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Dynamic Scenario Generator
Essential in risk management and
investment decision-making
Scenario analysis
Financial decision-making and risk management involve dealing with uncertainty. The extent to which financial goals will be
realized or risk limits violated depends on the path that financial and economic risk factors follow in the future. A good financial
risk management process helps to:
Identify the most important sources of uncertainty, the so-called risk factors
Estimate the expected return for taking this risk
Quantify and compare the impact of policy alternatives for dealing with these uncertainties so that optimal decisions can be made
Monitor the consequences of decisions over time to assess if the chosen strategy is still the right one
Generating scenarios
Scenario analysis, also known as Monte Carlo simulation or dynamic financial analysis, is a powerful tool for quantifying the uncertainty
of risk factors by generating a large number of plausible and relevant future evolutions of these variables. How do we define a scenario?
“A scenario is a possible evolution of the future consistent with a clear set of assumptions”.
This definition emphasizes that generating scenarios consists of two important steps:
1. Make explicit assumptions about the foreseen future behaviour of the risk factors. This is important for being able to interpret the
results of models that are based on the scenarios. Furthermore the properties of the scenarios directly impact the outcomes of such
models and therefore impact the decision making and risk management results.
2. An Economic Scenario Generator must be capable of producing scenarios that are fully consistent with these assumptions.
Scenario analysis is preferred over other approaches because:
It deals in a flexible manner with real world complexity in terms of relations within and between the risk factors and the
underlying goals and risk limits
It makes it possible to understand risks and help explain why certain decisions are the right ones.
Its strong visual aspects lead to better understanding and acceptance of the results obtained by the models, making it easier for
decision makers to adopt these models in practice.
Dynamic Scenario Generator
Ortec Finance has over twenty-five years experience in applying scenario analysis to financial decision-making and financial
risk management for both institutional and private clients across the globe. During this period the requirements placed on the
underlying generators of economic scenarios have steadily increased. To continue meeting these requirements Ortec Finance
developed its next generation Dynamic Scenario Generator (DSG).
The Dynamic Scenario Generator generates
world-class economic scenarios that are:
Consistent framework
1. M
ulti-horizon: scenarios with a horizon ranging from
several months to several decades
2. M
ulti-frequency: scenarios with a flexible simulation
frequency in terms of months or years
3. M
ulti-dimension: scenarios for tens to hundreds of risk
factors
4. R
ealistic: the future behaviour of the risk factors in the
scenarios resembles a wide range of important empirical
features of how financial markets and economies are
interrelated and dynamically evolve over time
5. U
p-to-date: scenarios are continuously aligned with the
most recent situation and dynamics of financial markets
in economies around the world.
The Dynamic Scenario Generator can provide scenarios for
developing strategies to meet long- and short-term goals,
for implementing these strategies, and for monitoring and
risk management of the implemented strategies against the
original goals. The DSG provides one consistent framework
of scenarios for all these different purposes as opposed to
having to resort to different scenario models for different applications. This consistency is of crucial importance for the
quality of financial decision-making and risk management,
and thereby increases the chances that institutional and private clients will ultimately meet their goals.
The five characteristics listed above, and in particular the fourth one, embody the “clear set of assumptions” from the stated
scenario definition. In order to arrive at scenarios that are fully consistent with these requirements, the DSG employs a unique
combination of statistical and econometric techniques which efficiently process vast amounts of time series data. The methodology is a mixture of frequency domain techniques, dynamic factor models and special techniques for dealing with nonnormal distributions such as skewness and heavy tails. The main purpose of the central frequency domain methodology is
to describe all empirical features of the behaviour of time series of financial and economic variables at the same time, rather
than focusing on only one or a few specific aspects.
the dsg at work - an example
The following figure illustrates how a stock price index has been decomposed into a long-term trend, business cycle and
monthly component using frequency domain-based filtering techniques. The central idea behind such a decomposition is
that these components contain information about the the long-, medium- and short-term dynamics of the stock price index
(returns). A correct replication of these dynamics in the scenarios is then achieved by modeling these components separately
and in the end recombining the component scenarios into scenarios for the total stock price index.
Equities US
Equities US
Decomposition:
Long-term trend
Medium-term behavior
Short-term behavior
Long-term trend scenarios
Medium-term scenarios
Short-term scenarios
Scenario properties
Some of the features of the empirical data data that are jointly replicated in the properties of the DSG scenarios can be found
below. Please note that this list is not complete (term structure dynamics, seasonal patterns, etc.) and that every item in the
list has its own extensive academic literature.
Horizon
Risk and return in terms of volatilities, correlations and distributions vary
with the horizon. Volatilities increase with the horizon, but typically not following the pattern of a simple random walk. Instead some variables show
lower volatility due to “mean reversion”, while others show higher volatility due to “trending behaviour”. Both historical data and the scenarios
reflect close to zero correlation between equities and inflation on short
horizons but a steady increase in correlation as the horizon gets longer.
Business cycles
Business cycle behaviour is observed in all times, countries and variables and shows specific and well-documented dynamic relations. Stock
prices, for example, tend to go down before GDP goes down which means
that stock prices tend to lead the real economy. When there are signs
of recovery in GDP unemployment will still be increasing for some time,
showing that unemployment typically lags the economy. Business cycles
from the DSG methodology strongly resemble the OECD composite leading indicator.
Volatilities
Risk varies over time as volatility itself is highly volatile with specific
dynamics and correlations. Both realized and implied volatilities show
great deviations from the average volatility on the same sample. Both
historical data and the scenarios show that volatility is negatively correlated with the returns on the underlying value (when volatility is high,
returns are low).
US Equity volatility
Historical
Scenarios
US Equity returns
Scenario properties (continued)
Tail risk
Risk varies across distributions as dependencies within and between
financial markets become stronger when conditions worsen. In both
historical data and scenarios this can be seen from the fact that correlations increase and converge towards a value of one (near-perfect)
correlation as we move further into the left tails of the distributions. This
important feature, by definition, cannot be replicated by a Normal distribution with a fixed correlation.
Tail Correlation Monthly Equity Returns
Quantile
Probability Distribution Annual Equity Returns
Non-normal distributions
Non-normal distributions are often observed in empirical data on both
long and short horizons. Both the historical data and the scenarios reflect
this. Stock returns, for example, have fat tails and are skewed to the left
while the distributions of long-term interest rates are skewed to the right.
Imposed views
To improve the quality of the scenarios and their expectations, views are used to add relevant information that is not included
in the underlying models or in the historical data on which these models have been calibrated. Views are of a more subjective
nature, think for example of the impact of central bank interventions on interest rates or personal expectations on the risk
premium of certain asset classes. Our methodology supports imposing these views as consistently as possible, taking into
account the (historical) dynamics and correlation structures.
use of scenarios
Scenarios are used in a wide range of applications by pension funds, insurers, sovereign wealth funds, financial planners and
housing associations.
Pension Funds
Pension Providers
Sovereign wealth funds
Asset Managers
Asset Liability Management
Portfolio Construction
Investment Strategies
Risk Monitoring
Insurance Companies
Asset Liability Management
Economic Capital Calculations
Risk Monitoring
Capital Management
Risk Neutral Scenarios
Valuation and pricing of
guarantees and profit sharing
Private Wealth
Managers
Wealth Planning
Asset Liability Management
Portfolio Construction
Risk Monitoring
Real Estate Managers
Asset Liability Management
Portfolio Construction
Dynamic Risk Analysis
Dynamic Scenario Generator
Essential in risk management and
investment decision-making
Why the DSG from Ortec Finance?
Using our Dynamic Scenario Generator provides the following advantages:
Only one real world scenario set is required for strategy, implementation and monitoring purposes. This brings important
consistency to the decision-making process.
High-frequency periodic updates based on new monthly data and changed market conditions can help signal the
consequences of deteriorations in the economic climate and increases in risk at an early stage.
Ortec Finance is an independent company with extensive global experience in applying real world scenarios in Asset
Liability Management (ALM) and risk management solutions.
Modeling real world scenarios is continually evolving. Ortec Finance has its own academic research team which
constantly reviews, improves and extends the models and techniques.
Ortec Finance employs its own researchers, economists and IT professionals to make professional, complete and flexible
tooling. This allows clients to operate the DSG themselves or embed the real world scenarios in their own decision-making solutions.
It produces real world scenarios to support financial decision-making and risk management. Risk neutral or arbitrage free
scenarios for valuation purposes are also included in the Ortec Finance range of solutions.
Ortec Finance
 Offices
Ortec Finance is a global provider of technology and advisory services for risk
and return management. Established in Rotterdam in 1981, Ortec Finance has
190 employees who combine mathematical skills, business acumen and customer
service to deliver innovative and pragmatic solutions. Our client base operates in
the pension, investment management, insurance, real estate and private wealth
management markets.
Rotterdam
Boompjes 40
3011 XB Rotterdam
The Netherlands
Tel. +31 (0)10 700 50 00
Amsterdam
Naritaweg 51
1043 BP Amsterdam
Nederland
Tel. +31 (0)20 700 97 00
We help improve the decision-making and -monitoring of investors.
We do so by designing, building and applying solutions for asset-liability management, performance measurement and risk attribution, and financial planning. Our
solutions consist of a combination of software models and professional consultancy. They remain on the cutting edge due to the strong ties between Ortec
Finance and the academic community.
London
Suite 9.10, City Tower
40 Basinghall Street
London,EC2V 5DE
United Kingdom
Tel. +44 (0)20 3770 5780
Pfaffikon
Poststrasse 4
8808 Pfäffikon SZ
Switzerland
Tel. +41 (0)55 410 38 38
[email protected]
www.ortec-finance.com