Financial Risk Management - Literature review Example

A Report of Financial Risk Management Managing financial risk is an important aspect of any financial institution. Financial disasters have proved that millions of dollars can be lost through poor financial management and supervision of financial risks. A financial manager is not only interested in the returns from the investments in a market but also the possible extreme and abnormal returns that seem possible. Without a careful analysis of the potential danger, the investment could cause catastrophical consequence when a shock occurs. With the experience of recent failure of large financial institutions such as the Barings Bank, sufficient risks control measures are clearly essential and the regulators have started to set restrictions on limiting the exposure to market risks. Value at Risk context says that precise prediction of the probability of an extreme movement in the value of a portfolio is essential for both risk management and regulatory purposes. Value at risk has so far been the most popular in determining financial risk in financial institutions and most risk managers feel that it could have prevented financial disasters like Barings, Orange County and Sumitomo. The Value at risk was developed in response to financial disasters of the 1990s and obtained an increasingly important role in market risk management. Value at risk is a statistical measure of potential loss from an unlikely or adverse event in a normal market environment According to (Gencay. R,) "The Value at risk summarizes the worst loss over a target horizon with a given level of confidence. It is a popular approach because it provides a single quantity that summarizes the overall market risk faced by an institution or an individual investor". To be presice VaR is the maximum expected loss over a given horizon period at a given level of confidence. Investors are mostly concerned on how much money they lose, without having a clear idea on the maximum value they can lose, investors cannot determine if they are receiving the right benefits for the risk they are undertaking. Determining the value at risk. There are many methods to calculate VaR, which fit different market conditions, data set and precision requirements. There are some of the more popular and effective ones. Generally, we can classify them into three types Variance Co-variance Approach: This method is based on the assumption that the short-term changes in the market parameters and in the value of the portfolio are normal. In this method the market parameters are nondependent and are restricted to the first degree of dependence -correlation. Yiu. K.F.C (2004) says that this is was pioneered by Markowitz 1952] and is basically a single-period model which makes an one-off decision at the beginning of the period and holds on until the end of the period. Benninga and Wiener (1998) say that this method is the fastest but however it relies heavily on several assumptions about the distribution of Market beta and linear approximation of the portfolio. There is bound to be high convexity in the case of options and bonds. Gencay and Selcuk (2004) argue that "Although sample variance as an estimator of the standard deviation in variance-covariance approach is simple, it has drawbacks at high quantiles of a fat-tailed empirical distribution. The quantile estimates of the variance-covariance method for the right tail (left tail) are biased downwards (upwards) for high quantiles of a fat-tailed empirical distribution. Therefore, the risk is underestimated with this approach. Another drawback of this method is that it is not appropriate for asymmetric distributions. Despite these drawbacks, this approach is commonly used for calculating the VaR from holding a certain portfolio, since the VaR is additive when it is based on sample variance under the normality assumption". Historical simulation: This method consists of going back in time and applying current weights to a time series of historical asset returns. These returns reconstruct the history of a hypothetical portfolio using the current position of the portfolio. In this method, the past variations in the distribution of changes in prices and rates. These past data are applied to the current set of rates, and then uses those to revalue the portfolio. This set of portfolio revaluations corresponding to the set of possible realizations of rates and the 99th percentile loss is narrowed down as the value at risk. Benninga and Wiener (1998) "The historical simulation is important when the amount of data is minimum and when the information about the profit and loss distribution is limited. But its main advantage is that it catches all recent market crashes". Gencay and Selcuk (2004) Argues that" The disadvantage of this method is that the high quantile estimates are not reliable since they are calculated from only a few observations. Furthermore, it is not possible to obtain any quantile estimates above the highest observed quantile. Monte Carlo simulation: This is a simulation technique in which some assumptions are made about the distribution of changes in the market prices and rates by assuming they are normally distributed. The parameters of the distribution are estimated and then these assumptions are used to give successive sets of possible future realizations of changes. The portfolio is revalued for each set of realizations. The 99th percentile loss is determined as the value at risk from these portfolio revaluations. According to Benninga and Wiener (1998) the Monte Carlo simulation is the most powerful method and is flexible enough to incorporate private information with historical observations. Discussion. Value at risk has so far been the bets tool in determining and monitoring risk. It has been seconded by various analysts. Despite all this there have been various articles on the limitations of the value at risk tool in a few aspects. Benati (2004) argues that "In spite of its success, VaR lacks some important properties. First of all, it does not measure how ugly a loss can be, because it does not depend on the shape of the tail. Secondly, it is not a sub additive measure, therefore it prevents diversification. These drawbacks are partially overcome by some new measures that rely on the expected value of the distribution tail e.g. by conditional VaR (CVaR), expected shortfall (ES), tail conditional expectation (TCE) and so on". A risk measure that satisfies the axioms of measurability, translation invariance, subadditivity, positive homogeneity, monotonicity and relevance is called coherent. She says that the alternative to this limitation of the VaR is the theory of Coherent risk measures. In recent years, many risk measures related to probability distribution function tails have been proposed, such as VaR, ES, and so on. It was soon shown that a quantile measure like VaR lacks some theoretical properties that are required by an operational risk measure. This observation led to the axiomatic definition of coherent risk measures. Benati (2004) says that "The approach of the authors begins by transforming the operational properties, required for a correct and solid risk measure, into a set of mathematical axioms. Then, risk measures that satisfy all these axioms are called coherent. The general contribution is that coherent risk measures are always representable as the worst conditional expectation (WCE) that is the infimum of a mean with respect to a set of probability distributions, called scenarios. As operational consequence, conditional measures such as conditional VaR or WCE are coherent". All these measures are not difficult to calculate if the probability distribution function is a continuously increasing one. Tan and Chang (2003) argue that "Risk managers are concerned with the accuracy of normal VaR when analyzing portfolios with fat-tailed distributions. This is because fat tails imply that extreme losses will occur far more frequently than the normal distribution would predict. This can be particularly worrying for stress testing using the Stress VaR approach which seeks to measure potential extraordinary losses under the normal VaR framework. This suggests that the normality assumption might still be appropriate in performing stress tests". Fan (2004)conclude that" The study of this paper shows that VaR methodology can be applied to risk management in stock market in China, which is an efficient tool to measure market risk. Promising opportunities exist for the application of this methodology in the area of risk management of China besides stock market". This paper applied the VaR methodology, to the stock market in China. From the comparison between the predicted VaR and real return, the calculated results are mostly satisfied with the confidence level at 95%. The losses have remained in the determined value at risk. Fan (2004) conducted their studies on the shanghai and shenzan indexes for a period length of 100 days. The fluctuation of the Chinese stock market is very high and the VaR was calculated for 475 days. Out of theses 475 there has only been 5% negative return over the determined VaR. This gives a confidence level of 95% as the negative outcomes over the calculated VaR come to only 5%. They conclude from their studies that the comparison between the predicted VaR and real return, the calculated results giving a confidence level of 95% as satisfying and confirm that VaR is a fairly accurate method of risk measurement. Real return and VaR in Shanghai The figures taken from Fan.Y et all (2004) shows the difference in real returns and VaR returns on a daily basis from the two markets in Shanghai and Shenzhen. Real return and VaR in Shenzhen Zmeskal (2003) conclude from their studies that" The VAR methodology does not provide a measure of worst absolute loss, only some confidence level. The VAR also assumes that the portfolio position is fixed over the horizon (frozen position). This assumption, however, ignores the possibility of changing the trading positions over time in response to changing market conditions (changing position risk).The study was conducted using the historical simulation method in estimating VaR.The VAR models are based on historical data and it is assumed that the recent past is a good projection of future randomness. Some situations where the historical patterns change abruptly will cause havoc with the models. Changing correlation coefficients can lead to drastically different measures of portfolio risk (event and stability risk)". This confirms that VaR has limitations as in decision making in portfolio management because it does not support the idea of diversification. Szego (2002) argues that" VaR in general turns out to be not even weakly coherent and in particular not sub additive. To try to measure risk without this property is like measuring the distance between two points using a rubber band instead of a ruler". He also argues that VaR is an incorrect method of measuring financial risk that it does not measure losses that exceed a certain limit, it provides conflicting results at different confidence levels and most of all its non sub additivity property which does not support diversification. Szego (2002) argues about better risk evaluating measures that are better during certain market conditions are Conditional value at risk (CVaR),Expected shortfall (ES), tail conditional expectation (TCE), worst conditional expectation (WCE) and spectral risk measures. VaR and CVaR. The above figure shows the value of risk that VaR cannot estimate which CVaR has determined. These studies show that there are certain limitations to VaR in determining the amount of money that can be lost during extreme market conditions. VaR is the most popular method of determining risk. Many analysts say that this method if invented earlier could have prevented major financial disastrous like the Barings bank, Enron and various others. Despite the effectiveness of VaR it seems to have some flaws in the exact determination of risk especially during extreme and abnormal market conditions. When the tail is fat and the probable outcomes are high in number VaR seems to have a flaw in determining the exact value at risk. VaR also has other faults in its property like being non sub additive which goes against diversification which is an efficient risk reduction tool. Many analysts recommend other measures to counter this problem. G.Szego (2002) says that if VaR is used to measure risk in most of financial situations, leads to disastrous results, the problem of risk measure can be easily solved by using CVaR or ER and portfolio problems in the mean CVaR plane can be solved by linear programming methods. CVaR is nothing other than a weighted average of VaR. VaR oversees a percentage of the risk which can be determined by CVaR. VaR has also been proved to be in coherent in many papers. And again CVaR proves to be the substitute for this flaw in VaR. there are other measures that can be used along withVaR like the Expected shortfall (ES), tail conditional expectation (TCE), worst conditional expectation (WCE) and spectral risk measures. This has been proved by G.Szego (2002). Overall an accurate measure cannot be recommended that can determine risk in any turn of the market. These measures individually do not perform very well but when combined with VaR can give highly accurate information. All the measures have some drawbacks in them. So far VaR id the most effective one but has its own problems. Research and studies are going on about a better risk evaluating measure. (Wordcount-2189). Net present value is a way of comparing the value of money now with the value of money in the future. A euro today is worth more than a euro in the future, because inflation erodes the buying power of the future money, while money available today can be invested and so grow. The technique is a three-stage process: "to calculate the present value of each element of cash expenditure in a proposal and then, to add these individual present values together to provide a total present value of the expenditures; to similarly calculate the present value of each element of cash income in a proposal and, then, to add these individual present values together to provide a total present value of the incomes; to deduct the total present value of expenditures from the total present value of the incomes, in order to determine the net present value"; Tinic, S. M., and West, R. R. (1986) If this calculation produces an NPV that is positive, the signal is to accept the proposal. If, however the NVP is negative, the signal is to reject the proposal Advantages of NPV There are two major advantages of NPV as a capital expenditure appraisal technique it accurately recognises the "time value of money" for all expenditures or receipts - irrespective of the exact time at which they are made or received it enables alternative proposals to be ranked in order of attractiveness It recognises the "time value of money" by converting future expenditures and receipts to their corresponding present value on investment criteria, taking account of the exact date on which they are expected to be made or received Alternative proposals can be ranked in order of attractiveness. This is important when considering either "mutually exclusive" proposals or "capital rationing" Disadvantages of NPV There are two major disadvantages of NPV as a method of appraising capital expenditure proposals: the net present value requires the organisation to calculate an interest rate to use for appraising capital investment proposals the net present value calculation is only valid for the interest rate that has been used The payback period is the most widely used technique and is literally the amount of time required for the cash inflows from a capital investment project to equal the cash outflows. The usual way that firms deal with deciding between two or more competing projects is to accept the project that has the shortest payback period. Payback is often used as an initial screening method. Firstly, it is popular because of its simplicity. Research over the years has shown that firms favour it and perhaps this is understandable given how easy it is to calculate. Secondly, in a business environment of rapid technological change, new plant and machinery may need to be replaced sooner than in the past, so a quick payback on investment is essential. Thirdly, the investment climate demands that investors are rewarded with fast returns. Many profitable opportunities for long-term investment are overlooked because they involve a longer wait for revenues to flow. Arguments against payback. It lacks objectivity. Who decides the length of the optimal payback time? No one does - it is decided by pitting one investment opportunity against another. Cash flows are regarded as either pre-payback or post-payback, but the latter tend to be ignored. Payback takes no account of the effect on business profitability. Its sole concern is cash flow. Relevance of Capital Market Theory. The old theory of CAPM makes the assumption that the CAPM line represents a long term model of assets fluctuations and risks versus returns. However, it is shown that the historical data does not take into consideration influential changes in the economy such as new technology. Therefore, from year to year, data can considerably change. The new CAPM theory has evolved to make different assumptions. First, CAPM states that investors have homogenous expectations and investments horizons. Secondly, it does not take into consideration transactions and information costs (as we know however, transactions and most importantly information costs can be very expensive even though some information costs can be included in the transaction costs if done through traders.). Furthermore, the CAPM model assumes that all investors have access to the same amount of information which is not true considering that they either don't have the time or the will to go into a deep research as the optimal portfolio theory would suggest in order to maximize profit. Although transactions and information costs along with equal access to information are important considerations, they can be eliminated for the model because they do not represent extremely changing variables for pricing an asset. David Nawrocki sates that the most important variable to include in the CAPM is the investors' expectations and investment horizons. This makes sense because as different individuals we have different needs and risks tolerance that can greatly affect the way we view an asset and the way we are going to create our portfolio. The author explains in great details why this assumption is wrong in the CAPM model. First, as stated earlier, different individuals will react differently to an asset. Therefore wealthier investors will be less risk averse that the one who does not have the same amount of money and can't afford great losses. Secondly, we have different motivations to invest. Some people will want to invest in order to have liquidity fast or other will want to keep their investment longer just to make their money "work". Therefore, the time horizons of investors will vary. Thirdly, we all have different goals, long term investors might want to save for future needs such as retirement and be less sensible to a change in the market while short term investors might want liquidity for the purpose of speculation or short term upcoming expenses therefore being very responsive to a change in the market. Fourthly, many investors are individuals and do not have the time, the knowledge or the will to go into exhaustive searches for their investments' optimal solutions. Only professionals or people with passion can go through long searches about the market therefore having a better performing portfolio. For the preceding reasons and for other costs linked to information's researches people will not have access to perfect information (although the market is considered to give perfect information if efficient). Because of so many varying needs and horizons, investors will not view the market and its pricing in the same way making the use of CAPM irrelevant for them. Vega's coherent market theory takes into consideration the time variable of market stating that markets are always changing so should the CAPM. A random pricing model will have a normal distribution over time which means that it can be used as an historical data. However, because of every day's changes and innovations, markets go through multiple cycles giving it non-normal distribution for which statistics can't predict the future. Vega's theory joins Nawrocki comments about the importance of individual decisions. Individual investors can reduce information search and find a coherent model by analyzing business cycles. There are 4 business cycles: Easoff in which the economy has reached its point of maximum growth and slows down. Interest rates are high because the FED is trying to reduce inflation. Plunge is a slow down in which the economy is declining along with interest rate. Revival is the recovery stage from its recession or slow down. Finally, Accelerate is the starting point again in which the economy continues its strong growth. Vega gives different strategies for the different business cycles but leaves to the individual the choice to invest in risk free assets or overseas assets when the economy booms. Vega's model is more complex than the CAPM and tries to avoid the different defects of the CAPM. Investors should look short term in the past for the model to work. Multifactor Models It is a common opinion that growth stocks are more valuable and will generate more returns than value stocks. Studies by Kenneth and French assume the opposite. Although value stocks are generally defined as low ratio to book value and market value while growth stocks have high ratio to book and market value, their studies proved the opposite. This makes sense to me because the growth of a company or an industry as seen with the dot com and many other fast growing industries forces the company to perform in a way that is sometimes not natural and will lead to a crash. For instance, the managers in order to keep investors' expectations about their company's growth might be led to influence their stocks prices or lie in their financial reports by fear to lose inflows of capital. As we can see with such examples as Enron, the outcome is dramatic on the whole economy and many changes have to be done in terms of policies later on. Therefore what seems to be priced as a high reward stocks by the market's information may turn to be a junk stock later on. Value stocks ask for more analytical skills and information research but is more conservative in that way that companies do not have an enormous pressure to perform therefore does not try to influence markets' decisions regarding its stocks. Some hidden gems can be in those stocks and many can be undervalued because disregarded by the public. This represents a great opportunity for profit. The CAPM does not reflect this view of stocks. Those findings have two implications for the investors: first, it is shown that by investing in value stocks the returns are much higher in many different countries around the world, providing investors with more options. Second, investors that base their judgments on the CAPM are mistaken and do not see the importance of value stocks because the CAPM does not take into consideration 2 variables of risks. Stock returns are commonly known today as being influenced by a one folded factor: the sensitivity of to the market return or beta. This is true but not complete. the CAPM needs to include a measure of risks between big and small stocks along with a measure of risks in value and growth stocks. This is important because once again different investors have different needs and exclude value stocks because the only available and easy data is CAPM therefore they are not interested by value stocks which may also explains their low prices while in fact the underlying company might have a growth potential. To examine stock returns, and make an informed decision you need those three measures of risks. Risks must be tied to earning to price, cash flow to price and dividend to price in all of the three dimensions stated above: small vs. big stocks, value vs. growth stocks and market returns sensitivity. Those risks analysis are necessary for investors to make informed decisions that are hidden by the CAPM. 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