Conditional VaR using EVT – Towards a planned margin scheme

Bhattacharyya, Malay; Ritolia, Gopal

doi:10.1016/j.irfa.2006.08.004

Cited by 54 publications

(25 citation statements)

References 11 publications

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“…Autocorrelogram and partial-autocorrelogram of the logarithmic return suggest that yield sequence presents a certain degree of serial correlation, Further, autocorrelogram of the square of the logarithmic return suggest that yield sequence square presents serial correlation significantly [6][7][8][9] . According to SSR 、 Parameters significantly 、AIC and SBC determine the final model .…”

Section: The Establishment Of the Model And The Analysis Of The Resultsmentioning

confidence: 96%

Empirical Research on VAR Model Based on GJR-GARCH, EVT and Copula

Zhang¹,

Zhou²,

Ming³

et al. 2015

SJAMS

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Abstract:In this paper, we establish GJR-GARCH models to extract the residuals of logarithmic returns of two index---New York stock exchange composite index (NYA) and NASDAQ. and estimate the distribution function of the residuals utilizing Gaussian kernel method and Extreme Value Theory. The kernel cumulative distribution function estimates are well suited for the interior of the distribution where most of the residuals are found and the POT method of Extreme Value Theory fits the extreme residuals in upper and lower tails well. The monte carlo technique is used to simulate the income of securities index 20000 times after we get the marginal distribution of the residual income of securities index. Secondly, By using the copula function to get the joint distribution of mthe two stock index. Lastly, According to the theory of VAR calculate VAR value of the portfolio consisting of two equal weight comprehensive index in different confidence levels.

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Section: The Establishment Of the Model And The Analysis Of The Resultsmentioning

confidence: 96%

Empirical Research on VAR Model Based on GJR-GARCH, EVT and Copula

Zhang¹,

Zhou²,

Ming³

et al. 2015

SJAMS

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“…Bali (2007) found that EVT method using the Box-Cox generalized extreme value distribution can more accurately reflect the extreme risks of financial institutions. Bhattacharyya et al (2008) combined the GARCH model and the EVT model to construct a VaR measurement model for the dynamic volatility and nonnormality of stock return, and concluded that the dynamic VaR model is the best model for measuring VaR.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Risk Measurement in Financial Market Based on GARCH-VaR and FHS——An Example of Chinese Bond Market

Chen¹,

Zhang²,

Deng³

2018

AEF

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Accurately measuring the risk of bond market is very important for improving the risk management level of bond market and maintaining the stability of the financial system. Taking ChinaBond New Composite Wealth (gross) Index as the research object, this paper selects the closing price from January 1, 2002 to March 30, 2018, establishes the GARCH, EGARCH and GJR-GARCH model based on normal distribution and t distribution, and finds out the volatility aggregation and the leverage effect of the bond market. Then, this paper use two methods to measure the risk of the bond market: first, we estimate the value at risk (VaR) of the bond market by the parameter method, using conditional variance estimated by the GARCH models, and we carry out backtesting analysis and the Kupiec failure rate test on measurement accuracy of VaR. The results show that t distribution hypothesis and elimination of autocorrelation of the yield rate can improve the accuracy and robustness of the estimation of the VaR; second, we simulate the future revenue path of the bond market and compare it with the actual loss, using Filtered Historical Simulation (FHS) based on Bootstrap method. The results show that the bond market has leverage effect. The maximum possible loss under extreme conditions can be far greater than the maximum possible revenue. But the estimated VaR under 95% confidence level can predict future risks very well. Finally, according to the conclusion, this paper puts forward some suggestions for regulators and investors from the perspective of risk management.

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“…Embrechts et al [7] provide an overview of the empirical application of EVT in modeling extreme risks in finance and specifically in estimating VaR. In recent years, many researchers have undertaken research in modeling extremes and estimating extreme risks in the stock and currency markets due to stock market crashes, currency crises and large credit defaults experienced in the financial [11], Ghorbel and Trabelsi, [12]. Bali [13] demonstrates that the EVT method yields better with respect to the skewed Student-t and normal distributions using the daily index of the DJIA stock markets.…”

Section: Introductionmentioning

confidence: 99%

Using Conditional Extreme Value Theory to Estimate Value-at-Risk for Daily Currency Exchange Rates

Omari¹,

Mwita²,

Waititu³

2017

JMF

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This paper implements different approaches used to compute the one-day Value-at-Risk (VaR) forecast for a portfolio of four currency exchange rates. The concepts and techniques of the conventional methods considered in the study are first reviewed. These approaches have shortcomings and therefore fail to capture the stylized characteristics of financial time series returns such as; non-normality, the phenomenon of volatility clustering and the fat tails exhibited by the return distribution. The GARCH models and its extensions have been widely used in financial econometrics to model the conditional volatility dynamics of financial returns. The paper utilizes a conditional extreme value theory (EVT) based model that combines the GJR-GARCH model that takes into account the asymmetric shocks in time-varying volatility observed in financial return series and EVT focuses on modeling the tail distribution to estimate extreme currency tail risk. The relative out-of-sample forecasting performance of the conditional-EVT model compared to the conventional models in estimating extreme risk is evaluated using the dynamic backtesting procedures. Comparing each of the methods based on the backtesting results, the conditional EVT-based model overwhelmingly outperforms all the conventional models. The overall results demonstrate that the conditional EVT-based model provides more accurate out-of-sample VaR forecasts in estimating the currency tail risk and captures the stylized facts of financial returns.

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Conditional VaR using EVT – Towards a planned margin scheme

Cited by 54 publications

References 11 publications

Empirical Research on VAR Model Based on GJR-GARCH, EVT and Copula

Empirical Research on VAR Model Based on GJR-GARCH, EVT and Copula

Research on Risk Measurement in Financial Market Based on GARCH-VaR and FHS——An Example of Chinese Bond Market

Using Conditional Extreme Value Theory to Estimate Value-at-Risk for Daily Currency Exchange Rates

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