Since the 1970s, world business transactions have experienced and contributed to diverse sources of financial uncertainty or risk (see, Dowd, 2005; Holton, 2003; Jorion, 2006; Tardivo, 2002a). Financial risk or risk created through financial transactions can be associated with value reduction. This reduction is due to market factors disequilibrium such as equity prices devaluation, interest or exchange rate fluctuations. Lately, in the competitive business environment, it has been discovered that firms have to face several financial risks namely market, credit, liquidity, operational and legal risks. The uncertainty scenario undoubtedly has had an impact on the volatility level of the financial market, thus influencing the return of an investment. Reflected in various dimensions such as the stock market, exchange rate, interest rate and commodity market, a volatile environment exposes firms to greater financial risk levels. Volatility that creates new dimension of business and systematic risk then forces firms to amend congruently their operational structure to accommodate changes in the environment. These conditions motivate firms to find new and better ways to manage risk, specifically in the case reported by Dowd (1999a) where investors were exposed to multiple problems of market risk. Although risk cannot be totally eliminated, Fong and Vasicek (1997) stress that its effect, particularly on investment losses, can be minimized thoroughly when one understands and manages it according to an effective risk measurement methodology. Ironically, the tremendous evolution in risk management practices coupled with innovation of financial engineering instruments have several distinctive effects, depending on the nature of business (Basle Committee, 1994; Dowd, 2005; Fong & Vasicek, 1997; Gastineau, 1993; Holton, 2003; Ibrahim, 1994). As indicated by several observers such as Brooks and Persand (2002) and Rahl and Lee (2000), viewing different kinds of business and investment portfolios based on an effective risk measurement tool is crucial in order to maximize returns and minimize risk. Thus by combining fundamental and analytical techniques to create new risk evaluation approaches, the process will be in a much better form to prevent larger financial losses. JP Morgan (1996) highlighted that the absence of a common point of reference for market risks makes it difficult to compare different approaches towards the measurement of market risks. The growing need for better empirical investigations to evaluate alternative measures of risk, says Brachinger (2002), realistically depends on whether the objective is to forecast choices under uncertainty or to provide superior predictors of introspective judgements about the risks being perceived. Along with the urgent needs of financial institutions to devise suitable mechanisms of risk management, the quantification of risk may avoid inappropriate policy decisions which can affect stakeholders. As noted by Nath and Reddy (2003), should the underlying risk not be properly estimated, it will lead firms to a lower profit level and jeopardize the financial stability condition, since less optimum capital is allocated throughout the organization. The theory and practice of risk management have developed extensively since the pioneering study of Markowitz (1952) who presented portfolio risk as the dispersion of standard deviation around the average return or mean. This modern investment theory, as portrayed in the well-known “Portfolio Selection: Efficient Diversification of Investment”, assists market users to incorporate results of wealth distribution according to an asset’s class and the best investment position. Following Markowitz (1952), risk of a security was later portrayed by Sharpe (1963, 1964) as its covariance with respect to the general market index or Beta. From there, the measurement and evaluation have since evolved to handle a portfolio’s market risk. Although the trade-off between risk and return is well recognized (higher returns can only be obtained at the expense of higher risk), JP Morgan (1996) points out that the risk measurement component of the analysis has not received broad attention. Within the same perspective, JP Morgan (1996) also reports that the exclusive attention on the role of return, however, has led to incomplete performance analysis. They conclude that the return measurement gives no indication of the cost in terms of risk. By the early 1990s, Value-at-Risk (henceforth VaR) had gained immense popularity and had become an integral risk management tool and a standard to monitor and control a firm’s risk exposures. By definition, VaR summarizes the worst expected loss that an institution could suffer over a target horizon under normal market conditions at a given confidence level (Butler, 1999; Dowd, 2005; Jorion, 1997, 2006). Cassidy and Gizycki (1997) conversely termed VaR as the earnings-at-risk or a potential loss amount. The main reason underlying its popularity lies in its simplicity of providing a single statistical figure summary of possible potential losses within a given time horizon. Since the introduction of the simplest VaR models, a range of approaches to calculate VaR has expanded from two important perspectives; number and complexity. Without doubt it is likely that VaR will become even more widely adopted over time (considering the views of academia and practical interest), since it is thought that such an approach may signal inefficiency in capital charges. Although Dowd (1998) reports that VaR enables firms to get a better sense of the overall risk and serves as a determinant of capital adequacy, the VaR methodology is not without criticism, one of which is that VaR may underestimate risk over simplified assumptions of normally distributed returns and constant variance. This is the case when ample empirical evidence has shown that rates of return distributions of financial time series exhibit fat tails, skewed to the left or peaked around the mode (Bali & Gokcan, 2003; Glasserman, Heidelberger, & Shahabuddin, 2000b; Zangari, 1996) and that volatility is time-dependent or better known as volatility clustering (Mandelbrot, 1963). Triggered by these circumstances, several related non-normal issues and consequences have been identified. From a regional perspective, an earlier survey by Murphy and Quinn (1993) observed that risk management practices by most Asian companies are underutilized. Both authors reported many companies either do not have any authorized risk management department/unit/staff or they lacked of precautionary actions to face instability in the business environment. Such act will definitely increase a firm’s risk and should be an encouragement to market users to pay extra attention to financial risk management aspect. In particular, since the 1980s the Malaysian economy has experienced several phases of growth and recession situations. Until June 1997, Malaysia grew prosperously with Gross National Product increasing at an average value of eight percent each year for eight consecutive years. But it was not long after mid 1997 that South East Asian countries were hit by currency crises which were led by misallocation of funds and overinvestment of capital (Dean, 1998, 2000). With extensive capital control, inclusive of pegging the Malaysian Ringgit to the US Dollar, Malaysia witnessed a better environment compared to its neighbouring countries, starting from the year 2000. As a result of a competitive market leading to higher risk exposure, professionals have turned their attention to search for a tool to measure VaR bounds with a specific distribution assumption without simultaneously over or underestimating it. Some of the current issues in VaR measures which motivate this research are explained in the following section.
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