Assessing the performance of generalized autoregressive conditional heteroskedasticity-based value-at-risk models: a case of frontier markets

The conditional autoregressive Value-at-Risk (CAViaR) model, as a conditional autoregressive specification for calculating the Value-at-Risk (VaR) of the security market, has been receiving more and more attentions in recent years. As asymmetry may have a significant influence on the markets and the returns may have an autoregressive mean, this study proposes some extended CAViaR models, including asymmetric indirect threshold autoregressive conditional heteroskedasticity (TARCH) model and indirect generalized autoregressive conditional heteroskedasticity (GARCH) model with an autoregressive mean. We also present two types of CAViaR-Volatility models by adding the volatility term as an exogenous explanatory variable. Our empirical results indicate that extended models perform more effectively on out-of-sample predictions, as both forecasting effect and model stability have been improved. In addition, we find that the forecasting effect is better at the lower quantile (1%) than at the higher quantile (5%); a possible explanation is that extreme market information has more impact on VaR. In addition, there is negative correlation between volatility and VaR; VaR decreases as volatility increases.

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PENERAPAN METODEEXPECTED SHORTFALLPADA PENGUKURAN RISIKO INVESTASI SAHAM DENGAN VOLATILITAS MODEL GARCH

Jurnal Gaussian ◽

10.14710/j.gauss.v8i1.26644 ◽

2019 ◽

Vol 8 (1) ◽

pp. 184-193

Author(s):

Nurul Fitria Fitria Rizani ◽

Mustafid Mustafid ◽

Suparti Suparti

Keyword(s):

At Risk ◽

Value At Risk ◽

Stock Price ◽

Garch Model ◽

Expected Shortfall ◽

Conditional Heteroskedasticity ◽

Investment Risk ◽

Stock Investment ◽

Autoregressive Conditional Heteroskedasticity ◽

High Volatility

One of the methods that can be used to measure stock investment risk is Expected Shortfall (ES). ES is an expectation of risk size which value is greater than Value at Risk (VaR), ES has characteristics of sub-additive and convex. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to model stock data that has high volatility. Calculating ES is done with data that shows deviations from normality using Cornish-Fisher's expansion. This researchapplies the ES at the closing stock price of PT Astra International Tbk. (ASII), PT Bank Negara Indonesia (Persero) Tbk. (BBNI), and PT Indocement Tunggal Prakarsa Tbk. (INTP) for the period of 11 February 2013 - 31 March 2019. Based on the volatility of GARCH (1,1) analysis, we find ES calculation for each stock by 95% level confidence. The ES for ASII shares is 4.1%, greater than the VaR value which isonly 2.64%.The ES for BBNI shares is 4.38%, greater than it’s VaR value which is only 2,86%. The ES for INTP shares is 6.22%, which is also greater than it’s VaR value which is only3,99%. The greather of VaR then Thegreather of ES obtained.Keywords: Expected Shortfall, Value at Risk, GARCH

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