant numerical methods any practitioner should
know, and a detailed study of certain numerical methods specific to finance
Syllabus
Solving systems of linear equations; Solving non-linear equations; Curve fitting; Interpolation;
Numerical Integration; Finite Difference Methods for Partial Differential Equations; Convex
Optimization; Methods for constrained optimization; Applications.
Expected Outcome
1. After successful completion of the course, the students shall demonstrate skills in the
application of numerical methods to solve practical problems in mathematical finance.
References
Paolo Brandimarte, “Numerical Methods in Finance and Economics “, John Wiley & Sons.
Gerald & Wheatley,“Applied Numerical Analysis”, Addison-Wesley
V.Rajaraman , “Computer Oriented Numerical Methods”.
M.K.Jain , S.R.K.Iyengar and R.K.Jain, “Numerical Methods for Scientific and Engineering
Computations”.
5. S.S.Sastry,“Introductory methods of Numerical Analysis”.
6. S. Rajasekharan,“Numerical Methods in Science and Engg”.
1.
2.
3.
4.
I
Solving systems of linear equations; Function approximation and
interpolation- Ad hoc approximation, Elementary polynomial
interpolation, Interpolation by cubic splines, Theory of function
4
Cluster: 1
Branch: Mechanical Engineering
21
End-Semester
Examination
3
% of Marks in
Nature of numerical computation; Errors and approximations, floating
point arithmetic, sources of errors, control of errors, propagation of
errors, Condition and stability, Rate of convergence.
Module
Contents
Hours Allotted
COURSE PLAN
15
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
approximation by least squares.
II
Solving non-linear equations- Bisection method, Newton’s method,
Optimization- based solution of non-linear equations, Solving a
functional equation by a collocation method, Homotopy continuation
methods
7
15
FIRST INTERNAL EXAM
III
IV
V
VI
Curve fitting: Method of least squares, non-linear relationships,
Correlation and Regression – Linear correlation, Measures of
correlation, Standard error of estimate, Coefficient of correlation.
3
Interpolation – Newton’s divided difference, Lagrange, Aitken, Hermite
and spline techniques – Inverse Interpolation – Numerical
differentiation.
3
Numerical Integration: Deterministic quadratureClassical
interpolatory formulas, Gaussian quadrature, Extensions and product
rules; Monte Carlo integration; Generating pseudorandom variates;
Variance reduction techniques; Quasi-Monte Carlo simulation.
4
Finite Difference Methods for Partial Differential Equations: Numerical
solution by finite difference methods, Explicit and implicit methods for
the heat equation, Solving the bi-dimensional heat equation,
Convergence, consistency, and stability.
4
SECOND INTERNAL EXAM
Convex Optimization: Elements of convex analysis- Convexity in
optimization, Convex polyhedra and polytopes, Classification of
optimization problems; Numerical methods for unconstrained
optimization- Steepest descent method, The subgradient method,
Newton and the trust region methods, No-derivatives algorithms:
quasi-Newton method and simplex search.
5
Methods for constrained optimization: Penalty function approach,
Duality theory, Kuhn-Tucker conditions, Kelley's cutting plane
algorithm, Active set method.
3
Option Pricing by Binomial and Trinomial Lattices; Option Pricing by
Monte Carlo Methods; Option Pricing by Finite Difference MethodsApplying finite difference methods to the Black- Scholes equation,
Pricing a vanilla European option by an explicit method, Pricing a
vanilla European option by a fully implicit method Pricing a barrier
option by the Crank-Nicolson method
END SEMESTER EXAM
Cluster: 1
Branch: Mechanical Engineering
22
15
15
20
6
Stream: Financial Engineering
20
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Course No.
01ME6615
Course Name
OBJECT ORIENTED
PROGRAMMING FOR
FINANCIAL ENGINEERS
L-T-P
Credits
Year of Introduction
3-0-0
3
2015
Course Objectives
1. To give a rigorous introduction to computer programming and software engineering with
special emphasis on applications to financial engineering
Syllabus
Introduction to C++ and Quantitative Finance;Generic Data Structures and Standard Template
Library; Interfaces to STL for QF Applications; Design Patterns; Binomial Method; Implementation
of One-Factor Black Scholes; Two-Factor Option Pricing; C++ Classes for Numerical Analysis
Applications in Finance; Monte Carlo Method Theory and C++ Frameworks.
Expected Outcome
1. After successful completion of the course, the students shall have the knowledge of
advanced complex techniques in C++ and real-life applications in financial engineering.
1.
2.
3.
4.
5.
6.
7.
8.
9.
References
Daniel J. Duffy, “Introduction to C++ for Financial Engineers: An object-oriented
approach”, John Wiley & Sons Ltd.
Ashok M. Kamthane, “Object oriented Programming with ANSI & Turbo C++”,
Pearson
Education.
Nagler, “Learning C++, A Hands on Approach”, Jaico publications.
Stanley B. Lippman and Josee Lajoie, “C++ Primer”, Pearson Education.
Balaguruswamy, “Object Oriented Programming with C++”, TataMcgraw Hill.
Nabajyothi barkakati, “Object Oriented Programming in C++” , Prentice Hall.
Balaguruswamy, “Numerical Methods”, TataMcgraw Hill.
C.F. Gerald and P.O.Wheatley, “Applied Numerical Analysis” , Pearson Education.
Cluster: 1
Branch: Mechanical Engineering
23
Stream: Financial Engineering
End-Semester
Examination
% of Marks in
Contents
Hours Allotted
Module
COURSE PLAN
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
I
Introduction to C++ and Quantitative Finance; C++ fundamentals,
Classes, Operator overloading, Memory Management
6
15
II
Functions, Namespaces, Inheritance, Run-Time Behaviour, C++
Templates; Generic Data Structures and Standard Template Library
8
15
7
15
7
15
V
C++ Classes for Numerical Analysis Applications in FinanceSolving tridiagonal systems, The trinomial method for assets,
Lattice data structures, Trinomial tree for the short rate , The
multidimensional binomial method, Generic lattice structures,
Approximating exponential functions.
7
20
VI
The Monte Carlo Method Theory and C++ Frameworks: The
Monte Carlo method in quantitative finance, Software architecture
for the Monte Carlo method, Examples and test cases- Plain
options, Barrier options, Asian options.
7
20
FIRST INTERNAL EXAM
III
Interfaces to STL for QF Applications, Data Structures for Financial
Engineering Applications- Property sets and data modelling for
quantitative finance, Lattice structures.
IV
Introduction to Design Patterns.Programming the Binomial
Method; Implementation of One-Factor Black Scholes; Two-Factor
Option Pricing: Basket and Other Multi-Asset Options- Modelling
basket option PDE in UML and C++, The finite difference method
for two-factor problems, Discrete boundary and initial conditions,
Assembling the system of equations.
SECOND INTERNAL EXAM
END SEMESTER EXAM
Cluster: 1
Branch: Mechanical Engineering
24
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Course No.
Course Name
L-T-P
Credits
Year of Introduction
01ME6999
RESEARCH METHODOLOGY
0-2-0
2
2015
Course Objectives
1. To prepare the student to do the M. Tech project work with a research bias.
2.
3.
4.
5.
To formulate a viable research question.
To develop skill in the critical analysis of research articles and reports.
To analyze the benefits and drawbacks of different methodologies.
To understand how to write a technical paper based on research findings.
Syllabus
Introduction to Research Methodology-Types of research- Ethical issues- Copy right-royaltyIntellectual property rights and patent law-Copyleft- OpenacessAnalysis of sample research papers to understand various aspects of research methodology:
Defining and formulating the research problem-Literature review-Development of working
hypothesis-Research design and methods- Data Collection and analysis- Technical writing- Project
work on a simple research problem
Approach
Course focuses on students' application of the course content to their unique research interests. The
various topics will be addressed through hands on sessions.
Expected Outcome
Upon successful completion of this course, students will be able to
1. Understand research concepts in terms of identifying the research problem
2. Propose possible solutions based on research
3. Write a technical paper based on the findings.
4. Get a good exposure to a domain of interest.
5. Get a good domain and experience to pursue future research activities.
References
1.
2.
3.
4.
5.
C. R. Kothari, Research Methodology, New Age International, 2004
Panneerselvam, Research Methodology, Prentice Hall of India, New Delhi, 2012.
J. W. Bames, Statistical Analysis for Engineers and Scientists, Tata McGraw-Hill, New York.
Donald Cooper, Business Research Methods, Tata McGraw-Hill, New Delhi.
Leedy P. D., Practical Research: Planning and Design, McMillan Publishing Co.
Cluster: 1
Branch: Mechanical Engineering
25
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
6. Day R. A., How to Write and Publish a Scientific Paper, Cambridge University Press, 1989.
7. Manna, Chakraborti, Values and Ethics in Business Profession, Prentice Hall of India, New
Delhi, 2012.
8. Sople, Managing Intellectual Property: The Strategic Imperative, Prentice Hall ofIndia, New
Delhi, 2012.
END SEMESTER EXAM
Introduction to Research Methodology: Motivation towards research Types of research: Find examples from literature.
Professional ethics in research - Ethical issues-ethical committees. Copy
I
right - royalty - Intellectual property rights and patent law - CopyleftOpenacess -Reproduction of published material - Plagiarism - Citation
5
and acknowledgement.
Impact factor.
Identifying major conferences and important
journals in the concerned area. Collection of at least 4 papers in the
area.
II
Defining and formulating the research problem - Literature SurveyAnalyze the chosen papers and understand how the authors have
undertaken literature review, identified the research gaps, arrived at
4
their objectives, formulated their problem and developed a hypothesis.
FIRST ASSESSMENT
Cluster: 1
Branch: Mechanical Engineering
26
Stream: Financial Engineering
End-Semester
Examination
Contents
% of Marks in
Hours Allotted
Module
COURSE PLAN
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
III
Research design and methods: Analyze the chosen papers to
understand formulation of research methods and analytical and
4
experimental methods used. Study of how different it is from
previous works.
IV
Data Collection and analysis. Analyze the chosen papers and study the
methods of data collection used. - Data Processing and Analysis
No end
semester
written
examinatio
n
5
strategies used – Study the tools used for analyzing the data.
SECOND ASSESSMENT
V
Technical writing - Structure and components, contents of a typical
technical paper, difference between abstract and conclusion, layout,
illustrations and tables, bibliography, referencing and footnotes- use of
5
tools like Latex.
VI
Identification of a simple research problem – Literature surveyResearch design- Methodology –paper writing based on a hypothetical
5
result.
END SEMESTER ASSESSMENT
Cluster: 1
Branch: Mechanical Engineering
27
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Course No.
Course Name
L-T-P
Credits
Year of Introduction
01ME6691
SEMINAR I
0-0-2
2
2015
Course Objectives
To make students
1. Identify the current topics in the specific stream.
2. Collect the recent publications related to the identified topics.
3. Do a detailed study of a selected topic based on current journals, published papers
and books.
4. Present a seminar on the selected topic on which a detailed study has been done.
5. Improve the writing and presentation skills.
Approach
Students shall make a presentation for 20-25 minutes based on the detailed study of
the topic and submit a report based on the study.
Expected Outcome
Upon successful completion of the seminar, the student should be able to
1. Get good exposure in the current topics in the specific stream.
2. Improve the writing and presentation skills.
3.
Explore domains of interest so as to pursue the course project
Cluster: 1
Branch: Mechanical Engineering
28
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Course No.
01ME6693
Course Name
DATA ANALYSIS
LABORATORY
L-T-P
Credits
Year of Introduction
0-0-2
1
2015
Course Objectives
1. Should acquire knowledge on working of data analysis software packages.
Syllabus
Experiments on conducting data analysis tasks using software packages.
Expected Outcome
1. Have working knowledge of data analysis software packages.
List of Experiments
1.
Data Analysis using SPSS / Excel /R /Python /SAS / Systat/EViews etc(free and open
source, trial or free academic version of the software package may be used).
Exercises shall be given on
Data input
Descriptive statistics and Tabulation
Fitting Probability Distribution
Data Munging
Hypothesis Testing
Graphical Analysis
t tests
ANOVA
Regression Analysis
Time Series and Autocorrelation
Clustering etc.
Cluster: 1
Branch: Mechanical Engineering
29
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
SEMESTER – II
Syllabus and Course Plan
Cluster: 1
Branch: Mechanical Engineering
30
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Course No.
01ME6602
Course Name
OPTIMIZATION IN
FINANCE
L-T-P
Credits
Year of Introduction
3-1-0
4
2015
Course Objectives
1. To introduce mathematical modelling and optimization techniques.
2. To apply these techniques for financial optimization.
3. To experiment with real-life problems and promote decision making skills.
Syllabus
Linear Programming; Sensitivity Analysis; Non-linear programming;Quadratic programming;
Conic Optimization Tools; Integer Programming; Dynamic Programming; Stochastic Programming;
Robust Optimization and Case studies in Financial Optimization.
Expected Outcome
1. The students will be able to model real life problems.
2. The students will have the knowledge to select and applysuitable optimization techniques in
financial optimization problems.
References
1. Gerard Cornuejols et al., Optimization Methods in Finance, Cambridge University
press.
2. Stavros Zenios, Andrea Consiglio and Søren S. Nielsen, Practical Financial
Optimization, John Wiley and Sons, Ltd.
Hours Allotted
I
Overview of optimization concepts; Linear programming: theory and
algorithms, Sensitivity Analysis;
3
Cluster: 1
Branch: Mechanical Engineering
31
Stream: Financial Engineering
End-Semester
Examination
Contents
% of Marks in
Module
COURSE PLAN
15
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
II
LP models: asset/liability cash-flow matching- Short-term financing;
asset pricing and arbitrage- Derivative securities and the fundamental
theorem of asset pricing, Arbitrage detection using linear programming
5
Nonlinear programming: Univariate optimization, Unconstrained
optimization, Constrained optimization, Non-smooth optimization:
sub-gradient methods
6
NLP models: volatility estimation-Volatility estimation with GARCH
models, Estimating a volatility surface.
2
15
FIRST INTERNAL EXAM
III
Quadratic programming: theory and algorithms; QP models: portfolio
optimization- Mean-variance optimization, Maximizing the Sharpe
ratio, Returns-based style analysis, Recovering risk-neutral probabilities
from options prices.
IV
Conic optimization tools- Second-order cone programming, Semidefinite programming, Algorithms; Conic optimization models in
finance- Tracking error and volatility constraints, Approximating
covariance matrices, Recovering risk-neutral probabilities fromoptions
prices, Arbitrage bounds for forward start options
9
15
9
15
SECOND INTERNAL EXAM
V
VI
Integer programming: theory and algorithms; Integer programming
models: Constructing index fund- Combinatorial auctions, the lockbox
problem, constructing an index fund, Portfolio optimization with
minimum transaction levels
Dynamic programming; DP models: option pricing- A model for
American options, Binomial lattice; structuring asset-backed securities
Stochastic programming: theory and algorithms- Two-stage problems
with recourse, Multi-stage problems, Decomposition, Scenario
generation; Stochastic programming models: Value-at-Risk and
Conditional Value-at-Risk- Risk measures, Minimizing CVaR;
asset/liability management.
Robust optimization: theory and tools; Robust optimization models in
finance, Robust multi-period portfolio selection, Robust profit
opportunities in risky portfolios, Robust portfolio selection, Relative
robustness in portfolio selection, Moment bounds for option prices.
7
20
4
5
20
6
Case Studies in Financial Optimization- Applications in International
Asset Allocation, Corporate Bond Portfolio Management, Insurance
Policies with Guarantees, Personal Financial Planning.
END SEMESTER EXAM
Cluster: 1
Branch: Mechanical Engineering
32
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Course No.
01ME6604
Course Name
EQUITY AND FIXED
INCOME
L-T-P
Credits
Year of Introduction
3-0-0
3
2015
Course Objectives
1. To introduce equity investments, security markets and indices, and equity valuation models.
2. To introduce the basic features and characteristics of fixed income securities and associated
risks.
3. Describe the primary issuers, sectors and types of bonds
4. To introduce yields and spreads and the effect of monetary policy on financial markets.
Syllabus
Market Organization and Structure; Security Market Indices; Market Efficiency; Equity Securities;
Industry and Company Analysis; Equity Valuation; Fixed Income; Risks Associated with Investing
in Bonds; Bond Sectors and Instruments; Yield Spreads; Valuation of Debt Securities; Yield
Measures; Spot Rates; Forward Rates; Measurement of Interest Rate Risk; Credit Analysis.
Expected Outcome
After the completion of the course, the students shall be able
1. To describe the characteristics of equity investments, security markets and indices.
2. To analyze industries, companies and equity securities and to describe and demonstrate the
use of equity valuation models.
3. To describe the basic features of bonds, the various coupon rate structures, and the structure
of various floating rate securities and the risks associated with investing in bonds.
4. To describe features, credit risk characteristics and distribution methods for government
securities, mortgage-backed securities etc.
References
1. Sharpe, W, G. Alexander, and J. Bailey, “Investments”. New Jersey: Prentice Hall,
lnc, 1999.
2. Dreman, D.,”Psychology of the Stock Market”. New York: AMACOM, 1977.
3. O'Shaughnessy, J., “What Works on Wall Street”. New York: McGraw-Hill, 2005.
4. Hill, Charles, and Gareth Jones, "External Analysis: The Identification of
Opportunities and Threats:' Strategic Management: An Integrated
Approach.”Boston, MA: Houghton Mifflin Co, 2008.
5. Porter, Michael E., “The Five Competitive Forces that Shape Strategy”. Harvard
Cluster: 1
Branch: Mechanical Engineering
33
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Business Review, vol. 86, no. 1:78-93, 2008.
6. “Equity and Fixed Income”. CFA Institute, 2012.
I
II
15
Security Market Indices: Index Definition and Calculations of Value and
Returns, Index Construction and Management, Uses of Market Indices,
Equity Indices, Fixed-Income Indices, Indices for Alternative
Investments.
3
Market Efficiency- Concept; Forms; Market Pricing Anomalies;
Behavioral Finance.
3
Equity Securities: Types and Characteristics of Equity Securities, Private
versus Public Equity Securities, Risk and Return Characteristics of
Equity Securities.
End-Semester
Examination
4
% of Marks in
Market Organization and Structure: The Functions of the Financial
System, Assets and Contracts, Financial intermediaries, Positions,
Orders, Primary Security Markets, Secondary Security Market and
Contract Market Structures, Market Regulation.
Module
Contents
Hours Allotted
COURSE PLAN
15
4
FIRST INTERNAL EXAM
Introduction to Industry and Company Analysis-Uses, Industry
Classification Systems, Describing and Analyzing an industry,
Company Analysis.
III
Equity Valuation- Valuation Concepts, Return Concepts, Equity
Valuation Models: Discounted Dividend Valuation- Dividend Discount
Model, Gordon Growth Model, Multistage Dividend Discount Models,
Financial Determinants of Growth Rates; Free Cash Flow Valuation;
Market-Based Valuation, Residual Income Valuation, Private Company
Valuation.
Fixed Income: Features of Debt Securities- Coupon Rate; Provisions for
Paying off Bonds; Put Provision; Currency Denomination; Embedded
Options.
IV
Risks Associated with Investing in Bonds
15
4
2
2
Bond Sectors and Instruments-Sectors; Sovereign Bonds; SemiGovernment/ Agency Bonds; Corporate Debt Securities; Asset Backed
Securities; Primary Market and Secondary Market for Bonds
Cluster: 1
3
Branch: Mechanical Engineering
34
3
Stream: Financial Engineering
15
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
V
VI
SECOND INTERNAL EXAM
Yield Spreads; Valuation of Debt Securities- General Principles of
Valuation, Traditional Approach to Valuation, Valuation Models.
5
Yield Measures, Spot Rates, and Forward Rates; Measurement of
Interest Rate Risk.
4
Fundamentals of Credit Analysis- Credit risk, Capital Structure,
Seniority Ranking, and Recovery Rates, Ratings Agencies, Credit
Ratings, and their Role in the Debt Markets.
END SEMESTER EXAM
Cluster: 1
Branch: Mechanical Engineering
35
15
25
5
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Course No.
01ME6406
Course Name
SYSTEM ANALYSIS AND
DESIGN
L-T-P
Credits
Year of Introduction
3-0-0
3
2015
Course Objectives
1. To apply system concepts to solve problems in industrial and business organizations.
2. To model and simulate discrete event systems.
3. To study the tools for modeling and simulating dynamic systems.
Syllabus
Introduction to System Simulation – Random Numbers – Random number generators – Generation
of Random deviates – Input modeling – Verification and validation of simulation models - Analysis
of Simulation outputs - Structure and Behavior of Dynamic systems – Tools for systems thinking –
Elements of system dynamics modeling – Steps in SD modeling – Overview of computer simulation
languages and packages.
Expected Outcome
1. The student will have an understanding of real life systems with interacting components,
elements and sub-systems, modeling and analysis of these interacting components and
elements in a system and the system as a whole.
2. The student will be able to conduct experiments on the system models and to predict the
system behavior at different environments and input states and parameter settings and to
find out the best suited system parameter settings to meet the predefined objectives.
References
1. Geoffrey Gordon, “System Simulation”, PHI.
2. Narsingh Deo, “System Simulation with Digital Computer”, PHI.
3. J. Banks, “Discrete Event System Simulation”, Pearson Education.
4. Fishman – John, “Concepts and Methods in Discrete Event Digital; Simulation”,
Willey & Sons.
5. Sterman, “Business Dynamics”, McGraw Hill.
6. Mohapatra, “System Dynamics”, PHI.
7. Ogata, “System Dynamics”, Pearson Education.
Cluster: 1
Branch: Mechanical Engineering
36
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Cluster: 1
Branch: Mechanical Engineering
37
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
I
10
Random Numbers: Properties, Generation of Pseudo-Random numbers
– Random number generators, Tests for random numbers – Frequency,
Run, Gap, Autocorrelation and Poker tests.
II
9
20
7
15
7
15
7
20
7
20
End-Semester
Examination
5
% of Marks in
Introduction to System Simulation: System approach to problem
solving, Steps in simulation study, Comparison of simulation and
numerical methods, Monte Carlo simulation.
Module
Contents
Hours Allotted
COURSE PLAN
FIRST INTERNAL EXAM
III
IV
Generation of Random Deviates: Inverse Transformation method Exponential, Uniform, Weibull, Triangular, and discrete distributions,
Direct transformation method for the Normal and Lognormal
distributions, Acceptance-rejection technique - Poisson and Gamma
distributions.
Input modeling - data collection, identifying the distribution with the
collected data, goodness of fit tests, Verification and Validation of
simulation models, Analysis of simulation Outputs.
Discrete event simulation techniques - Next-Event approach and Fixed
Time Increment methods.
SECOND INTERNAL EXAM
V
VI
Structure and Behavior of Dynamic systems: Fundamental modes of
dynamic behavior – Exponential growth, goal seeking, oscillation and
process point, Interactions of fundamental modes.
Tools for Systems thinking - Causal loop diagramming, Behavior of
low order systems - Analytical approach.
Elements of System Dynamics Modeling: Physical flows, Information
flows, Level & Rate variables, Flow diagrams, Delays, Information
smoothing, Table functions and Table function multipliers, First order
positive and negative feedback systems, Second order systems.
Steps
in
system
dynamics
modeling:
Problem
identification/conceptualization, fixing model aggregates and boundary,
principles of simulation modeling, Developing model equations.
Overview of computer simulation languages and packages.
END SEMESTER EXAM
Cluster: 1
Branch: Mechanical Engineering
38
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Course No.
01ME6612
Course Name
DERIVATIVES AND
ALTERNATIVE
INVESTMENTS
L-T-P
Credits
Year of Introduction
3-0-0
3
2015
Course Objectives
1. To introduce the students to derivative markets and instruments, alternative investments
and various methods of analysis.
Syllabus
Derivative Markets and Instruments; Forward Markets and Contracts; Futures Markets and
Contracts; Option Markets and Contracts; Swap Markets and Contracts; Interest Rate Derivative
Instruments; Risk Management Applications of Option Strategies; Alternative Investments:
Categories; Investment in Hedge Funds, Commodities, Real Estate; Private Equity.
Expected Outcome
1. The student will be able to demonstrate a working knowledge of the analysis of derivative
investments, including forwards, futures, options, and swaps as well as the analysis of
alternative investments, including hedge funds, private equity, real estate, and commodities.
References
1. “Derivatives and Alternative Investments”, CFA Institute, 2012.
2. Lionel Martellini, Philippe Priaulet, “Fixed-Income Securities: Valuation, Risk
Management and Portfolio Strategies”, Wiley.
I
II
15
Forward Markets and Contracts: Global Forward Markets, Types of
Forward Contracts, Pricing and Valuation of Forward Contracts, Credit
Risk and Forward Contracts
4
Futures Markets and Contracts: Futures Trading, the Clearinghouse,
Margins, and Price Limits, Futures Exchanges, Types of Futures
Contracts, Pricing and Valuation of Futures Contracts.
5
Cluster: 1
Branch: Mechanical Engineering
39
End-Semester
Examination
3
% of Marks in
Derivative Markets and Instruments: Types of Derivatives, Elementary
Principles of Derivative Pricing.
Module
Contents
Hours Allotted
COURSE PLAN
Stream: Financial Engineering
15
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
FIRST INTERNAL EXAM
III
Option Markets and Contracts: Basic Definitions and Illustrations of
Options Contracts, the Structure of Global Options Markets, Types of
Options, Principles of Option Pricing, Discrete-Time Option Pricing:
The Binomial Model, Continuous-Time Option Pricing: The BlackScholes-Merton Model, Pricing Options on Forward and Futures
Contracts and an Application to Interest Rate Option Pricing.
Swap Markets and Contracts: Global Swap Markets, Types of Swaps,
Pricing and Valuation of Swaps, Variations of Swaps, Swaptions, Credit
Risk and Swaps.
IV
Interest Rate Derivative Instruments: Interest Rate Futures, Interest Rate
Options, Interest Rate Swaps, Interest Rate Caps and Floors. Overview
of Credit Derivatives.
Risk Management Applications of Option Strategies: Option Strategies
for Equity Portfolios.
V
SECOND INTERNAL EXAM
Alternative Investments: Categories; Investment in Hedge Funds: Fee
Structures, Hedge Fund Strategies, Hedge Fund Databases and
Performance Biases, Factor Models for Hedge Fund Returns, NonNormality of Hedge Fund Returns, Liquidity, Complexity, and
Valuation Risks, Hedge Fund Replication, Hedge Fund Portfolio
Analysis, Performance Persistence of Hedge Funds
Commodities: Basics, Controversies, Commodities in a Portfolio,
Implementation of Commodity Strategies.
Real Estate: Private Real Estate Investments; Valuation approaches for
Real Estate Investments
Publicly Traded Real Estate Securities-Types; Valuation methods
VI
Private Equity: Introduction to Valuation Techniques in Private Equity
Transactions, Private Equity Fund Structures and Valuation
5
4
4
Branch: Mechanical Engineering
40
20
3
2
15
2
3
3
4
END SEMESTER EXAM
Cluster: 1
15
Stream: Financial Engineering
20
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Course No.
01ME6414
Course Name
DATA ANALYTICS USING R
AND PYTHON
L-T-P
Credits
Year of Introduction
3-0-0
3
2015
Course Objectives
1. Learn about what it’s like to be a Data Scientist.
2. Learn R and Python for Data Analytics.
Syllabus
Introduction to R; R and Rstudio; Basics of R; Advanced Data Structures; Reading Data into R;
Statistical Graphics; R programming; Data Munging; String Manipulation; Basic Statistics; Linear
Models; Predictive Modeling; Time Series Analysis; Clustering; Association Rules; Text Mining;
Sentiment Analysis; Social Network Analysis; Reports and Slideshows; R Package Building.
Introduction to Python; Python Programming; NumPy; Pandas; Data Loading, Storage , File
formats, Data Wrangling; Plotting and Visualization; Data Aggregation and Group Operations;
Time Series Analysis; Financial and Economic Data Applications
Expected Outcome
After Completion of course, the students will be able to use R and Python to:
1.
2.
3.
4.
Manipulate and extract information from data
Make informative plots
Construct and apply statistical learning methods for predictive modeling,
Properly select, tune, and assess models
5. Reproduce and present results from data analysis
References
1. Jarad Lander, “R for Everyone: Advanced Analytics and Graphics”, Addison Wesley.
2. Mark Gardener, “R The Statistical Programming” , Wiley.
3. James, Witten, Hastie and Tibshirani,“An Introduction to Statistical Learning: with
Applications in R”, free electronic version of this book available at http://wwwbcf.usc.edu/~gareth/ISL/.
4. Johannes Ledolter, “Data mining and business analytics with R”, John Wiley & Sons.
5. Torgo, Luís, “Data mining with R : learning with case studies”, CRC Press
6. Dirk Eddelbuettel, “Seamless R and C++ Integration with Rcpp”, Springer
7. http://www.rdatamining.com/
8. Wes McKinney, “Python for Data Analysis”, O’Reilly.
9. Peter Wang and Aron Ahmadia, “Fundamentals of Data Analytics in Python”, Addison
Wesley Live Lessons
Cluster: 1
Branch: Mechanical Engineering
41
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
I
Introduction to R; Installation of R and R Studio; Installing and loading
R packages
Basic building blocks in R; Advanced Data Structures in R; Reading data
into R; Statistical Graphs in R
R Programming
II
End-Semester
Examination
Contents
% of Marks in
Module
Hours Allotted
COURSE PLAN
1
3
10
3
Data Munching-Group manipulation, Reshaping; String Manipulation
3
Basic Statistics; Linear Models
4
20
FIRST INTERNAL EXAM
III
IV
Predictive Modeling: Generalized Linear Models; Model Diagnostics;
Regularization and Shrinkage
Nonlinear Models; Time Series and Autocorrelation; Multivariate data
exploration and discrimination.
Clustering;Association Rules; Text Mining; Sentiment Analysis; Social
Network Analysis; Reports and Slideshows
R Package Building, Introduction to Rcpp, Data structures, Using Rcpp
in package, Modules, Operators, Functions, Applications.
3
20
3
4
10
4
SECOND INTERNAL EXAM
V
VI
Introduction to Python: Python Libraries, Installation and Setup; Python
Programming: Data Types and Variables, Python input and output, If
statements, while loops, for loops, Iterators, Lists, Functions , Modules,
Object Oriented Programming, Inheritance, Exception Handling, Using
Data Structures.
Basic Analytics with Python; Numerical Analysis with NumPy
Advanced Analytics with Sci-Py and sci-kit learn
Branch: Mechanical Engineering
42
20
2
2
Tabular Data Analysis with Pandas; Python Visualization Tools;
Financial and Economic Data Applications
END SEMESTER EXAM
Cluster: 1
7
3
Stream: Financial Engineering
20
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Course No.
Course Name
L-T-P
Credits
Year of Introduction
01ME6616
FINANCIAL MARKETS
3-0-0
3
2015
Course Objectives
1. To learn the business and the economics of money and capital markets
2. To become familiar with the various types of financing available to a firm
3. To analyze the structural interrelationships among the important participants in the
financial markets
Syllabus
Money and Capital Markets; Financial Institutions; Financial Markets; Risk Management in
Financial Institutions; Indian Financial Markets.
Expected Outcome
At the end of this course students should be able to:
1. Explain financial markets and institutions and how firms obtain funds in the financial
markets.
2. Explain how the financial services component industries interact.
References
1. Frederic S. Mishkin and Stanley G. Eakins,“Financial Markets and Institutions”,
Pearson, 8/e.
2. Rakesh Sahani, “Financial Markets in India”, Anamika Publishers and
Distributors,2008.
End-Semester
Examination
Contents
% of Marks in
Hours Allotted
Module
COURSE PLAN
Money and Capital Markets; Overview of the Financial System;
I
Fundamentals of Financial markets-Interest rates and their role in
Valuation; Interest rate change; Risk and Term Structure; Efficiency of
7
15
7
15
Financial Markets.
II
Financial Institutions: Banks and Monetary Policy; Banking and the
Management of Financial Institutions ; Financial Regulation ; Banking
Industry: Structure and Competition ; The Mutual Fund Industry
;Insurance Companies and Pension Funds ; Investment Banks, Security
Cluster: 1
Branch: Mechanical Engineering
43
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Brokers and Dealers, and Venture Capital Firms
FIRST INTERNAL EXAM
III
Financial Markets: The Money Markets ; The Bond Market ; The Stock
Market ; The Mortgage Markets ; The Foreign Exchange Market ; The
International Financial System
7
15
7
15
5
20
9
20
Risk Management in Financial Institutions; Hedging with Financial
Derivatives.
IV
V
Nature and Role of Financial Markets in India; Behavioural Finance and
the Role of the Psychology; Primary Market for Industrial Securities in
India
SECOND INTERNAL EXAM
Role of Investment and Merchant Banking as Intermediaries in the field
of Financial Markets; Stock Market Volatility and Securities Trading In
India
Indian Stock Market Indices; Futures, Options and other financial
derivatives; Collective Investment Vehicles; Money Market in India;
Market for Government Securities and Foreign Investment in India.
VI
Liquidity, Turnover and Impact Costs on Indian Exchanges; Listing and
Delisting of Securities; Major Financial Services Operating in the Indian
Financial Markets.
Brief Introduction into Corporate Governance; Non-Performing Assets
(NPA) in Banking Sector; Insider Trading; Buy Back of Shares by the
Company; Benchmark Prime Lending Rate.
END SEMESTER EXAM
Cluster: 1
Branch: Mechanical Engineering
44
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Course No.
01ME6618
Course Name
QUANTITATIVE TRADING
STRATEGIES
L-T-P
Credits
Year of Introduction
3-0-0
3
2015
Course Objectives
1. To introduce the students to financial trading strategies which will focus on stock and equity
markets as well as the use of statistical arbitrage methods
2. To develop, automate and evaluate models that reflect market and behavioral patterns.
Syllabus
Foundations of Securities Trading and Market Microstructure; Information- and Inventory-Based
Microstructure Models ; Trading Costs and Optimal Trading Strategies; High Frequency Trading
Strategies
Expected Outcome
1. The students completing this course will develop the knowledge to apply theory for
practical application to realistic trading and strategy problems.
References
1. Lars N. Kestner,“Quantitative Trading Strategies: Harnessing the Power of
Quantitative Techniques to Create a Winning Trading Program”, McGraw Hill
Professional publication.
2. Morton Glant, “Optimal Trading Strategies: Quantitative Approaches for
Managing Market Impact and Trading Risk”.
3. Joel Hasbrouck, “Empirical Market Microstructure – The Institutions, Economics,
and Econometrics of Securities Trading”, Oxford University Press 2007
4. Maureen O’Hara, “Market Microstructure Theory”, Blackwell Publishing 1995
5. Irene Aldridge, “High Frequency Trading – A Practical Guide to Algorithmic
Strategies and Trading Systems”, Wiley, 2010.
Hours Allotted
I
Foundations of Securities Trading and Market Microstructure :
Nature of Markets and Prices; Trading Mechanisms, Markets and
Market Making; Univariate Time Series Analysis
7
Cluster: 1
Branch: Mechanical Engineering
45
Stream: Financial Engineering
End-Semester
Examination
Contents
% of Marks in
Module
COURSE PLAN
15
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
II
III
IV
V
VI
Information- and Inventory-Based Microstructure Models
Information-Based Model;Sequential Trade Model;Strategic Trade
Models
FIRST INTERNAL EXAM
Generalized Roll Model; Multivariate Linear Microstructure ModelsModeling Vector Time Series, Structural Model of Prices and Trades,
Forecasts and Impulse Response Functions, Random Walk
Decomposition in Multivariate Models , Other Structural Models
Multiple Securities and Multiple Prices; Inventory Models- Order
Arrival and Market Making , Market Maker’s Ruin Problem , Risk
Aversion and Dealer’s Problem , Empirical Analysis of Dealer ,
Inventories, Dynamics of Prices, Trades, and Inventories; Market Depth
SECOND INTERNAL EXAM
Trading Costs and Optimal Trading Strategies
Transaction Costs- Component Transaction Costs , Implementation
Shortfall , Market Impact, Timing Risk and Opportunity Costs,
Optimal Trading Strategies ; Trading Benchmarks- Benchmark Prices,
BAM and VWAP , VWAP Trading Strategies , Models of Order Slicing
and Timing
High Frequency Trading Strategies
Evolution of High Frequency Trading-Comparison with Traditional
Approaches to Trading , Evaluating Performance of High Frequency
Strategies , Market Efficiency and Trading Opportunities at Different
Frequencies;High Frequency Data;High Frequency Strategies-Trading
on Microstructure: Inventory Based and Information Based , Event
Arbitrage Strategies , Statistical Arbitrage Strategies , Managing
Portfolios of High Frequency Strategies
7
15
6
15
8
15
7
20
7
20
END SEMESTER EXAM
Cluster: 1
Branch: Mechanical Engineering
46
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Course No.
01ME6422
Course Name
ENTERPRISE RESOURCE
PLANNING
L-T-P
Credits
Year of Introduction
3-0-0
3
2015
Course Objectives
1. The student should be able to acquire knowledge in ERP architecture and different
packages.
2. Should have exposure to latest trends in ERP.
3. Ability to identify important issues pertaining to implementation of ERP software in an
industrial scenario
Syllabus
Introduction to ERP and Enterprise Applications; Risks and Benefits of ERP; ERP and Related
Technologies; ERP Manufacturing Perspective; Business Process Reengineering (BPR); ERP
Implementation—Life Cycle, Methodologies, Issues; Business Modules in an ERP Package; ERP
Market, ERP and eBusiness,ERP II, Future Directions and Trends in ERP, ERP Resources on the
Web, ERP Case studies.
Expected Outcome
After Completion of course, students should be able to
1. Understand the architecture of ERP systems.
2. Understand the working of different modules in ERP.
3. Understand the correct choice of an ERP package for the selected industry.
References
1.
2.
3.
4.
Alexis Leon, “ERP Demystified”, McGraw-Hill Education India Pvt. Ltd.,3/e.
Rajesh Ray, “Enterprise Resource Planning”, TMH,2011.
Mary Sumner, “Enterprise Resource Planning”, Pearson Education, 2010.
Bradford M., “Modern ERP Systems: Select Implement and Use Today’s Advanced
Business Systems”,H&M Books,2010.
Hours Allotted
I
Introduction to ERP and Enterprise Applications: Overview, Need,
3
Cluster: 1
Branch: Mechanical Engineering
47
Stream: Financial Engineering
End-Semester
Examination
Contents
% of Marks in
Module
COURSE PLAN
15
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
History, Risks and Benefits, Enterprise Applications
II
III
IV
V
VI
ERP and Related Technologies
2
ERP Manufacturing Perspective.
2
Business Process Reengineering; Business Process Modeling and
Business Modeling
3
ERP software packages and selection of ERP package-various
approaches to ERP selection;Procurement process for ERP package,
Features of various modules of ERP.
FIRST INTERNAL EXAM
ERP Implementation—Life Cycle, Methodologies, Issues, Hidden costs,
Vendors, Consultants and Users; ERP Project Management;ERP
Security; ERP Training; Change Management; Application Support.
ERP Functional Modules: Human Capital Management; Financial
Management; Procurement and Inventory Management; Supplier
Relationship Management; Production Planning and Execution; Supply
Chain Planning; Sales and Service; Warehouse and Transport
Management;
Customer
Relationship
Management;
Quality
Management; Maintenance Management and Enterprise Asset
Management; Product Lifecycle Management
SECOND INTERNAL EXAM
ERP Market: SAP AG, Baan company, People soft, Oracle corporation,
Microsoft Dynamics, JD Edwards world solution company, QUAD
system software associates, Epicor ERP, Lawson ERP etc. Open source
ERP packages.
ERP and eBusiness, ERP II, Future Directions and Trends in ERP, ERP
Resources on the Web
ERP Case studies: HRM, finance, production, materials, sales and
distribution.
END SEMESTER EXAM
Cluster: 1
Branch: Mechanical Engineering
48
15
3
6
15
9
20
8
20
3
15
3
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Course No.
Course Name
L-T-P
Credits
Year of Introduction
01ME6624
BIG DATA ANALYTICS
3-0-0
3
2015
Course Objectives
1. To bring together several key technologies used in manipulating, storing, and analyzing big
data.
2. To make the student understand details of Hadoop.
3. To introduce tools that provides SQL-like access to unstructured data.
Syllabus
Big Data: Importance, Applications, Data Analysis, Mining Data Streams, Frequent Item Sets and
Clustering, Big Data Frameworks, Visualization.
Expected Outcome
Students who complete this course will be able to
1. Categorize and Summarize Big Data and its importance.
2. Manage Big Data and analyze Big Data.
3. Apply tools and techniques to analyze Big Data.
References
1. Michael Berthold, David J. Hand, “Intelligent Data Analysis”, Springer, 2007.
2. AnandRajaraman and Jeffrey David Ullman, “Mining of Massive Datasets”,
Cambridge University Press, 2012.
3. Bill Franks, “Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data
Streams with Advanced Analytics”, John Wiley & sons, 2012.
4. Glenn J. Myatt, “Making Sense of Data”, John Wiley & Sons, 2007
5. Pete Warden, “Big Data Glossary”, O’Reilly, 2011.
6. Jiawei Han, MichelineKamber “Data Mining Concepts and Techniques”, Second
Edition, Elsevier, Reprinted 2008.
I
Cluster: 1
Branch: Mechanical Engineering
49
Stream: Financial Engineering
End-Semester
Examination
7
% of Marks in
Introduction to BigData Platform – Traits of Big data -Challenges of
Conventional Systems - Web Data – Evolution Of Analytic Scalability Analytic Processes and Tools - Analysis vs Reporting - Modern Data
Module
Contents
Hours Allotted
COURSE PLAN
10
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Analytic Tools - Statistical Concepts: Sampling Distributions – Re-
II
III
IV
Sampling - Statistical Inference - Prediction Error.
Regression Modeling - Multivariate Analysis - Bayesian Modeling Inference and Bayesian Networks - Support Vector and Kernel
Methods - Analysis of Time Series: Linear Systems Analysis Nonlinear Dynamics - Rule Induction - Neural Networks: Learning
And Generalization - Competitive Learning - Principal Component
Analysis and Neural Networks - Fuzzy Logic: Extracting Fuzzy
Models from Data - Fuzzy Decision Trees - Stochastic Search Methods.
FIRST INTERNAL EXAM
Introduction To Streams Concepts – Stream Data Model and
Architecture - Stream Computing - Sampling Data in a Stream –
Filtering Streams – Counting Distinct Elements in a Stream –
Estimating Moments – Counting Oneness in a Window – Decaying
Window - Real time Analytics Platform(RTAP) Applications - Case
Studies - Real Time Sentiment Analysis, Stock Market Predictions.
Mining Frequent Itemsets - Market Based Model – Apriori Algorithm –
Handling Large Data Sets in Main Memory – Limited Pass Algorithm –
Counting Frequent Itemsets in a Stream – Clustering Techniques –
Hierarchical – K-Means – Clustering High Dimensional Data –
CLIQUE And PROCLUS – Frequent Pattern based Clustering Methods
– Clustering in Non-Euclidean Space – Clustering for Streams and
Parallelism.
SECOND INTERNAL EXAM
7
15
7
15
7
20
6
20
8
20
MapReduce – Hadoop, Pig, Hive, MapR – Sharding – NoSQL
V
VI
Databases - S3 - Hadoop Distributed File Systems –Oracle Big DataVisualizations - Visual Data Analysis Techniques - Interaction
Techniques
Systems and Analytics Applications - Analytics using Statistical
packages-Approaches to modeling in Analytics – correlation,
regression, decision trees, classification, association Intelligence from
unstructured information-Text analytics-Understanding of emerging
trends and technologies-Industry challenges and application of
Analytics.
END SEMESTER EXAM
Cluster: 1
Branch: Mechanical Engineering
50
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Course No.
Course Name
L-T-P
Credits
Year of Introduction
01ME6692
MINI PROJECT
0-0-4
2
2015
Course Objectives
To make students
Design and develop a system or application in the area of their specialization.
Approach
The student shall present two seminars and submit a report.The first seminar shall highlig
objectives, methodology, design and expected results. The second seminar is the presenta
work / hardwareimplementation.
Expected Outcome
Upon successful completion of the miniproject, the student should be able to
1. Identify and solve various problems associated with designing and implementing a system o
application.
2. Test the designed system or application.
Cluster: 1
Branch: Mechanical Engineering
51
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Course No.
01ME6694
Course Name
OPTIMIZATION AND
SIMULATION LABORATORY
L-T-P
Credits
Year of Introduction
0-0-2
1
2015
Course Objectives
1. Should acquire knowledge on simulation model building and simulation through software
packages.
2. Should have working knowledge of optimization software packages.
Syllabus
Simulation Modeling and Optimization with emphasis on Financial Applications.
Expected Outcome
1. Understand simulation model building and simulation through software packages
2. Use optimization software packages to solve optimization problems in Finance.
List of Experiments
1. Exercises on solving optimization problems using IBM ILOG CPLEX
/AIMMS/GAMS/Lindo/Lingo etc (free and open source, trial or free academic version of
the software package may be used).
Exercises shall be on:
Linear and Nonlinear Programming Problems
Integer Programming Problems
Quadratic Programming Problems
Robust Optimization etc.
2. Simulation model building and conducting simulation experiments using Simio /Arena
/ AnyLogic / Vensim /NetLogo etc. (free and open source/ trial / free academic version
of the software package may be used)
Exercises shall be on:
Discrete Event Modeling
System Dynamics
Agent Based Modeling
Cluster: 1
Branch: Mechanical Engineering
52
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
SEMESTER - III
Syllabus and Course Plan
Cluster: 1
Branch: Mechanical Engineering
53
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Course No.
Course Name
L-T-P
Credits
Year of Introduction
01ME7611
ASSET PRICING
3-0-0
3
2015
Course Objectives
1. To endow students with the foundations of financial economics and to expose them to the
classic and modern models of asset pricing.
2. To develop technical skills and become comfortable in modeling an economic problem of
their choice.
Syllabus
Introduction to Continuous-Time Stochastic Models: Diffusions & Diffusion Models; Equity
Premium and Risk, Time Varying Risk Premium, the Cross Section of Stock Returns, Asset Pricing
Theory; Classic issues in Finance; Equilibrium, Contingent Claims, Risk-Neutral Probabilities: Risk
Free Rate and Macroeconomics, Consumption and Risk Premiums; Contingent Claims, State Prices,
Risk-Neutral Probabilities; Mean-Variance Frontier; Implications of the Existence and Equivalence
Theorems; Factor Pricing Models, Value Premium, the Fama-French model; Term Structure
Models; Portfolio Theory.
Expected Outcome
1. The students completing this course will have a “Big Picture” conceptually and with
applicable tools in Asset Pricing.
References
1. John H. Cochrane,“Asset Pricing”, (Revised), Princeton University Press, 2003;
http://faculty.chicagobooth.edu/john.cochrane/teaching/35904_Asset_Pricing/
2. Rajnish Mehra, “Handbook of the Equity Risk Premium”, Elsevier,2008.
I
End-Semester
Examination
Contents
% of Marks in
Hours Allotted
Module
COURSE PLAN
Introduction to Continuous-Time Stochastic Models: Diffusions &
Diffusion Models; Ito's Lemma; Stochastic Differential Equations.
Cluster: 1
Branch: Mechanical Engineering
54
3
Stream: Financial Engineering
20
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Equity Premium and Risk, Time Varying Risk Premium, the Cross
3
Section of Stock Returns, Asset Pricing Theory.
Classic issues in Finance; Equilibrium, Contingent Claims, Risk-Neutral
II
Probabilities: Risk Free Rate and Macroeconomics, Consumption and
Risk Premiums, Risk Premiums & Betas, Mean Variance Frontier and
Roll Theorem, Random Walks & Time-Varying Risk Premiums, General
Equilibrium and Causality
6
15
8
20
FIRST INTERNAL EXAM
Contingent Claims, State Prices, Risk-Neutral Probabilities: States &
Complete Markets, Discount Factor in Complete Markets, Risk-Neutral
Probabilities in Complete Markets, Investors in Complete Markets, RiskIII
Sharing in Complete Markets, State-Space Geometry. Incomplete
Markets: Discount Factor in Incomplete Markets. Positive M &
Arbitrage.
Mean-Variance Frontier: Classic Approach, State-Space [HansenRichard] Approach, Comparing Frontiers, Roll Theorem.
Implications of the Existence and Equivalence Theorems: History and
Representation, Fishing, Mimicking Portfolio Theorem & Fishing.
Conditioning Information: Conditioning Down, Instruments
Managed Portfolios, Conditional & Unconditional Models
IV
&
3
Factor Pricing Models, Value Premium, the Fama-French model:
15
Introduction/Overview, CAPM / Simple 2-Period Quadratic, CAPM:
Derivation with Log Utility or IID Consumption Growth, ICAPM / State
Variables, Multifactor Models - Outside Income, Multifactor Models -
5
Portfolio Intuition, Multifactor Models – U’ Intuition, Macro, Mimicking
Portfolios, Arbitrage Pricing Theory (APT), APT vs Equilibrium Models.
SECOND INTERNAL EXAM
The Fama-French Model, The Fama/French 3-Factor Model, The
Fama/French Model, Using the 3-Factor Model, Momentum & Reversal.
V
Option Pricing: Payoffs, Arbitrage Bounds, Black-Scholes, Other
Method, Spanning, State Prices, and Current Models, Date, Smile,
7
Models.
Cluster: 1
Branch: Mechanical Engineering
55
Stream: Financial Engineering
15
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Term
VI
Structure
Models:
Introduction,
Definitions,
Expectation
Hypothesis, Risk Premium Introduction, Facts - Fama/Bliss, Statistical
Factor Models, Term Structure Model with Expectations Hypothesis,
Discrete-Time Vasicek, Other Approaches, Continuous-Time Term
7
Structure. Portfolio Theory: Classic Approach, Mean-Variance, Merton,
Merton Examples.
END SEMESTER EXAM
Cluster: 1
Branch: Mechanical Engineering
56
Stream: Financial Engineering
15
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Course Name
FINANCIAL BUSINESS
INTELLIGENCE
Course No.
01ME7613
L-T-P
Credits
Year of Introduction
3-0-0
3
2015
Course Objectives
The course aims at
1. Exposing the field of business intelligence systems.
2. Providing a practical understanding of the business intelligence life cycle and the
techniques used in it.
3. Helping the students to decide on appropriate technique.
Syllabus
Evolution, History and Power of Financial Business Intelligence; Business intelligence
architecturesBusiness focussed data analysisUser Types Visualization; Efficiency measures; BI
software packages; Financial Data Modelling.;Marketing models; Logistic and Production models;
Case studies.
Expected Outcome
Students who complete this course will be able to
1. Explain the fundamentals of business intelligence.
2. Link data mining with business intelligence.
3. Explain the data analysis and knowledge delivery stages.
4. Apply Business intelligence methods to decision making in finance.
References
1. Larissa T. Moss, S. Atre , “Business Intelligence Roadmap: The Complete Project
Lifecycle for Decision Making”, 1st Edition, Addison Wesley, 2003.
2. Carlo Vercellis, “Business Intelligence: Data Mining and Optimization for Decision
Making”, 1st Edition, Wiley Publications, 2009.
3. David Loshin Morgan, Kaufman, “Business Intelligence: The Savvy Manager's
Guide”, 2nd Edition, 2012.
4. Cindi Howson, “Successful Business Intelligence: Secrets to Making BI a Killer App”,
1st Edition, McGraw-Hill, 2007.
5. Nils H. Rasmussen, Paul S. Goldy, Per O. Solli, Financial Business Intelligence:
Trends, Technology, Software Selection, and Implementation”, John Wiley and Sons.
Cluster: 1
Branch: Mechanical Engineering
57
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Hours Allotted
I
Evolution of Financial Business Intelligence: History and Power of BI
tools. Financial and Non-financially focussed tools, Data storage
methodology, Data warehousing, Front end analytic tools.
5
Effective and timely decisions - Data, information and knowledge - Role
of mathematical models - Business intelligence architectures: Cycle of a
business intelligence analysis - Enabling factors in business intelligence
projects - Development of a business intelligence system - Ethics and
business intelligence.
5
Business focussed data analysis – Top down logical data modelling –
Bottom up source data analysis – Data cleansing – Deliverables of data
analysis – Importance of data analysis
4
II
End-Semester
Examination
Contents
% of Marks in
Module
COURSE PLAN
15
15
FIRST INTERNAL EXAM
III
IV
V
VI
The Business Intelligence User Types - Standard Reports - Interactive
Analysis and Ad Hoc Querying - Parameterized Reports and SelfService Reporting- Dimensional analysis - Alerts/Notifications
Visualization: Charts, Graphs, Widgets, Scorecards and Dashboards,
Geographic Visualization- Integrated Analytics- Considerations:
Optimizing the Presentation for the Right Message.
Efficiency measures – The CCR model: Definition of target objectives –
Peer groups – Identification of good operating practices: cross efficiency
analysis – Virtual inputs and outputs – Other models.
SECOND INTERNAL EXAM
Overview of Business Intelligence Software, Major software companies
in BI, Software Evaluation. Implementation of BI system: project
planning, Multidimensional model definition and maintenance,
financial data modelling.
Marketing models – Logistic and Production models – Case studies.
Future of business intelligence-Emerging Technologies, Predicting the
Future, BI Search & Text Analytics-Advanced Visualization- Rich
Report, Future beyond Technology.
5
20
4
5
15
6
15
8
20
END SEMESTER EXAM
Cluster: 1
Branch: Mechanical Engineering
58
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Course No.
01ME7415
Course Name
HEURISTIC SOLUTION
METHODS
L-T-P
Credits
Year of Introduction
3-0-0
3
2015
Course Objectives
The main objectives of this course are:1. To introduce the students to various metaheuristic solution algorithms.
2. To demonstrate the applications of these algorithms for solving large real life
problems
Syllabus
Introduction to Non-traditional optimization; Computational Complexity; Classification of
heuristic solution techniques; Metaheuristics; Introduction to evolutionary computation; Genetic
Algorithms: Concepts, Algorithm, Binary GA, Continuous GA, Hybrid GA, Parallel GA. Scatter
Search-Components, Algorithm, Applications. Multi objective evolutionary optimization; Greedy
Randomized Adaptive Search Procedure, Ant Colony Algorithms: Overview, Basic algorithm,
Variants; Particle Swarm Optimization; Lagrangean Relaxation; Local Search Algorithms; Tabu
Search; Simulated Annealing, Components, Variants of Simulated Annealing; Artificial Neural
Networks- Biological and Artificial Neural Networks, Basic Concepts, Generic Algorithm,
Constraint Programming- Problem Formulation in Constraint Programming, Basic Search and
Constraint Propagation, Constraint Programming vs Mathematical Programming; Applications of
the above mentioned heuristic methods to solve different types of optimization problems.
Expected Outcome
After Completion of course,
1. The students will have the knowledge of various metaheuristic solution algorithms and
their applications.
2. The students will have the skill to model real life problems and will be able to apply proper
heuristic techniques to solve them.
References
1. GüntherZäpfel , Roland Braune, Michael Bögl, “Metaheuristic Search Concepts-A
Tutorial with Applications to Production and Logistics”, Springer.
2. Michalewicz Z, “Genetic Algorithms + Data Structures = Evolution Programms”,
Springer-Verlag,Berlin.
3. J.Dreo,A.Petrowski, EricTaillard , “Metaheuristics for Hard Optimization:Methods
Cluster: 1
Branch: Mechanical Engineering
59
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
and case studies”, Springer.
4. Colin R. Reeves, “Modern Heuristic Techniques for Combinatorial Problems”, John
Wiley and Sons.
I
Introduction
to Non-traditional
optimization, Computational
Complexity; Heuristics – classification: Construction Heuristics, Local
Search, Multi-Start Procedures; Assessing the Quality of Heuristics.
Metaheuristics- Definition, Classification.Introduction to evolutionary
computation
II
Genetic Algorithm - Genetic Algorithms: Basic concepts, Encoding,
Selection, Crossover, Mutation-Binary GA, Continuous GA, Hybrid GA,
Parallel GA-Application of GA in solving Constrained and
Combinatorial Optimization problems, Reliability problem, Sequencing
problem, Scheduling problem, Transportation problem etc.
End-Semester
Examination
Contents
% of Marks in
Module
Hours Allotted
COURSE PLAN
4
10
2
4
20
Scatter Search-Components, Algorithm, Applications.
1
Multi objective evolutionary optimization: Pareto optimality, Multiobjective evolutionary algorithms.
3
FIRST INTERNAL EXAM
Greedy Randomized Adaptive Search Procedure
III
IV
V
1
Ant Colony Algorithms: Overview, Basic algorithm, Variants,
Formalization and properties of ant colony optimization, Applications
in Scheduling, VRP etc
Particle Swarm Optimization – Basic Concepts: Social Concepts, Swarm
Intelligence Principles, Computational Characteristics; PSO in Real
Number Space: Velocity Updating, Topology of the Particle Swarm,
Parameter Selection; Discrete PSO; PSO Variants; PSO Applications in
TSP, Knapsack Problems, Quadratic Assignment Problem etc.
Lagrangean Relaxation: Basic Methodology, Lagrangean heuristic and
problem reduction, Lagrangean multipliers, Dual Ascent algorithm,
Tree search. Applications of Lagrangean Relaxation in solving facility
location problems, Logistics, Inventory Problems etc.
SECOND INTERNAL EXAM
Local Search Algorithms, Tabu Search –Tabu Search Principles,
Neighborhood, Candidate list, Short term and Long term memory,
Cluster: 1
Branch: Mechanical Engineering
60
4
20
3
6
10
5
20
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Threshold Accepting, Application of TS in Planning and Scheduling,
Telecommunications,
Portfolio
management,
Facility
layout,
Transportation, Routing and Network Design.
VI
Simulated Annealing -Main Components of Simulated Annealing,
Homogenous vs. Inhomogenous Simulated Annealing, Annealing
Schedules, Applications in sequencing and scheduling, Travelling
salesman problem etc. Variants of Simulated Annealing.
3
Artificial Neural Networks- Biological and Artificial Neural Networks,
Basic Concepts, Generic Algorithm, Application Areas, Application of
ANN to solve TSP, Knapsack Problems etc.
3
Constraint Programming- Problem Formulation in Constraint
Programming, Basic Search and Constraint Propagation, Constraint
Programming vs Mathematical Programming, Application of Constraint
Programming in Bin Packing, Scheduling, Sequencing, Facility Location
problems etc.
END SEMESTER EXAM
Cluster: 1
Branch: Mechanical Engineering
61
20
3
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Course No.
Course Name
L-T-P
Credits
Year of Introduction
01ME7617
PREDICTIVE MODELING
3-0-0
3
2015
Course Objectives
1. To understand the terms and terminologies of predictive modeling.
2. To study the various predictive models, their merits, demerits and application.
3. To get exposure to various analytical tools available for predictive modeling.
Syllabus
Data Mining, Partitioning, Cleaning, Splitting; Artificial Neural Networks; Multivariate Analysis;
Association Rules; Clustering Models; Time Series Models; Predictive Modeling Markup Language;
Tools and Technologies in Predictive Modeling; Modeling Business Problems and solution
programs using R language.
Expected Outcome
Students who complete this course will be able to
1. Design and analyze appropriate predictive models.
2. Define the predictive models using PMML.
3. Apply statistical tools for analysis.
References
1. Kattamuri S. Sarma, “Predictive Modeling with SAS Enterprise Miner: Practical
Solutions for Business Applications”, 2nd Edition, SAS Publishing, 2007.
2. Alex Guazzelli, Wen-Ching Lin, Tridivesh Jena, James Taylor, “PMML in Action
Unleashing the Power of Open Standards for Data Mining and Predictive
Analytics”, 2nd Edition, Create Space Independent Publishing Platform,2012.
3. Ian H. Witten, Eibe Frank, “Data Mining: Practical Machine Learning Tools and
Techniques”, Morgan Kaufmann Series in Data Management Systems, Morgan
Kaufmann, 3rd Edition, 2011.
4. Eric Siegel, “Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or
Die”, 1st Edition, Wiley, 2013.
5. Conrad Carlberg, “Predictive Analytics: Microsoft Excel”, 1st Edition, Que
Publishing, 2012.
6. Jeremy Howard, Margit Zwemer, Mike Loukides, “Designing Great Data ProductsInside the Drivetrain Approach, a Four-Step Process for Building Data Products –
Ebook”, 1st Edition, O'Reilly Media, March 2012.
7. Thomas W Miller, “Modeling Techniques in Predictive Analytics: Business Problems
and Solutions with R”, Pearson Education, Inc., 2014.
Cluster: 1
Branch: Mechanical Engineering
62
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
End-Semester
Examination
Contents
% of Marks in
Hours Allotted
Module
COURSE PLAN
Core ideas in data mining - Supervised and unsupervised learning I
Classification vs Prediction -Steps in data mining- SEMMA Approach –
Sampling -Pre-processing - Data cleaning - Data Partitioning - Building
7
15
7
15
10
20
4
15
6
15
8
20
a model - Statistical models - Statistical models for predictive analytics.
Data splitting – Balancing- Overfitting –Oversampling –Multiple
II
Regression - Artificial neural networks (MLP) - Variable importanceProfit/loss/prior probabilities - Model specification - Model selection Multivariate Analysis.
FIRST INTERNAL EXAM
Association Rules-Clustering Models –Decision Trees- Ruleset ModelsK-Nearest Neighbors – Naive Bayes - Neural Network Model –
III
Regression Models – Regression Trees – Classification & Regression
Trees (CART) – Logistic Regression – Mulitple Linear Regression
Scorecards –Support Vector Machines – Time Series Models Comparison between models - Lift chart - Assessment of a single model.
Introduction to Predictive Modeling Markup Language (PMML) –
IV
PMML Converter - PMML Structure – Data Manipulation in PMML –
PMML Modeling Techniques - Multiple Model Support – Model
Verification.
SECOND INTERNAL EXAM
Tools and Technologies in Predictive Modeling: Weka – RapidMiner –
V
IBM SPSS Statistics- IBM SPSS Modeler – SAS Enterprise Miner –
Apache Mahout – R Programming Language.
VI
Modeling Business Problems and solution programs using R language:
Advertising and Promotion, Preference and Choice, Market Basket
Analysis, Economic Data Analysis, Operations Management, Text
Analytics, Sentiment Analysis, Brand and Price, Sports Analytics.
END SEMESTER EXAM
Cluster: 1
Branch: Mechanical Engineering
63
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Course No.
Course Name
L-T-P
Credits
Year of Introduction
01ME7419
MANAGERIAL ECONOMICS
3-0-0
3
2015
Course Objectives
The main objectives of this course are:1. To develop an understanding of the basic concepts, tools and techniques of economics
2. Application of these techniques to various areas of decision making.
3. To help the students to appraise business around him and to develop skills and to relate
corporate decision on the future prospects of business.
Syllabus
Introduction, economic profit, firms, demand theory and forecasting, production theory and
analysis, cost theory and analysis, market structure and equilibrium, CVP analysis pricing
decisions and introduction to taxes and duties.
Expected Outcome
1. The students will acquire the knowledge of economic theory to ascertain the demand to
help decision making on managerial perspective.
2. The students will become able to analyze a business situation based on the knowledge on
pricing, costing and production functions of firms.
3. The students will become conversant on tax and tax laws to enable the nation's and the
firm's growth.
1.
2.
3.
4.
5.
6.
7.
8.
9.
References
H. Craig Petersen and W. Cris Lewis, “Managerial Economics”, Pearson Education
D. N. Dwivedi, “Microeconomics: Theory and Applications”, Pearson Education
H. Scott Bierman and Luis Fernandez, “Game Theory with Economic Applications”,
Pearson Education.
Karl.E.Case and R.C.Fair, “Principles of Economics”, Pearson Education
A. Ramachandra Aryasry and V.V. Ramana Murthy,“Engineering Economics and
Financial Accounting”:, Tata Mc graw Hill Publishing Company Ltd., New Delhi,
2004.
V. L. Mote, Samuel and G. S. Gupta, “Managerial Economics – Concepts and cases”,
Tata McGraw Hill Publishing Coimpany Ltd, New Delhi, 1981.
A.Nag, “Macro Economics for Management Students”, MacMillan India Ltd., New
Delhi, 1999.
Jawaharlal, “Cost Accounting”, Tata McGraw Hill.
Norman N Barish, “Economic Analysis for Engineering and Managerial Decision
Making”, McGraw Hill Book Company, 1983.
Cluster: 1
Branch: Mechanical Engineering
64
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Cluster: 1
Branch: Mechanical Engineering
65
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
End-Semester
Examination
% of Marks in
Module
Hours Allotted
COURSE PLAN
Contents
I
Introduction to Managerial Economics, The Nature of the Firm,
Economic Profit, Profit in a marketsystem, Economics and Decision
making, Total, Average and Marginal concepts, Economic models.
6
15
Demand Theory and Analysis:- Individual demand, Market demand,
Total and Marginal Revenue, PriceElasticity, Income Elasticity and
Cross Elasticity, Use of Regression analysis for Demand estimation.
Economic Forecasting: - Sources of data, Time Series Analysis – Trend
projection, ExponentialSmoothing; Barometric Forecasting, Input /
Output analysis.
FIRST INTERNAL EXAM
3
15
Production Theory and Analysis:- The Production Function, Isoquants –
Expansion path, Cobb Douglas function – Cost concepts – Cost output
relationship – Economies and diseconomies of scale – Cost functionsDetermination of cost- Estimation of cost. Economies of Scale and
Scope, Estimating the Production Function.
Cost Theory and Analysis:- Economic concept of cost, Production and
Cost, Short-Run and Long-RunCost functions, Profit Contribution
Analysis, Operating Leverage, Estimating Cost Functions.
7
15
7
15
Market Structure – Various forms – Equilibrium of a firm – Perfect
competition – Monopolistic competition – Oligopolistic competition –
Pricing of products under different market structures – Methods of
pricing – Factors affecting pricing decision – Differential pricing –
Government Intervention and pricing.
4
20
Monopolypower and its measurement - regulation in practice - pricing
under Oligopoly – NashEquilibrium - Cournot Model - Collusion and
Cartel
Pricing Decisions:- Pricing of Goods and Services, Pricing of Multiple
Products, Price Discrimination,Product bundling, Peak-Load pricing,
Markup Pricing, Input pricing and Employment, Economic Rent,Wage
and Income Differentials
4
The concept of profit: Profit planning, control and measurement of
profits. Profit maximization – Cost volume profit analysis – Investment
Analysis. Introduction to Excise duty,Taxes on Profit, Taxes on Inputs,
Property taxes and Tax preferences.
4
II
III
IV
3
SECOND INTERNAL EXAM
V
VI
Cluster: 1
Branch: Mechanical Engineering
66
4
Stream: Financial Engineering
20
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
END SEMESTER EXAM
Course No.
Course Name
L-T-P
Credits
Year of Introduction
01ME7621
FINANCIAL MODELING
3-0-0
3
2015
Course Objectives
1. To enable the student to model, design and implement a wide range of financial models for
derivatives pricing and asset allocation
Syllabus
Financial Markets; Diffusion Models; Models with Jumps; Multi-Dimensional Models; Option
Pricing by Transform Techniques and Direct Integration; Pricing Non-Standard Vanilla Options;
Bermudan and American Options; The Cosine Method and Barrier Options; Monte Carlo
Simulation and Applications; Calibration and Optimization; Model Risk.
Expected Outcome
1. The students will be able to describe and review market models
2. The students will be able to use numerical methods for pricing and risk management
References
1. Joerg Kienitz, Daniel Wetterau, “Financial Modelling: Theory, Implementation and
Practice with MATLAB Source”, John Wiley & Sons
2. Alastair Day ; “Mastering Financial Modelling”, Penguin Books Ltd, 2/e, 2008
3. Swan Jonathan, “Practical Financial Modelling: A Guide to Current Practice”,
Elsevier, 2/e, 2008.
Hours Allotted
I
Financial Markets- Financial Time-Series, Statistical Properties of
Market Data and Invariants, Implied Volatility Surfaces and Volatility
Dynamics, Applications- Asset Allocation, Pricing, Hedging and Risk
Management.
5
Cluster: 1
Branch: Mechanical Engineering
67
Stream: Financial Engineering
End-Semester
Examination
Contents
% of Marks in
Module
COURSE PLAN
15
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Diffusion Models: Local Volatility Models- The Bachelier and the Black–
II
Scholes Model, The Hull–White Model, The Constant Elasticity of
Variance Model, The Displaced Diffusion Model, CEV and DD Models;
Stochastic Volatility Models; Stochastic Volatility and Stochastic Rates
Models.
Models with Jumps: Poisson Processes and Jump Diffusions;
Exponential L´evy Models; Exponential L´evy Models with Stochastic
Volatility, Stochastic Clocks, Martingale Correction.
5
15
4
FIRST INTERNAL EXAM
III
Multi-Dimensional Models: Multi-Dimensional Diffusions; MultiDimensional Heston and SABR Models; Parameter Averaging;
Markovian Projection; Copulae.
5
15
Option Pricing by Transform Techniques and Direct Integration: Fourier
IV
V
Transform; he Carr–Madan Method; The Lewis Method; The Attari
Method; The Cosine Method; Comparison, Stability and Performance;
Extending the Methods to Forward Start Options; Density Recovery.
5
Pricing Non-Standard Vanilla Options; Bermudan and American
Options; The Cosine Method and Barrier Options.
4
SECOND INTERNAL EXAM
Monte Carlo Simulation and Applications: Sampling Diffusion
Processes; Special Purpose Scheme; Adding Jumps; Bridge Sampling;
Libor Market Model; Multi-Dimensional L´evy Models.
Calibration
and
Optimization:
The
Nelder–Mead
Method;
5
Differential Evolution; Simulated Annealing.
5
20
Model Risk – Calibration, Pricing and Hedging: Calibration; Pricing
Exotic Options; Hedging
4
END SEMESTER EXAM
Cluster: 1
Branch: Mechanical Engineering
68
20
he
Levenberg–Marquardt Method; The L-BFGS Method; The SQP Method;
VI
15
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Course No.
Course Name
L-T-P
Credits
Year of Introduction
01ME7691
SEMINAR II
0-0-2
2
2015
Course Objectives
To make students
1. Identify the current topics in the specific stream.
2. Collect the recent publications related to the identified topics.
3. Do a detailed study of a selected topic based on current journals, published papers
and books.
4. Present a seminar on the selected topic on which a detailed study has been done.
5. Improve the writing and presentation skills.
Approach
Students shall make a presentation for 20-25 minutes based on the detailed study of
the topic and submit a report based on the study.
Expected Outcome
Upon successful completion of the seminar, the student should be able to
1. Get good exposure in the current topics in the specific stream.
2. Improve the writing and presentation skills.
3. . Explore domains of interest so as to pursue the course project.
Cluster: 1
Branch: Mechanical Engineering
69
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Course No.
Course Name
L-T-P
Credits
Year of Introduction
01ME7693
PROJECT (PHASE 1)
0-0-12
6
2015
Course Objectives
To make students
1. Do an original and independent study on the area of specialization.
2. Explore in depth a subject of his/her own choice.
3. Start the preliminary background studies towards the project by conducting
literature survey in the relevant field.
4. Broadly identify the area of the project work, familiarize with the tools required for
the design and analysis of the project.
5. Plan the experimental platform, if any, required for project work.
Approach
The student has to present two seminars and submit an interim Project report. The
first seminar would highlight the topic, objectives, methodology and expected
results. The first seminar shall be conducted in the first half of this semester. The
second seminar is the presentation of the interim project report of the work
completed and scope of the work which has to be accomplished in the fourth
semester.
Expected Outcome
Upon successful completion of the project phase 1, the student should be able to
1. Identify the topic, objectives and methodology to carry out the project.
2. Finalize the project plan for their course project.
Cluster: 1
Branch: Mechanical Engineering
70
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
SEMESTER – IV
Syllabus and Course Plan
Cluster: 1
Branch: Mechanical Engineering
71
Stream: Financial Engineering
Kerala Technological University
Master of Technology – Curriculum, Syllabus & Course Plan
Course No.
Course Name
L-T-P
Credits
Year of Introduction
01ME7694
PROJECT (PHASE 2)
0-0-23
12
2015
Course Objectives
To continue and complete the project work identified in project phase 1.
Approach
There shall be two seminars (a mid term evaluation on the progress of the work and
pre submission seminar to assess the quality and quantum of the work). At least one
technical paper has to be prepared for possible publication in journals / conferences
based on their project work.
Expected Outcome
Upon successful completion of the project phase II, the student should be able to
1. Get a good exposure to a domain of interest.
2. Get a good domain and experience to pursue future research activities.
Cluster: 1
Branch: Mechanical Engineering
72
Stream: Financial Engineering