A comparison of GARCH models for VaR estimation

Orhan, Mehmet; Köksal, Bülent

doi:10.1016/j.eswa.2011.09.048

Cited by 53 publications

(23 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is the basis of VaR estimation, representing the standard approach to risk measuring -RiskMetrics. In general form, it can be written as (Orhan & Köksal, 2012):…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

GARCH models in value at risk estimation: empirical evidence from the Montenegrin stock exchange

Smolović

Lipovina-Božović

Vujošević

2017

Economic Research-Ekonomska Istraživanja

View full text Add to dashboard Cite

This article considers the adequacy of generalised autoregressive conditional heteroskedasticity (GARCH) model use in measuring risk in the Montenegrin emerging market before and during the global financial crisis. In particular, the purpose of the article is to investigate whether GARCH models are accurate in the evaluation of value at risk (VaR) in emerging stock markets such as the Montenegrin market. The daily return of the Montenegrin stock market index MONEX is analysed for the period January 2004-February 2014. The motivation for this research is the desire to approach quantifying and managing risk in Montenegro more thoroughly, using methodology that has not been used for emerging markets so far. Our backtesting results showed that none of the eight models passed the Kupiec test with 95% of confidence level, while only the ARMA (autoregressive movingaverage model) (1,2)-N GARCH model did not pass the Kupiec test with a confidence level of 99%. The results of the Christoffersen test revealed three models (ARMA(1,2)-TS GARCH(1,1) with a Student-t distribution of residuals, the ARMA(1,2)-T GARCH(1,1) model with a Student-t distribution of residuals, and ARMA(1,2)-EGARCH(1,1) with a reparameterised unbounded Johnson distribution [JSU] distribution of residuals) which passed the joint Christoffersen test with a 95% confidence level. It seems that these three models are appropriate for capturing volatility clustering, since all of them failed for a number of exceptions. Finally, none of the analysed models passed the Pearson's Q test, whether with 90%, 95% or 99%.

show abstract

“…It is the basis of VaR estimation, representing the standard approach to risk measuring -RiskMetrics. In general form, it can be written as (Orhan & Köksal, 2012):…”

Section: Methodsmentioning

confidence: 99%

“…The theoretical background for the VaR method is given by Jorion (1996), Duffie and Pan (1997) and Dowd (1998). Although there are some theoretical studies about the shortcoming of VaR due to its lack of sub-additivity and convexity (Cheng, Liu, and Wang (2004)), there is still no better measure to quantify risk (Orhan & Köksal, 2012).…”

Section: Introductionmentioning

confidence: 99%

GARCH models in value at risk estimation: empirical evidence from the Montenegrin stock exchange

Smolović

Lipovina-Božović

Vujošević

2017

Economic Research-Ekonomska Istraživanja

View full text Add to dashboard Cite

show abstract

“…(Orhan & Köksal, 2012). For example, in this study in order to take into account fat tails of asset returns, we used student-t distribution in conjunction with normal distribution in the estimation of VaR values.…”

Section: Value At Risk Analysismentioning

confidence: 99%

Alteration of Risk in Asian Bond Markets During and After Mortgage Crisis: Evidence from VaR Analysis

Günay¹

2016

AAMJAF

View full text Add to dashboard Cite

show abstract

“…To the best of our knowledge, a large volume of recent studies have been investigated and written about the value-at-risk (VaR) issue for various financial markets by using GARCH techniques, such as Angelidis et al (2004), Huang and Lin (2004), Liu and Hung (2010), Orhan and Köksal (2012) and So and Yu (2006) for stock markets, Al Janabi (2006), Bams et al (2005) and So and Yu (2006) for foreign exchange rate markets, Chan and Gray (2006), Sadeghi and Shavvalpour (2006) and Sadorsky (2006) for energy markets. However, despite the importance of VaR on financial risk management and the popularity of ETFs for common investors, there seems to have been relatively little work endeavored on ETFs.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Downside Risk of Exchange-Traded Funds: Do the Volatility Estimators Matter?

Wang¹,

Chen²,

Liu³

et al. 2015

IJEF

View full text Add to dashboard Cite

This paper aims to propose the augmented GJR-GARCH (GJR-GARCH M ) model that extends the GJR-GARCH model by comprising overnight returns volatility (ONV), daily high-low prices range (PK), and fear index (VIX) as explanatory variables for the GJR's variance equation, respectively. The proposed models are used to estimate the daily value-at-risk values and evaluate their downside risk management performance for the SPDRs covering the period from 2009 to 2014. Empirical results show that the GJR-GARCH M model outperforms the GJR-GARCH model for most cases, suggesting that the GJR-GARCH-based VaR forecasts can be moderately improved with the additional information embodied in the ONV, PK and VIX volatility estimators. In addition, daily high-low prices range and VIX are far more informative than the overnight volatility estimator for improving the GJR-GARCH-based VaR forecasts. Risk managers can employ the proposed models for estimating and controling the potential loss of ETFs in the face of financial catastrophes.

show abstract

A comparison of GARCH models for VaR estimation

Cited by 53 publications

References 27 publications

GARCH models in value at risk estimation: empirical evidence from the Montenegrin stock exchange

GARCH models in value at risk estimation: empirical evidence from the Montenegrin stock exchange

Alteration of Risk in Asian Bond Markets During and After Mortgage Crisis: Evidence from VaR Analysis

Analyzing the Downside Risk of Exchange-Traded Funds: Do the Volatility Estimators Matter?

Contact Info

Product

Resources

About