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

Abstract

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%.

Forecasting Exchange Rates Comparison In The Presence Of Instabilities

“…The fourth term or coefficient, gamma (leverage) term explains the leverage effect. The APARCH model adds the fifth term called ( ) delta (Kisinbay, 2003, Ding, 2011, Smolović, Lipovina-Božović and Vujošević, 2017.…”

Development of a trading strategy for risk-averse investors based on VaR models

“…There are times when the GARCH is unstable, if the sum of the weights is higher than one, then it is not recommended to use it (Korkmaz & Aydin, 2002). Furthermore, there are more advanced GARCH methods such as TGARCH, EGARCH, but they failed to assess VaR when dealing with emerging markets (Smolovic, Bozovic, & Vujosevic, 2017). The best way to spot the differences is to plot them all on the graph and then delve deeper into these differences.…”

Value at risk estimation of the SET50 index: Comparison between stock exchange of Thailand and Thailand futures exchange

“…Wong et al (2016) provide extensive comparison of conditional volatility and VaR forecasts on S&P500, FTSE100, and DAX30 market indices among 13 risk models that involve GARCH and realized volatility specifications. Smolović et al (2017) also use both asymmetric and symmetric GARCH type models with four different distributions (normal, student-t, skewed student-t, and a reparameterised Johnson distribution) in estimating VaR for the Montenegro MONEX index before and during the global financial crisis. Some studies calculate VaR for stock index futures contracts.…”