Managing market risk with VaR (Value At Risk) (original) (raw)
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Market risk estimates the uncertainty of future earnings, due to the changes in market conditions. Value at Risk has become the standard measure that financial analysts use to quantify market risk. For estimating risk, the issue is that different ways to estimate volatility can lead to very different VaR calculations. The performance of SMA with rolling windows of 100 and EWMA using 0.94 (proposed by RiskMetrics) as smoothing constant λ and rolling window of 100 days, perhaps the most widely used methodology for measuring market risk is analyzed from investment activities on 7 stock exchange indices from developed and emerging markets. Binary Loss Function (BLF) is employed to measure the accuracy of VaR calculations because VaR models are useful only if they predict future risks accurately. The subject of this research is to determine the possibility of application of the SMA and EWMA models VaR with 95% and 99% confidence level in investment processes on the stock exchange markets of the selected countries. The methodology applied in the research includes analyses, synthesis and statistical/mathematical methods. The aim of the research is to show whether the models work the same and whether financial analysts from emerging countries can use the same model as their counterparts from the developed countries. The results show that risk managers in developing just as those in developed countries can use risk metric EWMA model as a tool for estimating market risk at 95% confidence level.
2018
Risk estimation or volatility estimation at financial markets, particularly stock exchange markets, is complex issue of great importance to theorists and practitioners. Models used to estimate volatility forecasts are translated into better pricing of stocks and better risk management. The aim of this research is to test applicability of simple models like Simple Moving Average (SMA) and Exponentially Weighted Moving Average (EWMA) to estimate risk. The performance of SMA and EWMA with rolling window of 100 using 0.94, 0.96, and 0.90 as smoothing constant were analyzed on investment activities of time series of 10 stocks comprising MBI-10. Binary Loss Function (BLF) is employed to measure accuracy of VaR calculations, because VaR models are useful only if they predict future risks accurately. Results show that risk managers can use SMA (100) and risk metric EWMA(100) smoothing constant of 0.96 model as a tool for estimating market risk at 95% confidence. At 99% confidence level both...
Value at Risk: A Standard Tool in Measuring Risk : A Quantitative Study on Stock Portfolio
2011
The role of risk management has gained momentum in recent years most notably after the recent financial crisis. This thesis uses a quantitative approach to evaluate the theory of value at risk which is considered a benchmark to measure financial risk. The thesis makes use of both parametric and non parametric approaches to evaluate the effectiveness of VAR as a standard tool in measuring risk of stock portfolio. This study uses the normal distribution, student t-distribution, historical simulation and the exponential weighted moving average at 95% and 99% confidence levels on the stock returns of Sonny Ericsson, Three Months Swedish Treasury bill (STB3M) and Nordea Bank. The evaluations of the VAR models are based on the Kupiec (1995) Test. From a general perspective, the results of the study indicate that VAR as a proxy of risk measurement has some imprecision in its estimates. However, this imprecision is not all the same for all the approaches. The results indicate that models which assume normality of return distribution display poor performance at both confidence levels than models which assume fatter tails or have leptokurtic characteristics. Another finding from the study which may be interesting is the fact that during the period of high volatility such as the financial crisis of 2008, the imprecision of VAR estimates increases. For the parametric approaches, the t-distribution VAR estimates were accurate at 95% confidence level, while normal distribution approach produced inaccurate estimates at 95% confidence level. However both approaches were unable to provide accurate estimates at 99% confidence level. For the non parametric approaches the exponentially weighted moving average outperformed the historical simulation approach at 95% confidence level, while at the 99% confidence level both approaches tend to perform equally. The results of this study thus question the reliability on VAR as a standard tool in measuring risk on stock portfolio. It also suggest that more research should be done to improve on the accuracy of VAR approaches, given that the role of risk management in today's business environment is increasing ever than before. The study suggest VAR should be complemented with other risk measures such as Extreme value theory and stress testing, and that more than one back testing techniques should be used to test the accuracy of VAR.
Journal of Economics and Business, 2018
The purpose of this journal is how to measurement the probability of the maximum risk level for stocks of state-owned banks in Indonesia. One consideration for investors to make an investments decision in stock instruments by monitoring the daily volatility movement and the trend. From the risk perspective by measuring the maximum probability of risk level in the future, it can be a consideration in making stock investment decisions that have the same movements and characteristics. Risk projection based on historical data with a certain time period can be calculated with Value at Risk (VaR) with a certain level of confidence. Thus the investment decision will be optimal. The Value at Risk model will calculate the expected losses. This research will show that volatility that looks the same but has a different level of risk. This research will measuring a useful value for prospective investors in determining investments among the stocks of state-owned banks in Indonesia. The data used in this study is the stock closing price that has been adjusted taken from the source of the website www.yahoo finance for the period 2015-2018. This study stipulates that the VAR model is the maximum risk estimate that may arise with a 95% confidence level, or an error rate that is tolerated at 5%. The VaR method used is historical analysis.
Evaluation of Value at Risk in Emerging Markets
International Journal of Financial Management, 2017
Financial institutions have witnessed numerous episodes of financial crises all over the world during the last four decades. The researchers, academicians and policy makers in the field of finance studied these episodes extensively and to mitigate the risk involved in these crises have proposed several measures in the financial literature, but Value at Risk (VaR) has emerged as a more popular risk measurement technique. Although a number of studies have been undertaken in this area of research for developed markets but very few studies have been conducted in developing and emerging market economies. This study makes an attempt to evaluate the performance of VaR in emerging markets namely Brazil, Russia, India and China by considering Historical, Monte Carlo and GARCH Simulations to calculate VaR for the period 1998 to 2015. The study found that GJRGARCH Simulation is more suitable for Brazil and China while Historical Simulation for Russian and Indian Stock Markets based on the backtesting experiment..in market behaviour, it becomes vital to measure the level of risk for potential investors and agents even after knowing its presence, in order to survive in the global competitive market in a dynamic manner. Unlike the matured financial markets, the emerging financial markets are characterized with insufficient liquidity, the small scale of trading and asymmetrical and low number of trading days with certain securities (Andjelić, Djaković and Radišić, 2010). In the recent times, the emerging markets have been playing a crucial role due to greater potential in terms of economic growth and investment opportunities. However, the emerging stock markets are relatively young markets and have not developed sufficiently so as to identify all information that affects the stock prices and therefore, do not respond quickly to the publicly disclosed information (Benaković and Posedel, 2010). After the financial instabilities during 70's and advent of derivative markets, floating exchange rates led to development of several risk measurement methods. Among these Value-at-Risk (VaR) has emerged as a popular measure for assessing the market risk of the portfolio among the trading community. It can be defined as the maximum potential loss of a specific portfolio for a given time horizon. Increasing availability of the financial data and rapid advances in computer technology led to the development of various VaR models that can be applied for the risk management profession. The application of VaR models and comparing their relative performance gained a momentum in the field of financial economics. However, there is no common model that can give best forecasts of these models see for example,
Estimating the Accuracy of Value-at-Risk (VAR) in Measuring Risk in Equity Investment in India
SSRN Electronic Journal, 2000
Over the past few years, the Value-at-Risk (VaR) has become a standard measure of market risk embraced by banks, trading firms, mutual funds and others, including even the non financial firms. But any risk measure is useful and reliable only insofar as it can be verified for its accuracy. This paper attempts to evaluate the accuracy of VaR in estimating the risk in equity investment in India. For this purpose we have used daily data for 30 securities comprising BSE-Sensex and two major stock indices-BSE Sensex and NSE Nifty for the period January 2006 to February 2007 and portfolionormal method (parametric approach to VaR calculation) for calculation of VaR. The hypothesis regarding accuracy of VaR estimates has been tested using Chi-square test. The results show that VaR estimate does not accurately measure the risk in equity investment in India as VaR overestimates the loss in 24 securities out of 30 securities. It is only in case of 4 securities that the observed number of violations is exactly equal to the expected number. These results may be attributed to non-normal distribution of equity returns in Indian securities market as against the normally distributed returns assumed under portfolio-normal method. All the securities are showing excess kurtosis estimate, exhibiting the leptokurtic returns' distribution and also, out of 30 securities, 20 are showing negatively skewed returns and 10 are showing positively skewed returns. Moreover the assumption of past representing the future is also not validated in the present case in the context of stock volatility observed during the period. We
Risk Assessment in Emerging Equity Markets: The case of
2013
South Africa is gripped by a poor investment culture with a large number of adults not investing. There is need to inform would be investors about alternative forms of investments like the stock market. The research estimates the amount of risk exposure for a South African equity portfolio using Value at Risk (VaR). The research reviews literature to get an understanding of the commonly used risk measure for financial markets. It looks at pros and cons of using VaR in determining risk exposure for the financial markets. The research will explore all possible methods of calculating VaR these are namely; Historic simulation, Monte Carlo simulation and Variance covariance method. VaR for the purpose of this research is calculated using the variance covariance method An equity portfolio is constructed using PE ratio as cut off criteria (cut off range 10<PE<12), this made the size of the portfolio small (20 stocks that complied with the selection criteria). The research will give an estimate value of amount of exposure to be expected in an equity portfolio.
South East European Journal of Economics and Business, 2015
The study evaluated the sensitivity of the Value- at- Risk (VaR) and Expected Shortfalls (ES) with respect to portfolio allocation in emerging markets with an index portfolio of a developed market. This study utilised different models for VaR and ES techniques using various scenario-based models such as Covariance Methods, Historical Simulation and the GARCH (1, 1) for the predictive ability of these models in both relatively stable market conditions and extreme market conditions. The results showed that Expected Shortfall has less risk tolerance than VaR based on the same scenario-based market risk measures
Application of VaR (Value at Risk) method on Belgrade Stock Exchange (BSE) optimal portfolio
2014
The main objective of this study is to determine the adequacy of the measurement of market risks of financial institutions in Serbia by the method of Value at Risk (VaR). For investors, in the current global financial crisis, it is particularly important to accurately measure and allocate risk and efficiently manage their portfolio. Possibility of application of VaR methodology, which is basically designed and developed for liquid and developed markets, should be tested on the emerging markets, which are characterized by volatility, illiquidity and shallowness of the market. Value of VaR in this study was calculated using historical and parametric methods and backtesting analysis was used to verify the adequacy of the application of VaR models. Backtesting VaR model performance analysis was conducted to compare the ex-ante VaR estimate to the ex-post returns. The empirical results show that parameter exponentially weighted moving average model gives lower values at risk in both cases (95% and 99%) due to the fact that this method assigns weights to more recent returns while our portfolio is exposed to a lower volatility in recent time. Based on the results of Kupiec's and Christoffersen's test, it was observed that VaR estimates obtained by both, parametric and historical simulation, give a good prediction of market risk, at 95% and 99% confidence level.
A Comparative Performance of Conventional Methods for Estimating Market Risk Using Value at Risk
This paper presents a comparative evaluation of the predictive performance of conventional univariate VaR models including unconditional normal distribution model, exponentially weighted moving average (EWMA/RiskMetrics), Historical Simulation, Filtered Historical Simulation, GARCH-normal and GARCH Students t models in terms of their forecasting accuracy. The paper empirically determines the extent to which the aforementioned methods are reliable in estimating one-day ahead Value at Risk (VaR). The analysis is based on daily closing prices of the USD/KES exchange rates over the period starting January 03, 2003 to December 31, 2016. In order to assess the performance of the models, the rolling window of approximately four years (n=1000 days) is used for backtesting purposes. The backtesting analysis covers the sub-period from November 2008 to December 2016, consequently including the most volatile periods of the Kenyan shilling and the historical all-time high in September 2015. The empirical results demonstrate that GJR-GARCH-t approach and Filtered Historical Simulation method with GARCH volatility specification perform competitively accurate in estimating VaR forecasts for both standard and more extreme quantiles thereby generally out-performing all the other models under consideration.