An important modelling decision faced by risk management is the identification of scenarios with probabilities attached, leading to selection of plausible scenarios to support forward looking risk analysis and stress testing. For example, the computation of Expected Credit Losses (ECL) under IFRS 9 requires an accommodation of suitably devised long-range scenarios comprising baseline, upside and downside. A sufficiently long history of data is used to predict the macro variables to a future 3-year horizon. The response of one or several credit metrics (e.g. credit index) is mapped out from the scenarios. For instance, for IFRS9 purpose the forward default probabilities are constructed for each rating class. Finally, the credit portfolio metrics of interests are calculated under each scenario.
A common component to applications of Scenario Analysis in Financial Stress Testing is the set-up and use of a framework. The Stress Testing Framework provides the linkage between macroeconomic scenarios and risk metrics, with the purpose to project financial metrics under mulltiple economic scenarios.
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Scenario Narrative
Stress Testing is well established as a risk management tool across financial institutions. This holds particularly true in in relation to Bank Capital, where regulatory requirements demand that banks adhere to regular internal and external stress testing exercises. A common question of interest to Portfolio and Risk Managers when evaluating the uncertainty of extreme market events is the following:
“How sensitive is my portfolio to losses as a result of macro and financial shocks?”
Macro Stress Testing aids in exploring exactly this. i.e. how hypothetical future economic or environment shocks transmit to portfolio losses and income conditions. In a portfolio-driven approach the drivers of a portfolio are identified and plausible scenarios are devised to facilitate the stressing of these drivers. For example, if unemployment rate would be identified as the key driver of losses in a retail portfolio, the stress test had to reflect changes in the unemployment rate and further, to explain how change in unemployment rate transmit to credit factors and to credit losses. However, other requirements may be imposed on the design of scenarios, for example by incorporating suitable liquidity and funding shocks into the scenarios.
To begin a scenario design one has therefore to identify the risks that matter most first, that is the identification of financial risks that can impact negatively on the financial or solvency position of the financial institution. However, the stress scenario is not meant to forecast of economic conditions in the near or distant future.
Scenario Design
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Providing the shocks on key macro and financial variables have been identified, e.g. GDP, undemployment rate, house price index, these shocks are projected into the future, for example by using Vector Autoregression (VAR): Given a VAR model with coefficient matrix \(A\) has been estimated, forecasts can be generated by sampling from the distribution of the innovation vector \(\epsilon\). To exemplify by a given VAR(1) model
\[\boldsymbol{Y}_{T+1} = \boldsymbol{\mu} + \boldsymbol{A} \cdot \boldsymbol{Y}_{T} + \boldsymbol{\epsilon}\]
a forecast for the next period \(T+1\) is generated by drawing the innovation vector \(\boldsymbol{\epsilon}\) from the multivariate normal distribution \(N(0,\boldsymbol{\Sigma})\).
TIP: The Vector Autorregression (VAR) method is suitable for analysing macroeconomic and financial data with the aim to project the trend of each variable. Similar to historical scenarios, the VAR approach is implicitly accounting for relationships among variables. Precisely, any VAR model is consistent with the historical time series patterns underlying the data. For instance, the historical evolution of bond returns may have an impact on the historical evolution of stock returns and vice versa, and this interelationship is incorporated by specifying a VAR model. → READ MORE ...
Scenario translation ensures the projected shocks are mapped accordingly to losses and earnings, with credit losses quantified by means of changes in the risk parameter PD, LGD and EAD, which are conditioned on the current position of the state of the economy.
Market Risk Stress Testing
Despite the common standards in using Value at Risk (VaR) and Expected Shortfall (ES) metrics, significant residual risks remain due to extreme and random market moves in the risk drivers. Ideally, forward looking scenarios should be evaluated in Market Risk Stress Testing to identify plausible and extreme shocks. However, scenarios based on historical data are often preferred due to the traceability of evidence for identifying shocks.
After the design of suitable scenarios for the core assets a scenario expansion needs to be applied to capture the responses of the non-core assets to the shocks in the drivers. The modelling of the co-dependence between variables under stressed market conditions is essential too. Typically the scenarios provided in regulatory or internal stress tests specify the time path of some key variables only. To exemplify the case for scenario expansion, consider a scenario where GDP growth and inflation change of EU countries is available, but US macro and financial variables are left unspecified.