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This chapter highlights the conceptual aspects of a rating system without focusing on mathematical and technical aspects. An overview is provided of the different aspects of risk measurement and modelling: data, modelling techniques, and implementation for use. All aspects of the development and implementation of a new model are discussed. The system life cycle is explained in section 4.2. Section 4.3 provides a high-level overview on credit scoring models. Such models rely on data, for risk measurement, model use, and model development. The data issues are discussed in Section 4.4. Section 4.5 provides a bird's eye view on the model development process of internal rating systems. Implementation aspects are discussed in Section 4.6. Section 4.7 explains that models need to be maintained and updated regularly. Section 4.8 explains the different, but also partially overlapping aspects of model validation, quality control, and backtesting.

Risk measurement and management is an important and complex subject for hedge fund stakeholders, managers, and investors. Given that hedge funds dynamically trade a wide range of financial instruments, their returns show tail risk and nonlinear characteristics with respect to many financial markets that require advanced downside risk measures, such as value-at-risk, expected shortfall, and tail risk, to capture risk adequately. This chapter reviews the nature of these risks and presents the measurement tools needed, focusing on fixed-income instruments, derivative securities, and equity risk measurement, and stressing the importance of frequent assessment to capture the possibly rapidly changing risk profiles of hedge funds. This chapter also provides an overview of the linear factor models that investors often use to measure hedge fund risk exposures along many risk factors.

This chapter describes work at the Bank of England to develop a quantitative framework to guide macroprudential analysis and bank stress-testing work. The Risk Assessment Model for Systemic Institutions (RAMSI) explicitly characterizes bank balance sheets and allows for feedback effects associated with asset fire sales and network interactions. The model is calibrated to UK financial system data and the results present prototypical projections for system-wide banking assets. Despite the joint normality of the risk factors of the model, RAMSI generates a bimodal asset distribution, reflecting the possibility of second and higher round defaults associated with feedback effects of the kind described in earlier chapters. RAMSI is potentially a policy tool for risk assessment and macroprudential stress-testing exercises.

Interest rates are part of the fabric of finance, used for assessing rates of return on investments, determining costs of capital to firms, compounding and discounting cash flows, and as underlying variables in many derivative instruments. As interest rates change, so do values of associated securities, resulting in substantial risk to investors in these financial products. Interest rate risk measurement is often defined in terms of the sensitivity of prices to changes in interest rates. Duration is a measure used for small changes in rates, and convexity provides a correction to duration when the rate changes are larger. Forecasting how short- and long-term rates move based on macroeconomic factors becomes important for businesses in any country, as these rate changes affect borrowing costs and investment opportunities. Financial institutions carry out interest rate risk management using instruments such as interest rate swaps, or through more advanced approaches such as asset-liability management and gap analysis.

This chapter examines complex probability distributions whose shapes make them appropriate for characterizing insurance and other financial risks. In particular, it introduces two important families of distributions: the Pareto family and the symmetric Lévy-stable family, both of which are frequently used to model particularly “risky” random variables with heavy tails (i.e. with large amounts of weight spread over the more extreme values of the random variable). To describe the measurement of risk, the chapter begins by defining the statistical moments of a distribution. It then shows how these quantities are used to compute the expected value (mean), standard deviation, and other helpful parameters.