21 Fundamentals of Financial Risk Management Illustrative Use of Copula Method

21 fundamentals of financial risk management

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Fundamentals of Financial Risk Management Illustrative Use of Copula Method for Aggregating Credit and Operational Losses for SifiMortgage 22
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Fundamentals of Financial Risk Management Stress Testing & Scenario Analysis Where analytic and simulation-based methods can be used to provide the risk analyst insight with a specified level of confidence an estimate of economic capital defined by VaR, other methods to estimate adverse or extreme scenarios have an important role in understanding the firm’s ability to withstand such events. Stress testing and scenario analysis rely more on historical experience to drive the analysis. Scenario analysis, of which stress testing could be considered a subset requires the analyst to first identify key outcomes of interest to study. It could be credit losses in the consumer portfolio, projections of firm or business unit net income, loan loss reserves or other outcomes tied to the financial performance of the company. 23
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Fundamentals of Financial Risk Management Stress Testing & Scenario Analysis Critical to the analysis is identifying a set of stress factors that directly or indirectly affect the outcome variables. These are usually developed empirically and are included in risk models along with borrower, loan and other risk attributes of interest. As a result, what may be important to one portfolio, business or risk type may be only an indirect effect to another. Consider for example the risk analysts at SifiMortgage. 24
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Fundamentals of Financial Risk Management Stress Testing & Scenario Analysis Having experienced the mortgage crisis of 2008-2009 and suffered massive credit losses, management has decided that it needs to conduct a periodic stress test of mortgage credit losses in its $100 billion loan portfolio. Credit losses in turn are defined by the product of the default rate and loss severity rate once a loan enters default. The risk team has estimated statistical models of default and severity using historical loan level data over the last 10 years using a number of borrower, loan, property and risk factors to explain default and loss severity under different time periods. In addition, the team has determined that house price appreciation rates (HPA) are a key driver determining whether a borrower will eventually default on their loan and what percentage of the loan will be received by SifiMortgage should a loan default. 25
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Fundamentals of Financial Risk Management Stress Testing & Scenario Analysis A generalized representation of how HPA affects both variables is shown in Figure 6-6. On average annual national HPA rates have historically been 2-3% on a nominal basis. This is shown by the vertical dotted line denoted Average. The corresponding default and severity rates for the average HPA can be observed from the figure and when combined illustrate the amount of credit losses expected to occur over time.
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