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Risk Management

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Risk Management

Risk Management—is an inescapable part of economic activity. People generally manage their affairs to be as happy and secure as their environment and resources will allow. But regardless of how carefully these affairs are managed, there is risk because the outcome, whether good or bad, is seldom predictable with complete certainty. There is risk inherent in nearly everything we do, but this reading will focus on economic and financial risk, particularly as it relates to investment management. All businesses and investors manage risk, whether consciously or not, in the choices they make. At its core, business and investing are about allocating resources and capital to chosen risks. In their decision process, within an environment of uncertainty, these organizations may take steps to avoid some risks, pursue the risks that provide the highest rewards, and measure and mitigate their exposure to these risks as necessary.

Risk management processes and tools make difficult business and financial problems easier to address in an uncertain world. Risk is not just a matter of fate; it is something that organizations can actively manage with their decisions, within a risk management framework. Risk is an integral part of the business or investment process. Even in the earliest models of modern portfolio theory, such as mean–variance portfolio optimization and the capital asset pricing model, investment return is linked directly to risk but requires that risk be managed optimally. Proper identification and measurement of risk, and keeping risks aligned with the goals of the enterprise, are key factors in managing businesses and investments. Good risk management results in a higher chance of a preferred outcome—more value for the company or portfolio or more utility for the individual.

  • Risk and risk management are critical to good business and investing. Risk management is not only about avoiding risk.
  • Taking risk is an active choice by boards and management, investment managers, and individuals. Risks must be understood and carefully chosen and managed.
  • Risk exposure is the extent to which an organization’s value may be affected through sensitivity to underlying risks.
  • Risk management is a process that defines risk tolerance and measures, monitors, and modifies risks to be in line with that tolerance.
  • A risk management framework is the infrastructure, processes, and analytics needed to support effective risk management; it includes risk governance, risk identification and measurement, risk infrastructure, risk policies and processes, risk mitigation and management, communication, and strategic risk analysis and integration.

Financial Risk Management



Financial risk management is a huge field with diverse and evolving components, as evidenced by both its historical development and current best practice One such component—probably the key component—is risk measurement, in particular the measurement of financial asset-return volatilities and correlations (henceforth “volatilities”). Crucially, asset-return volatilities are time varying, with persistent dynamics. This is true across assets, asset classes, time periods, and countries, as vividly brought to the fore during numerous crisis events, most recently and prominently the 2007–2008 financial crisis and its long-lasting aftermath. The field of financial econometrics devotes considerable attention to time-varying volatility and associated tools for its measurement, modeling, and forecasting. In this chapter we suggest practical applications of the new “volatility econometrics” to the measurement and management of market risk, stressing parsimonious models that are easily estimated. Our ultimate goal is to stimulate dialog between the academic and practitioner communities, advancing best-practice market risk measurement and management technologies by drawing upon the best of both.

Six key themes emerge, and we highlight them here. We treat some of them directly in explicitly focused sections, while we treat others indirectly, touching upon them in various places throughout the chapter, and from various angles. The first theme concerns aggregation level. We consider both portfolio-level (aggregated, “top-down”) and asset-level (disaggregated, “bottom-up”) modeling, emphasizing the related distinction between risk measurement and risk management. Risk measurement generally requires only a portfolio-level model, whereas risk management requires an asset-level model. The second theme concerns the frequency of data observations. We consider both low-frequency and high-frequency data, and the associated issue of parametric vs. non-parametric volatility measurement. We treat all cases, but we emphasize the appeal of volatility measurement using non-parametric methods used with high-frequency data, followed by modeling that is intentionally parametric. The third theme concerns modeling and monitoring entire time-varying conditional densities rather than just conditional volatilities.
We argue that a full conditional density perspective is necessary for thorough risk assessment, and that best-practice risk management should move—and indeed is moving—in that direction. We discuss methods for constructing full conditional density forecasts. The fourth theme concerns dimensionality reduction in multivariate “vast data” environments, a crucial issue in asset-level analysis. We devote considerable attention to frameworks that facilitate tractable modeling of the very high-dimensional covariance matrices of practical relevance. Shrinkage methods and factor structure (and their interface) feature prominently. The fifth theme concerns the links between market risk and macroeconomic fundamentals. Recent work is starting to uncover the links between asset-market volatility and macroeconomic fundamentals. We discuss those links, focusing in particular on links among equity return volatilities, real growth, and real growth volatilities. The sixth theme, the desirability of conditional as opposed to unconditional risk measurement, is so important that we dedicate the following subsection to an extended discussion of the topic. We argue throughout the chapter that, for most financial risk management purposes, the conditional perspective is distinctly more relevant for monitoring daily market risk.

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