Macro-Economic Variable Stress Testing
Effective ESGs are implemented using econometric models for the set of market and macroeconomic variables of interest in performing various multiperiod portfolio simulations. These econometric models have varying degrees of sophistication, including simple contemporaneous transformations, single factor autoregressive models, single factor GARCH models with changing volatility, common factor models, multifactor contemporaneous models, multifactor autoregressive models, and multifactor MGARCH models with changing volatilities and correlations. The risk factor vector modeling and simulation capabilities in KRM support this entire range of econometric models for ESG.
Two important characteristics of an effective ESG are (i) an ability to generate time series for individual market and macroeconomic variables that exhibit trends, volatility, and other stochastic behavior that describes real world observed dynamics for each variable, and (ii) an ability to generate multiple time series simultaneously that exhibit interdependencies and correlations observed in the real world. The risk factor vector modeling and simulation capabilities in KRM implement the first characteristic by allowing each component risk factor to have an associated transformation function that can describe the trend, volatility, and other dynamics of the component risk factor. The second characteristic is implemented by defining derived risk factors with values that depend on the values of one or more basic risk factors and by describing correlated changes in the values of basic risk factors. This permits the KRM Analytical Engine to simulate economically coherent market and macroeconomic time series.
Another essential characteristic of an effective ESG is the ability to generate market and macroeconomic time series over a multiple-year simulation horizon with a periodicity required for the analysis being performed. Some analyses performed by insurance companies require monthly market and macroeconomic time series over simulation horizons as long as 80 years, so the ESG must generate the time series for almost 1,000 monthly periods. The risk factor vector modeling and simulation capabilities in KRM support this need by allowing users to specify the periodicity and number of periods in a simulation calendar with no effective limitations.
Flexibility in model structure is an additional desirable characteristic of an effective ESG. Each organization has its own unique exposures to the market and macroeconomic sources of risk, and the ESG should be capable of being configured to produce market and macroeconomic time series that describe these specific risk sources. The risk factor vector modeling and simulation capabilities in KRM support this need by allowing users to model the risk sources they believe are material to the results produced by their portfolio analyses without requiring modeling of unnecessary risk sources.
While sophisticated economic scenario generation capabilities are available in the KRM solution, users are not precluded from using market and macroeconomic time series generated by external ESG software when performing multiperiod portfolio analyses. The open structure of the KRM Database allows these externally-generated time series to be easily imported and applied to portfolio simulations.
Kamakura has expertise in econometric modeling that is available on a consulting basis for development and application to ESG. We have developed a variety of econometric models for other clients, and we can offer our expertise, experience, and existing models to assist an insurer with its ESG needs.
Kamakura advantage for Macroeconomic Variable Stress Testing
- KRM enables clients to define, integrate, and stress an unlimited cross section of macro-economic (risk) factors, such as the consumer price index (“CPI”) for a specific country, the gross domestic product (“GDP”) index for a specific country, the unemployment rate for a specific country or geographical subdivision, the home sales index for a specific country or geographical subdivision, etc.
- KRM clients can tie default probabilities and losses given defaults (or recovery rates = 1 – LGDs) to these macro-economic factors.
- KRM clients can relate movements in the relevant underlying macro-economic factors to drive default probabilities and losses given defaults (or recovery rates = 1 – LGDs).
- KRM clients can instantaneously and/or over a defined horizon stress the relevant underlying macro-economic factors to generate comprehensive and robust credit-adjusted analytics for virtually an unlimited number of scenarios at the individual transaction, sub-portfolio, and/or portfolio levels.