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Operational risk refers to the risks attached to people, processes, technology or external events such as fraud. Brown, Goetzmann, Liang and Schwarz [2009] provide a detailed description of how to build a scoring model to detect operational risk in hedge funds.
Concentration risk is the risk arising from all investments in securities of a particular issuer, sector, country or other common factor. Concentration risk emerged in this recent crisis when securitized subprime assets were found to be present in many parts of investors’ portfolios without proper management of the aggregate exposure. Many cash management products, money market funds and other commonly used investment vehicles were found to be exposed to subprime assets.
The recent credit crisis has also reminded us of the importance of liquidity risk. Market liquidity may fall very quickly as risk aversion suddenly rises in a crisis situation, effectively shutting down certain segments of the market and increasing the cost of selling in most others. For investors, this raises the danger of being unable to meet cash-flow obligations. In 2008, the endowments that were heavily invested in illiquid asset classes, such as private equity and real estate, found themselves with a sharp liquidity mismatch and had to liquidate part of their holdings at distressed values.
Given the variety of problems, the dynamic nature of portfolios and the links between assets and asset classes, it is hard to imagine efficiently managing risk at the portfolio or enterprise level without a proper framework to measure, monitor and aggregate risk. Multi-asset class risk systems can help address this problem.
Fundamental Factors Driving Portfolio Risk And Return
While VaR and volatility act as barometers to provide an advance warning signal against heightened volatility, these measures are not useful in understanding the sources of risk. Looking at the fundamental factors driving each asset reveals the sources of rising risk. The individual impact of such factors can be derived using multiple-factor models, which not only measure and forecast risk for a portfolio, but also break down that risk according to the contribution from the various factors used in the model.
In the Barra risk models, for example, each stock receives an exposure to various factors such as country, sector or style (value, growth, leverage, liquidity, size, nonlinear size and momentum) that drive risk. These country and industry factors help to determine how much of a portfolio’s risk is specific to a particular country or industry after netting out other effects. For instance, a portfolio of Korean stocks is clearly subject to the country risk of Korea, but also reflects the risk of the technology sector that has a disproportionately high weight in that market. Using country and industry factors helps to disentangle these effects and identify “pure” exposures of a portfolio to country and industry risk. Similarly, a portfolio of bonds has different exposures to different changes in the term structure, whether it is a shift of the whole curve, a steepening or flattening of the curve or a change in its convexity.
At the portfolio level, these fundamental factors will be linked across different asset classes. For example, the value and size factors in equities will be linked to yield curve factors in fixed income, as well as other factors from commodities and other asset groups. Figure 8 illustrates how factors can be integrated into a multi-asset class correlation matrix.
With an integrated factor model, factors from the country level are in fact combined together to arrive at the global factors that apply to all markets. This greater granularity allows for different factors in different markets, thereby facilitating a finer analysis of exposures of various assets worldwide. By combining all factors from different asset groups, a portfolio manager can more effectively analyze portfolio exposure to multiple levels across asset classes.
In the recent crisis, many investors did not have sufficiently strong modeling capabilities to comprehensively cover the many derivatives in their portfolio, thus causing them to miscalculate the true risk of these instruments. It is now clear that there was an overreliance on credit ratings, which in some cases were based upon wrong or flawed models. Given the importance of models in risk measurement, modeling risk cannot be overlooked. The integrity of the models—and that of the modelers—should be assessed. Complex and untested instruments should be subject to additional prudent constraints.
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