Forecasting the Jordanian Stock Index : Modelling Asymmetric Volatility and Distribution Effects within a GARCH Framework

• Autorzy: Heitham Al-Hajieh , Hashem AlNemer , Timothy Rodgers , Jacek Niklewski
• Języki publikacji: EN

Abstrakty

EN

The modelling of market returns can be especially problematical in emerging and frontier financial markets given the propensity of their returns to exhibit significant non-normality and volatility asymmetries. This paper attempts to identify which representations within the GARCH family of models can most efficiently deal with these issues. A number of different distributions (normal, Student t, GED and skewed Student) and different volatility of returns asymmetry representations (EGARCH and GJR-GARCH) are examined. Our data set consists of daily Jordanian stock market returns over the period January 2000 - November 2014. Using both the Superior Predicative Ability (SPA) and Model Confidence Set (MCS) testing frameworks it is found that using GJR-GARCH with a skewed Student distribution most accurately and efficiently forecasts Jordanian market movements. Our findings are consistent with similar research undertaken in respect to developed markets. (original abstract)

Słowa kluczowe

EN

GARCH model; Forecasting; Stock market indexes

PL

Model GARCH; Prognozowanie; Indeks giełdowy

• Czasopismo: Copernican Journal of Finance & Accounting
• Rocznik: 2015
• Tom: 4
• Numer: nr 2
• Strony: 9--25

Twórcy

autor

Heitham Al-Hajieh

• King Abdulaziz University, Saudi Arabia

autor

Hashem AlNemer

• University of Jeddah, Saudi Arabia

autor

Timothy Rodgers

• Coventry University, UK

autor

Jacek Niklewski

• Coventry University, UK

Bibliografia

• Al-Hajieh, H., Redhead, K., & Rodgers T. (2011). Investor sentiment and calendar anomaly effects: A case study of the impact of Ramadan on Islamic Middle Eastern markets. Research in International Business and Finance, 25 (3), 345-356. http://dx.doi. org/10.1016/j.ribaf.2011.03.004.
• Andrikopoulos, P., Niklewski, J., & Rodgers T. (forthcoming). The portfolio diversification benefits of frontier markets: an investigation into regional effects. Handbook of Frontier Markets. Ed. by Andrikopoulos P., Gregoriou G., and Kallinterakis V. Elsevier.
• Assaf, A. (2015). Value-at-Risk analysis in the MENA equity markets: Fat tails and conditional asymmetries in return distributions. Journal of Multinational Financial Management, 29, 30-45. http://dx.doi.org/10.1016/j.mulfin.2014.11.002.
• Awartani, B. M., & Corradi, V. (2005). Predicting the volatility of the S&P-500 stock index via GARCH models: the role of asymmetries. International Journal of Forecasting, 21 (1), 167-183. http://dx.doi.org/10.1016/j.ijforecast.2004.08.003.
• Balaban, E. (2004). Comparative forecasting performance of symmetric and asymmetric conditional volatility models of an exchange rate. Economics Letters, 83 (1), 99- -105. http://dx.doi.org/10.1016/j.econlet.2003.09.028.
• Bentes, S. R., Menezes, R., & Ferreira, N. B. (2013). On the asymmetric behaviour of stock market volatility: evidence from three countries. International Journal of Academic Research, 5 (4), 24-32. http://dx.doi.org/10.7813/2075-4124.2013/5-4/A.4.
• Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31 (3), 307-327. http://dx.doi.org/10.1016/0304-4076(86)90063-1.
• Brailsford, T. J., & Faff, R. W. (1996). An evaluation of volatility forecasting techniques. Journal of Banking & Finance, 20 (3), 419-438. http://dx.doi.org/10.1016/0378- 4266(95)00015-1.
• Brooks, R. (2007). Power arch modelling of the volatility of emerging equity markets. Emerging Markets Review, 8(2), 124-133. http://dx.doi.org/10.1016/j.ememar. 2007.01.002.
• Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 50 (4), 987-1007. http://dx.doi.org/10.2307/1912773.
• Engle, R. F., & Ng, V. K. (1993). Measuring and testing the impact of news on volatility. The journal of finance, 48(5), 1749-1778. http://dx.doi.org/10.1111/j.1540-6261.1993.tb05127.x.
• Fernández, C., & Steel, M. F. (1998). On Bayesian modeling of fat tails and skewness. Journal of the American Statistical Association, 93(441), 359-371. http://dx.doi.org /10.1080/01621459.1998.10474117.
• Glosten, L. R., Jagannathan, R., & 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, 48 (5), 1779-1801. http://dx.doi.org/10.1111/j.1540-6261.1993.tb05128.x.
• Gokcan, S. (2000). Forecasting volatility of emerging stock markets: linear versus non-linear GARCH models. Journal of Forecasting, 19 (6), 499-504. http://dx.doi. org/10.1002/1099-131X(200011)19:6%3C499::AID-FOR745%3E3.0.CO;2-P.
• Hansen, P. R. (2005). A test for superior predictive ability. Journal of Business & Economic Statistics, 23 (4), 365-380. http://dx.doi.org/10.1198/073500105000000063.
• Hansen, P. R., & Lunde, A. (2005). A forecast comparison of volatility models: does anything beat a GARCH (1, 1)?. Journal of applied econometrics, 20 (7), 873-889. http:// dx.doi.org/10.1002/jae.800.
• Hansen, P. R., & Lunde, A. (2014). MULCOM 3.00. Econometric toolkit for multiple comparisons. Unpublished working paper.
• Hansen, P. R., Lunde, A., & Nason, J. M. (2011). The model confidence set. Econometrica, 79 (2), 453-497. http://dx.doi.org/10.3982/ECTA5771.
• Heynen, R. C., & Kat, H. M. (1994). Volatility prediction: a comparison of the stochastic volatility, Garch (1, 1) and Egarch (1, 1) models. The Journal of Derivatives, 2 (2), 50- -65. http://dx.doi.org/10.3905/jod.1994.407912.
• Liu, H. C., & Hung, J. C. (2010). Forecasting S&P-100 stock index volatility: The role of volatility asymmetry and distributional assumption in GARCH models. Expert Systems with Applications, 37 (7), 4928-4934. http://dx.doi.org/10.1016/j.eswa.2009.12.022.
• Marcucci, J. (2005). Forecasting stock market volatility with regime-switching GARCH models. Studies in Nonlinear Dynamics & Econometrics, 9(4), 1-53. http://dx.doi. org/10.2202/1558-3708.1145.
• McMillan, D., Speight, A., & Apgwilym, O. (2000). Forecasting UK stock market volatility. Applied Financial Economics, 10(4), 435-448. http://dx.doi.org/10.1080/ 09603100050031561.
• Mittnik, S., Paolella, M. S., & Rachev, S. T. (2000). Diagnosing and treating the fat tails in financial returns data. Journal of Empirical Finance, 7 (3), 389-416. http://dx.doi. org/10.1016/S0927-5398(00)00019-0.
• Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 59(2), 347-370. http:// dx.doi.org/10.2307/2938260.

Identyfikatory

DOI

10.12775/CJFA.2015.013