Assessing the Performance of Value-at-Risk Models in Chinese Stock Market (original) (raw)

Parametric Value-at-Risk analysis: Evidence from stock indices

The Quarterly Review of Economics and Finance, 2012

We evaluate the performance of several volatility models in estimating one-day-ahead Value-at-Risk (VaR) of seven stock market indices using a number of distributional assumptions. Because all returns series exhibit volatility clustering and long range memory, we examine GARCH-type models including fractionary integrated models under normal, Student-t and skewed Student-t distributions. Consistent with the idea that the accuracy of VaR estimates is sensitive to the adequacy of the volatility model used, we find that AR (1)-FIAPARCH (1,d,1) model, under a skewed Student-t distribution, outperforms all the models that we have considered including widely used ones such as GARCH (1,1) or HYGARCH (1,d,1). The superior performance of the skewed Student-t FIAPARCH model holds for all stock market indices, and for both long and short trading positions. Our findings can be explained by the fact that the skewed Student-t FIAPARCH model can jointly accounts for the salient features of financial time series: fat tails, asymmetry, volatility clustering and long memory. In the same vein, because it fails to account for most of these stylized facts, the RiskMetrics model provides the least accurate VaR estimation. Our results corroborate the calls for the use of more realistic assumptions in financial modeling.

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.

Evaluation Approaches of Value at Risk for Tehran Stock Exchange

2015

The purpose of this study is estimation of daily Value at Risk (VaR) for total index of Tehran Stock Exchange using parametric, nonparametric and semi-parametric approaches. Conditional and unconditional coverage backtesting are used for evaluating the accuracy of calculated VaR and also to compare the performance of mentioned approaches. In most cases, based on backtesting statistics Results, accuracy of calculated VaR is approved for historical, Monte Carlo and Volatility-Weighted historical simulation methods. It is also approved for GARCH type of volatility models under normal distribution and Riskmetrics model under student-t distribution. On the other hand, it is observed that parametric approach measures VaR value more than non-parametric and semi-parametric approaches. This result indicates that GARCH type of volatility models under student-t distribution overestimate magnitude of value at risk. Finally, four volatility models of parametric approach including NARCH, NAGARCH ...

Testing Applicability of Value at Risk Models in Stocks Markets

Mediterranean Journal of Social Sciences, 2014

This paper evaluates the forecasting performance of Value at Risk (VaR) method based on two wide spread approaches, historical simulation and Risk Metrics, before and after the sub-prime crisis in the context of developed and emerging capital markets. We present results on both VaR 1% and VaR 5% on a one-day horizon for Belex 15 and SAX. For comparative purposes, the paper also focuses on the DJIA and the STOXX Eastern Europe Total Market Index, an index representative of emerging European stock markets. In order to validate accuracy of VaR results we employ different back test techniques. Results indicate that the relative performance of VaR as a measure of market risk significantly underestimates the true level of market risk in Serbian stock market, in contrast to Slovak, where standard VaR approaches accurately capture market risk exposure. Results also provide evidence that the characteristic of stock markets and their asset returns in combination with the desired confidence level and risk horizon determine how well a certain approach performs on a certain stock market.

A comparative study on value at risk and conditional value at risk with an application to the Malaysian financial market

Value at risk (VaR) and conditional value at risk (CVaR) are frequently used as risk measures in risk management. VaR estimates the maximum expected loss over a given time period at a given acceptance level, whereas CVaR measures the extreme risk or the risk beyond VaR. This paper aims to perform an empirical study on VaR and CVaR based on the daily returns of the Malaysian stock markets traded in Kuala Lumpur Composite Index (KLCI) over a time period using the RiskMetrics and the peaks over the threshold (POT) methods. In particular, the IGARCH (1, 1) model is applied for the RiskMetrics method, whereas the generalized Pareto distribution (GPD), a distribution based on an extreme value theory, is considered for the POT method. The results show that the GPD, which is considered in the POT method, provides an adequate fit to the data of threshold excesses, and the POT is a more reliable measure of risks compared to the RiskMetrics.

Estimating and Testing the Value at Risk Models: An Empirical Evidence from Khartoum Stock Exchange Sudan

2014

This paper aims to estimate and test the Value at Risk (VaR) of portfolio i.e. Khartoum Stock Exchange (KSE) index via variance methods, historical simulation and quantile method for the period 2005-2011. The main results are: KAE index is stastionarity, not normally distributed, and 0.44 of the total returns are negative indicating losses. Only the empirical quantile have passed the back-testing procedure. Historical simulation, generalizes autoregressive heteroscedasticity (GRCH(1,1) and RiskMetrics underestimate the risk, while the generalized formula overestimates the risk.

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.

VaR Analysis for the Shanghai Stock Market

ipcsit.com

In this paper we investigated the relevance of the skewed Student's t distribution innovation in capturing long-memory and asymmetry features in the volatility of Shanghai stock markets. We also examined the performance of in-sample and out-of-sample value-at-risk (VaR) analyses using the FIAPARCH model with the normal, Student's t, and skewed Student's t distribution innovations. We found that risk managers and portfolio investors can estimate VaR and optimal margin levels most accurately by using the skewed Student's t FIAPARCH VaR models of long and short trading positions in the Shanghai stock market.

A Detailed Comparison of Value at Risk in International Stock Exchanges

2003

This work investigates the performance of different models of Value at Risk (VaR). We include a wider range of methods (Parametric, Historical simulation, Monte Carlo simulation, and Extreme value theory models) and several models to compute the conditional variance (exponential moving averages, GARCH and asymmetric GARCH models) under Normal and Student's t-distribution of returns. We analyse four European indexes (IBEX-35, CAC40, DAX and FTSE100), the American Dow Jones and S&P 500 indexes, the Japanese Nikkei 225 index and the Hong Kong Hang Seng index. We examine two periods: a stable period and a volatile one. To choose the best model, we employ a two-stage selection approach. First, we test the accuracy of different models of VaR. We use the unconditional and conditional coverage test, the Back-Testing criterion and the dynamic quantile test. A model survived if all tests indicated the model is accurate. With regard to the first stage, the best models are Parametric and Extreme value theory methods, when they use asymmetric and nonasymmetric GARCH models under Student's t-distribution of returns. Second, we evaluate the loss function of these models. We use several non-parametric tests to test the superiority of a VaR model in terms of the loss function. The result of the second stage indicates that the best model is a Parametric model with conditional variance estimated by asymmetric GARCH model under Student's t-distribution of returns. Nowadays the Parametric models are not as popular because some authors argue that the most conventional parametric specifications have failed in capturing some rare events. However, this paper shows that these models can obtain successful VaR measures if conditional variance is estimated with a GARCH model to capture the characteristic of the returns. This model is usually an exponential GARCH under Student's t-distribution of returns. JEL: G32, G11, C52.

Value-at-Risk for South-East Asian Stock Markets: Stochastic Volatility vs. GARCH

Journal of Risk and Financial Management, 2018

This study compares the performance of several methods to calculate the Value-at-Risk of the six main ASEAN stock markets. We use filtered historical simulations, GARCH models, and stochastic volatility models. The out-of-sample performance is analyzed by various backtesting procedures. We find that simpler models fail to produce sufficient Value-at-Risk forecasts, which appears to stem from several econometric properties of the return distributions. With stochastic volatility models, we obtain better Value-at-Risk forecasts compared to GARCH. The quality varies over forecasting horizons and across markets. This indicates that, despite a regional proximity and homogeneity of the markets, index volatilities are driven by different factors.