Value-at-risk predictive performance: a comparison between the CaViaR and GARCH models for the MILA and ASEAN-5 stock markets
Keywords:Value at risk, GARCH, CaViaR, MILA, ASEAN
Purpose. This paper tests the accuracies of the models that predict the Value-at-Risk (VaR) for the Market Integrated Latin America (MILA) and Association of Southeast Asian Nations (ASEAN) emerging stock markets during crisis periods.
Design/methodology/approach. Many VaR estimation models have been presented in the literature. In this paper, the VaR is estimated using the Generalized Autoregressive Conditional Heteroskedasticity, EGARCH and GJR-GARCH models under normal, skewed-normal, Student-t and skewed-Student-t distributional assumptions and compared with the predictive performance of the Conditional Autoregressive Value-at-Risk (CaViaR) considering the four alternative specifications proposed by Engle and Manganelli (2004).
Findings. The results support the robustness of the CaViaR model in out-sample VaR forecasting for the MILA and ASEAN-5 emerging stock markets in crisis periods. This evidence is based on the results of the backtesting approach that analyzed the predictive performance of the models according to their accuracy.
Originality/value. An important issue in market risk is the inaccurate estimation of risk since different VaR models lead to different risk measures, which means that there is not yet an accepted method for all situations and markets. In particular, quantifying and forecasting the risk for the MILA and ASEAN-5 stock markets is crucial for evaluating global market risk since the MILA is the biggest stock exchange in Latin America and the ASEAN region accounted for 11% of the total global foreign direct investment inflows in 2014. Furthermore, according to the Asian Development Bank, this region is projected to average 7% annual growth by 2025.
Assaf, A. (2009), “Extreme observations and risk assessment in the equity markets of MENA region: tail measures and value-at-risk”, International Review of Financial Analysis, Vol. 18 No. 3, pp. 109-116.
Bali, T.G. and Theodossiou, P. (2007), “A conditional-SGT-VaR approach with alternative GARCH models”, Annals of Operations Research, Vol. 151, pp. 241-267.
Bao, Y., Lee, T. and Saltoglu, B. (2004), “Evaluating predictive performance of value-at-risk models in emerging markets: a reality check∗”, Journal of Forecasting, Vol. 25 No. 2, pp. 101-128.
Bollerslev, T. (1986), “Generalized autoregressive conditional heteroskedasticity”, Journal of Econometrics, Vol. 31, pp. 307-327.
Chen, C.W.S., Gerlach, R., Hwang, B.B.K. and McAleer, M. (2012), “Forecasting value-at-risk using nonlinear regression quantiles and the intra-day range”, International Journal of Forecasting, Elsevier, Vol. 28 No. 3, pp. 557-574.
Christoffersen, P.F. (1998), “Evaluating interval forecasts”, International Economic Review, Vol. 39 No. 4, pp. 841-862.
Dimitrakopoulos, D.N., Kavussanos, M.G. and Spyrou, S.I. (2010), “Value at risk models for volatile emerging markets equity portfolios”, The Quarterly Review of Economics and Finance, Vol. 50 No. 4, pp. 515-526.
Drakos, A.A., Kouretas, G.P. and Zarangas, L. (2015), “Predicting conditional autoregressive value-at-risk for stock markets during tranquil and turbulent periods”, Journal of Financial Risk Management, Vol. 4, September, pp. 168-186.
Engle, R.F. and Manganelli, S. (2004), “CAViaR: conditional autoregressive value at risk by regression quantiles”, Journal of Business and Economic Statistics, Vol. 22 No. 4, pp. 367-381.
Glosten, L.R., Jagannathan, R. and Runkle, D.E. (1993), “On the relation between the expected value and the volatility of the nominal excess return on stocks”, The Journal of Finance, Vol. 48 No. 5, pp. 1779-1801.
Ho, L.C., Burridge, P., Cadle, J. and Theobald, M. (2000), “Value-at-risk: applying the extreme value approach to Asian markets in the recent financial turmoil”, Pacific Basin Finance Journal, Vol. 8 No. 2, pp. 249-275.
Huang, D., Yu, B., Fabozzi, F.J. and Fukushima, M. (2009), “CAViaR-based forecast for oil price risk”, Energy Economics, Elsevier, Vol. 31 No. 4, pp. 511-518.
Jian, Z., Wu, S. and Zhu, Z. (2018), “Asymmetric extreme risk spillovers between the Chinese stock market and index futures market: an MV-CAViaR based intraday CoVaR approach”, Emerging Markets Review, Vol. 37, pp. 98-113.
Klochkov, Y., Härdle, W.K. and Xiu, X. (2019), “Localizing multivariate CAViaR”, IRTG 1792 Discussion Paper, No. 2019-007, Humboldt-Universität zu Berlin, International Research Training Group 1792, High Dimensional Nonstationary Time Series, Berlin.
Koenker, R. and Bassett, G.J. (1978), “Regression quantiles”, Econometrica, Vol. 46 No. 1, pp. 33-50.
Kuester, K., Mittnik, S. and Paolella, M.S. (2006), “Value-at-risk prediction: a comparison of alternative strategies”, Journal of Financial Econometrics, Vol. 4 No. 1, pp. 53-89.
Kupiec, P.H. (1995), “Techniques for verifying the accuracy of risk measurement models”, The Journal of Derivatives, Vol. 3 No. 2, pp. 73-84.
Laporta, A.G., Merlo, L. and Petrella, L. (2018), “Selection of value at risk models for energy commodities”, Energy Economics, Vol. 74, pp. 628-643.
Li, Z., Wang, Y. and Huang, Z. (2020), “Risk connectedness heterogeneity in the cryptocurrency markets”, Frontiers in Physics, Vol. 8, pp. 1-13.
Lia, Z., Dong, H., Florosc, C., Charemisd, A. and Failler, P. (2021), “Re-examining bitcoin volatility: a CAViaR-based approach”, Emerging Markets Finance and Trade.
Liu, S., Gao, H., Hou, P. and Tan, Y. (2019), “Risk spillover effects of international crude oil market on China's major markets”, Energy, Vol. 7 No. 6, pp. 819-840.
Lizarzaburu, E.R., Burneo, K., Galindo, H. and Berggrun, L. (2015), “Emerging markets integration in Latin America (MILA) stock market indicators: Chile, Colombia, and Peru”, Journal of Economics, Finance and Administrative Science, Vol. 20, pp. 74-83.
Mcmillan, D.G. and Kambouroudis, D. (2009), “International review of financial analysis are risk metrics forecasts good enough? Evidence from 31 stock markets”, International Review of Financial Analysis, Vol. 18 No. 3, pp. 117-124.
Nelson, D. (1991), “Conditional heteroskedasticity in asset returns: a new approach”, Econometrica, Vol. 59 No. 2, pp. 347-370.
Peng, W. (2021), “The transmission of default risk between banks and countries based on CAViaR models”, International Review of Economics and Finance, Vol. 72, pp. 500-509.
Rubia, A. and Sanchis-marco, L. (2013), “On downside risk predictability through liquidity and trading activity: a dynamic quantile approach”, International Journal of Forecasting, Vol. 29, pp. 202-219.
Wu, D. (2020), “CAViaR and the empirical study on China's stock market”, Journal Physics: Conference Series, Vol. 1634, 012096, 2020.
Youssef, M., Belkacem, L. and Mokni, K. (2015), “Value-at-risk estimation of energy commodities: a long-memory GARCH-EVT approach”, Energy Economics, Vol. 51, pp. 99-110.
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