Forecasting the volatility of the dow jones islamic stock market index: Long memory vs. regime switching - Adnen Ben Nasr; Thomas Lux; Ahdi Noomen Ajm; Rangan Gupta (Kiel University, Department of Economics, 2014)
The financial crisis has fueled interest in alternatives to traditional asset classes that might be less affected by large market gyrations and, thus, provide for a less volatile development of a portfolio. One attempt at selecting stocks that are less prone to extreme risks, is obeyance of Islamic Sharia rules. In this light, we investigate the statistical properties of the Dow Jones Islamic Stock Market Index (DJIM) and explore its volatility dynamics using a number of up-to-date statistical models allowing for long memory and regime-switching dynamics. We find that the DJIM shares all stylized facts of traditional asset classes, and estimation results and forecasting performance for various volatility models are also in line with prevalent findings in the literature. Overall, the relatively new Markov-switching multifractal model performs best under the majority of time horizons and loss criteria. Long memory GARCH-type models always improve upon the short-memory GARCH specification and additionally allowing for regime changes can further improve their performance.
Keywords: G15; G17; G23; 330; islamic finance; volatility dynamics; long memory; multifractals