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Risk-Management Methods

Risk-Management Processes To discuss risk-management methods of investment banks, their risk-management processes must first be understood. Figure 8.18 is a graphical representation of the risk-management process. The application of risk-management methods is mainly focused on the two areas of risk identification and risk assessment. The next step is to review major risk-management methods adopted by investment banks in mature international markets.

Major Risk-Management Techniques and Methods Theoretically, the proposal of the asset management theory, the arbitrage pricing theory,

Morgan Stanley Group Risk-Management Organizational Architecture Diagram

FIGURE 8.17 Morgan Stanley Group Risk-Management Organizational Architecture Diagram

and the option pricing model together laid the foundation for investment bank risk-management methods in their modern sense. In practice, several world-shocking financial crises in the 1990s (such as the Barings Bank incident and the Daiwa Bank incident) caused some large international banks to study and establish their own internal risk-management measurement and capital allocation models. As the worldwide demand by financial institutions for financial risk management and relevant techniques and software increases, a large number of counseling companies and software companies specializing in risk-management services have surfaced. Their presence drives the continuous update and development of risk-management techniques, methods, and software. Following are today's major risk-management methods, techniques, and software, categorized by type of risk:

- Market risks: The change of interest rate directly affects the value of fixed income securities and loan portfolios. Therefore, interest rate risks are the most important market risk facing investment banks.

Investment Bank Risk-Management Process

FIGURE 8.18 Investment Bank Risk-Management Process

As a result, the development of interest rate risk-management methods is most notable. The major methods include gap analysis, duration analysis, and convexity analysis. The key behind gap analysis is the measurement of sensitivity to the asset/debt ratio. The duration and convexity analyses evaluate interest rate risks of bond securities. Duration analysis reflects bonds' sensitivity to interest rate changes. Convexity analysis reflects the sensitivity of the duration of a bond to changes in interest rates. The best-known specific measurement model is the risk metrics developed by J.P. Morgan to measure the value at risk (VaR) or market risk of its assets. Other methods developed to allocate capital according to risks include the risk adjust return on capital (RAROC).

Credit risks: The prevailing risk-management methods are those that make decisions based on a comprehensive analysis of the client or counterparty's ability to perform its contractual duty and the probability of default. They mainly include the expert method, the credit rating method, and the credit scoring method. Major measuring models include the risk metrics developed by J.P. Morgan, the credit risk method developed by CSFB, the KTV model developed by the KTV company, and the McKinley model developed by the McKinley company. Comprehensive risk management: At the methodology level, this mainly includes statistical analyses and scenario analyses. Statistical analysis examines factors affecting the income of portfolios and evaluates the correlation coefficients between them through a series of metric techniques. These parameters reflect the extents to which the asset income reacts to different risk factors. This also provides technical specifications that may hedge against certain types of risks. Scenario analyses require risk managers to envision possible states of an investment portfolio and examine potential losses in extreme situations. These steps are repeated for all relevant variables to reach a final conclusion. The following three models are well recognized in the industry:

1. The risk monitor model developed by AXIOM Software Company integrates variance and covariance analyses. This Monte-Carlo simulation, historical simulation, and multiple factor analysis provide a sustainable and consistent measuring method for all markets, business lines, and financial instruments.

2. The risk watch model developed by Algorithmics Company is based on market pricing. It further gave rise to the mark to future mode for the pricing of asset portfolios. As a spin-off from scenario analyses, this model puts the asset portfolio in a scenario at one or more points in time in the future and determines the price of the asset portfolio based on the impact of risk factors on the portfolio.

3. The risk book model developed by Askari Company integrates various risk analyses into a consistent analysis structure, providing multiple risk perspectives through a consistent analysis system. - Financial engineering methods: In addition to the risk-management methods mentioned previously, modern financial engineering methods are also worth noting. This generation of methods was facilitated by the transformation to the market-based interest rate system in the 1970s and the subsequent rapid development of derivative financial instruments. Financial engineering methods involve the design, development, and implementation of novel financial instruments and financial means, as well as innovative solutions to financial problems. Typical methods include the VaR Delta model developed by Financial Engineering Associates, Inc. The model is able to determine how a new transaction will affect the VaR of the entire asset portfolio without recalculating the VaR of the investment bank. The model also provides VaR composition analyses, as well as client data not included in risk metrics. Another typical model is the risk IQ model developed by IQ Financial Systems. This is an integrated analysis model for market risks, credit risks, and liquidity risks. In particular, the model can be used to measure the overall risks of investment banks.

Intrinsic Flaws of Risk-Management Models Our previous discussions and analyses have shown that risk management in investment banks in mature international markets has entered a stage of comprehensive management. Accordingly, risk-management techniques have also evolved from qualitative analysis into current quantitative analysis, based on a large number of data models. Quantitative techniques and mathematical statistics models were once considered a major breakthrough in risk management. They improved the precision of risk measurement in investment banks to the extent that measurement is no longer a general interval or ambiguous subjective judgments. However, every model is based on certain hypotheses. Any discrepancy between the hypothesis and reality will compromise the effectiveness of conclusions from the model. Simulation techniques for future securities prices also need further improvement. Currently, neither historical data nor data based on certain hypothetic models can be relied on and expected to stand theoretical and factual tests, which gives rise to the so-called model risks. Therefore, if an investment bank relies too heavily on quantitative models for risk management while ignoring relevant qualitative research, accumulated hidden model risks may, over time, bring on a fatal blow to the bank. Risk-management models have intrinsic flaws. Their calculated results are effective as a reference for investment decision making only within certain bounds, and they should not be used as the final basis for any investment decision.

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