Ebook A Value At Risk Approach To Measuring Equity Trading Risk Exposure In Emerging Stock Markets
Trading of financial securities – stocks (equities), bonds (fixed income instruments), derivative products, and structured products, etc. has been on uninterrupted expansion in emerging economies. While these emerging-markets share some similarities in development patterns, it is often their individual differences that create unique expected return opportunities and embedded risks, which may be addressed through art and science risk management techniques. The role of trading risk management and its proper implementations are essential factors in the success of emerging-markets' financial trading activities.
After all, what is most needed is a better understanding of the trading risk management process. This can be accomplished by striking a number of institutional changes that will help reduce the uncertainties in the trading of securities. In the rapidly changing and increasingly integrated financial markets, better management and closer supervision of the trading positions being taken (and their trading units) will better ward against hidden risks than formal regulations that focus on particular instruments, markets or participants. Naturally this has to be accompanied with clearer legal environment, risk management and accounting standards, in addition to greater disclosures of trading transactions. Accordingly, this will make users, dealers, and regulators better off and can improve their assessments of all kinds of risks they may encounter.
In the 1950s, Markowitz (1959) described the theoretical framework for modern portfolio theory and the creation of efficient portfolios. 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. The concepts of Value At Risk (VAR) and other advanced risk management techniques are in fact not new and are based – with some modifications on modern portfolio theory. Thanks to J. P. Morgan, RiskMetricsTM (1994) document, the concept of VAR and other modern risk management techniques and procedures are popularized. Since then the VAR concept is widespread and several specific applications are adapted to credit risk management and mutual funds investments.
For a comprehensive survey, and the different VAR analysis and techniques, one can refer to Jorion (2001). For the most part, VAR analyses in the public domain have been limited to comparing different modeling approaches and implementation procedures using illustrative portfolios (Pritsker, 1997). In their paper Berkowitz and O'Brien (2001) question how accurate VAR models are at commercial banks. Due to the fact that trading accounts at large commercial banks have considerably grown and become increasingly diverse and complex, the authors presented statistics on the trading revenues from such activities and on the associated VAR forecasts internally estimated by banks.
According to Culp, Mensink, and Neves (1998), VAR can be adapted for the use in asset management and for the estimation of market risk in the long term horizon. In their study, they explore the application of VAR to asset management and with particular attention on the importance of VAR for multicurrency asset managers. In another relevant study, Dowd, Blake, and Cairns (2004) tackle the problem of the estimation of VAR for long-term horizon. In their paper they offer a different; however a rather straightforward, approach that avoids the inherited problems associated with the square-root of time rule, as well as those associated with attempting to extrapolate day-to-day volatility forecasts over long horizons.
In his research papers, Al Janabi (2007a; 2007b; 2005) establishes a practical framework for the measurement, management and control of trading risk. The effects of illiquid assets, that are dominant characteristics of emerging markets, are also incorporated in his models. The established models and the general framework of risk calculations are mainly based on matrix-algebra techniques. Moreover, in his latest research paper, Al Janabi (2007a), the robust quantitative measurements and procedures of market risk are applied to emerging markets' equity trading portfolios that are combined with foreign exchange trading portfolios. Market risk management models, which are implemented in his latest work, are applied to both the Mexican foreign exchange and stock markets.
Set against this background, the objective of this paper is to define key equity trading risk management methods, rules and procedures that financial entities, regulators and policymakers should consider in setting-up their daily equity trading risk management objectives and then to adapt it to the specific needs of emerging-markets. The suggested analytical/quantitative methods and procedures can be implemented in almost all-emerging economies, if they are adapted to correspond to each market's initial level of sophistication. In order to illustrate the proper use of VAR and stress testing (scenario analysis) methods, some practical reports of equity trading risk management are presented for the Casablanca Stock Exchange (CSE).
To this end, the parameters required for the construction of appropriate and simplified VAR and stress-testing methods are reviewed from previous work and adapted the specific applications of these methods to emerging-markets. The theoretical mathematical/analytical models that are applied herein are based on matrix-algebra approach. The latter approach can in fact simplify the programming process in EXCELTM worksheets and can also permit easy incorporation of short selling of assets in the equity trading process. Moreover, a simplified approach for the incorporation of illiquid asset, in daily trading risk management practices, is defined and is appropriately integrated into the VAR and stress-testing models.
Trading risk management models, which are developed in this work, are applied to the CSE. Firstly, several tests of abnormal (asymmetric) distributions of returns are performed. To this end, various tests of skewness, kurtosis and Jarque-Bera statistics are implemented. This is followed by a study of daily volatilities along with the calculations of betas of the sample stocks. Furthermore, various case studies are carried out with the objectives of calculating VAR numbers under various market scenarios. The different scenarios are performed first with distinct asset allocation percentages, second by studying the effects of liquidity of trading assets (unwinding period of assets) and finally by taking into account the possibilities of short selling in daily trading operations.
Our case studies demonstrate VAR numbers under normal market condition along with severe crisis condition (stressed or abnormal market conditions) and with different liquidation horizons. Moreover, all VAR numbers are calculated and presented (under normal and severe market conditions) with three different correlation factors (+1, 0 and exact (empirical) correlations between the various risk factors). Under correlation +1, one is assuming 100% positive relationships between all risk factors (risk positions) all the times, whereas for the zero-correlation case, there is no relationships between all positions at all the times. The last correlation case takes into account the empirical correlations between all positions and is calculated via variance/covariance matrix.
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