Download Data Sourcing, Statistical Processing and Time Series Analysis

Presented at EDAMBA summer school, Soréze (France)
23 July – 27 July 2009
Data Sourcing, Statistical Processing
and Time Series Analysis
An Example from Research into
Hedge Fund Investments
Presenter:
University:
Supervisor:
Research Title:
Contact:
Florian Boehlandt
University of Stellenbosch – Business School
Prof Eon Smit
Prof Niel Krige
Pricing hedge funds a.k.a.
The sustainability of parametric and semiparametric pricing models as estimators of
hedge fund performance
[email protected]
‘In the business world, the rearview mirror is
always clearer than the windshield’
- Warren Buffett -
Research Purpose
1. Developing accurate parametric pricing
models for hedge funds and fund of hedge
funds
2. Accounting for the special statistical
properties of alternative investment funds
3. Providing practitioners and statisticians with
a framework to assess, categorize and predict
hedge fund investments
Research Approach
Research Philosophy
Logical-positivistic, deductive research:
Postulation of hypotheses that are tested via standard statistical procedures
Research Approach
Empirical analysis:
Interpreting the quality of pricing models on the basis of historical data
Data Sourcing
External secondary data:
Historic time series adjusted for data-bias effects
Data Sourcing
Data
Sources
Hedge Fund
Databases
Financial
Databases
Risk
Simulation
Monte Carlo
(Solver)
Confidence
(RiskSim)
CISDM/MAR
DATA
POOL
Data Treatment
Data
Treatment
DATA
POOL
Risk
Simulation
Statistical
Processing
Excel / VBA
Statistica
EViews
FACTOR
ANALYSIS
STATISTICAL
CLUSTERING
MODEL
BUILDING
Data Import
Access Database
Information
• Code
• Fund (Name)
• Main Strategy
Performance
• MM_DD_YYYY (Date)
• Yield
• Ptype (ROI or AUM)
System
Information
• Leverage (Yes/No)
Excel Pivot table report
Database Management
•
•
•
•
Avoiding duplicate entries
Cross-referencing data from various sources
Combining and aggregating different databases
Efficient storage due to relational data
management
• Queries allow for retrieval/display of specific data
• Linked-in with Microsoft VBA and Excel (data
displayable as Pivot table reports)
• Searching for specific entries via SQL
Data Bias
Survivorship
Inclusion of graveyard funds
SelfSelection
Multiple databases
Database
Instant
History
Look-ahead
Rolling-window observation / Incubation
period
Statistical tests for TSA
• Regression Statistics (Alpha, Average Error term,
Information Ratio)
• Normality (Chi-squared, Jarque Bera)
• Goodness of fit, phase-locking and collinearity
(Akaike Information Criterion, Hannan-Schwartz)
• Serial Correlation (Durbin-Watson, Portmanteau)
• Non-stationarity (unit root)
Prediction Models
Prediction
Models
AR
GLS
PCA
Polynomial
Fitting
Constrained
ARMA
Univariate
Taylor Series
Lagrange
ARIMA
Multivariate
Higher CoMoments
KKT
Conditional
Simulation
Literature Review
• Hedge Fund Linear Pricing Models
– Sharpe Factor Model (Sharpe, 1992)
– Constrained Regression (Otten, 2000)
– Fama-French Factor Model (Fama, 1992)
• Factor Component Analysis (Fung, 1997)
• Simulation of Trading component (lookback
straddle)
Sources
Fama, E.F. & French, K.R. 1992. The Cross-Section of Expected Stock Returns.
Journal of Finance, 47(2), June, 427-465. [Online] Available:
http://links.jstor.org/sici?sici=00221082%28199206%2947%3A2%3C427%3ATCOESR%3E2.0.CO%3B2-N
Fung, W. & Hsieh, D.A. 1997. Empirical characteristics of dynamic trading
strategies: the case of hedge funds. Review of Financial Studies, 10(2), Summer,
275-302. [Online] Available: http://faculty.fuqua.duke.edu/~dah7/rfs1997.pdf
Otten, R. & Bams, D. 2000. Statistical Tests for Return-Based Style Analysis. Paper
delivered at EFMA 2001 Lugano Meetings, July. [Online] Available:
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=277688
Sharpe, W.F. 1992. Asset allocation: management style and performance
measurement. Journal of Portfolio Management, Winter, 7-19. [Online] Available:
www.uic.edu/classes/fin/fin512/Articles/sharpe.pdf