This chapter examines a practical methodology for the assessment and control of market and liquidity risk exposures for financial trading portfolios that consist of certain equity assets. The applied technique is based on the contemporary concept of liquidity-adjusted value at risk (LVaR) along with the application of optimization risk-engine algorithms. This chapter proposes a broad market and liquidity risk management model that can concurrently perform LVaR estimation under regular and stressed market scenarios. It takes into account the effects of illiquidity of traded equity assets. In order to demonstrate the appropriate application of LVaR and stress-testing techniques, real-world case analysis of trading risk management are presented for the Gulf Cooperation Council (GCC) stock markets. To this end, a number of optimization case studies are examined with the aim of developing a novel technique of trading risk measurement as well as the implementation of a risk optimization process for the computation of the maximum permitted LVaR limits.
TopIntroduction And Literature Review
To quantify the risks involved in their trading operations, major financial institutions are increasingly exploiting Value at Risk (VaR) models. Since financial institutions differ in their individual characteristics, a tailor-made internal risk models are more appropriate. Fortunately, and in accordance with the latest Basel capital accord, financial institutions are permitted to develop their own internal risk models for the purposes of providing for adequate risk measures. Furthermore, internal risk models can be used in the determination of economic capital that banks must hold to endorse their trading of securities.
The advantage of such an approach is that it takes into account the relationship between various asset types and can accurately assess the overall risk, including liquidity risk and firm-specific factors, for a whole combination of trading assets. As such, the notion of an internal risk model is the fact that the regulators allow financial institutions to develop their own risk models and use their own parameters instead of using models and parameters mandated by the regulatory bodies. The key benefits of internal risk models are convenience for the financial institutions, the ability of the financial entities to account for firm-specific factors, and lower regulatory costs.
Risk management has become of paramount importance in the financial industry and a major endeavor by academics, practitioners, and regulators, and a cornerstone of recent interest is a class of models called Value at Risk (VaR) techniques. The concepts of VaR and other advanced risk management techniques are not new and are based—with some modifications—on modern portfolio theory. Albeit VaR is one of many—both quantitative and qualitative—factors that should be integrated into a cohesive risk management approach, it is remarkably a vital one. In fact, VaR is not the maximum loss that will occur, but rather a loss level threshold that will be pierced some percentage of the time. The actual loss that occurs could be much higher than the VaR estimates. As such, VaR should be used in conjunction with other risk measures such as stress-testing, scenario analysis, and other asset/business specific risk measures. The most common VaR models estimate variance/covariance matrices of asset returns using historical time series, under the assumption that asset returns are normally distributed and that portfolio risk is a function of the risk of each asset and the correlation factors among the returns of all trading assets within the portfolio. The VaR is then calculated from the standard deviation of the portfolio, given the appropriate investment/liquidation horizon, and the specified confidence interval.
In the 1950s Markowitz (1959) described the theoretical framework for modern portfolio theory and the creation of efficient portfolios. Markowitz’s mean-variance portfolio optimization methodology is a landmark in the development of modern portfolio theory. The solution to the Markowitz theoretical models revolves around the portfolio weights, or the percentage of asset allocated to be invested in each instrument. Sharpe (1963), developed the single-index model, which relates returns on each security to the returns on a common index—abroad market index of common stock returns such as S&P 500 is generally used for this purpose.
Despite many criticisms and limitations of the VaR method, it has proven to be a very useful measure of market risk, and is widely used in financial and non-financial markets. The RiskMetricsTM techniques, developed and popularized by J. P. Morgan (Morgan Guaranty Trust Company, 1994), has provided a tremendous impetus to the growth in the use of VaR concept and other modern risk management techniques and procedures. Since then the VaR concept is well-known and scores of specific applications are adapted to credit risk management and mutual funds investments. The general recognition and use of large scale VaR models has initiated a considerable literature including statistical descriptions of VaR and assessments of different modeling techniques. For a comprehensive survey, and the different VaR analysis and techniques, one can refer to Jorion (2001).