Value-at-risk systems and their application in integrated risk management

Journal of the Academy of Business and Economics, March, 2004 by Hari P. Sharma, Dinesh K. Sharma, Julius A. Alade

ABSTRACT

Value-at-Risk (VAR) has become a standard benchmark for measuring financial risks. VAR systems and models identify a first-order magnitude of financial risks and provide a forward-looking measure of a portfolio's overall downside risk potential. The recent trend and motivation for using VAR systems are institutions' needs to integrate their financial risks such as market, credit and operations risks. VAR methodologies are evolving in finding ways of integrating diverse financial risks and will continue to advance worldwide standards. This study presents an overview of concept and quantitative techniques of VAR and how VAR systems evolve for managing and integrating financial risks.

1. INTRODUCTION

Financial and non-financial institutions are required to report value-at-risk (VAR), a risk measure for potential losses (financial risks) on a regular basis barring Congressional amendment of the new SEC Rule on Disclosures about Derivatives and Other Financial Instruments. Financial risks are those, which relate to possible losses in financial markets including losses from interest rate movements, defaults on financial obligations or operational inefficiencies. Risk managers must consciously plan for the consequences of adverse outcomes and, by so doing, are better prepared for inevitable uncertainties. Internal uses of VAR and other sophisticated risk measures are on the rise in many institutions and risk managers are expected to set VAR limits on amounts and probabilities, for trading operations and fund management (Berkelaar et al., 2002).

Early VAR estimates were linear multipliers of variance-covariance estimates of the risk factors. These types of market risk techniques soon became popular, mainly because of their link to modern portfolio theory. However, during worldwide market crises, users noticed that early models failed to provide good VAR estimates. The early VAR models were also referred to as parametric because of the strong theoretical assumptions they impose on underlying properties of the data (Barone-Adesi and Giannopoulos, 2001). VAR estimates are currently based on two main techniques: (1) the variance-covariance approach, and (2) simulation. VAR systems differ in computational methodology, computational time, and accuracy in nonlinear approximation. The Group of Thirty (1993) report on derivatives stated that "market risk is best measured as value-at-risk," because it provides a summary statistic of the order of magnitude of potential losses due to market risk (Jorion, 2002). The VAR revolution is the result of several factors, such as: (1) regulatory pressures for better control of financial risks, (2) globalization of financial markets, and (3) technological innovations in computational techniques. These factors made it possible to integrate and manage enterprise wide risk. VAR methodologies are expanding and finding ways of integrating diverse financial risks and will continue to evolve as worldwide standards for managing numerous types of financial risks.

The rest of this paper is organized as follows. Section 2, presents a brief review of literature. Section 3, describes mathematical foundation of VAR systems. Section 4 presents the various aspect of financial risk while section 5 discusses the integration of risks. The last section presents the conclusion.

2. LITERATURE REVIEW

Researchers have developed several quantitative techniques for managing financial risks. Markowitz (1952) used the standard deviation as an intuitive measure of dispersion with a major part of his study on explaining the tradeoff between expected return and risk in the mean-variance framework for normally distributed returns. Roy (1952) presented confidence-based risk measures and presented a "safety first" criterion for portfolio selection. He advocated choosing portfolios that minimize the probability of a loss greater than a disaster level. Sharpe's (1964) developed Capital Asset Pricing Model (CAPM). Baumol (1963) also proposed risk measurement criteria based on a lower confidence limit at a given probability level. Other developments include the Multiple Factor Model (1966), Black-Scholes Option Pricing Model (1973), Binomial Optional Model (1979), Risk Adjusted Return on Capital (1983), Limits on Exposure by Duration Bucket (1986), Risk-Weighted Assets for Banks Limits on "Greeks" (1988), and Stress Testing (1992).

Risk Management has truly experienced a revolution since the early 1990s. In November 1994, Orange County's investment pool lost $1.7 billion from the structured notes and leveraged repurchase agreements or "repos". Repos are contracts in which the seller of securities, such as Treasury Bills, agrees to buy them back at a specified time and price. In February 1995, Baring Plc lost $1.5 billion because a Singapore based trader, Nick Leeson, took unauthorized futures and options positions linked to the Nikkei 225 and Japanese government bonds. The common lesson of these disasters is that billions of dollars can be lost through poor supervision and management of financial risks. These disasters forced financial institutions and regulators to focus on VAR, a then new measure of financial market risk developed in response to these financial disasters. The VAR methodology was easy-to-understand and easy to apply in quantifying market risk. For instance, a bank might say that the daily VAR of its trading portfolio is $25 million at the 99% confidence level. In other words, there is only one chance in a hundred, under normal market conditions, for a loss greater than $25 million to occur. The recent developments include Risk Metrics (1994), Credit Metric and Credit Risk (1997), Integration of Credit and Market Risk (1998), and Enterprise wide Risk Management (2000). In January of 1997, the Securities and Exchange Commission (SEC) established rules for the quantitative and qualitative reporting of risks associated with highly market sensitive assets (i.e. derivatives positions) of reporting firms (Jorion, 2000).