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Estimating and Forecasting Conditional Volatility and Correlations of the Dow Jones Islamic Stock Market Index Using Multivariate GARCH-DCC

Listed author(s):
  • Omer, Gamal Salih
  • Masih, Mansur

Volatility is a measure of variability in the price of an asset and is associated with unpredictability and uncertainty about the price. Even it is a synonym for risk; higher volatility means higher risk in the respective context. With regard to stock market, the extent of variation in stock prices is referred to stock market volatility. A spiky and rapid movement in the stock prices may throw out risk-averse investors from the market. Hence a desired level of volatility is demanded by the markets and its investors. The traditional methods of volatility and correlation analysis did not consider the effect of conditional (or time-varying) volatility and correlation. Hence a major issue facing the investors in the contemporary financial world is how to minimize risk while investing in a portfolio of assets. An understanding of how volatilities of and correlations between asset returns change over time including their directions (positive or negative) and size (stronger or weaker) is of crucial importance for both the domestic and international investors with a view to diversifying their portfolios for hedging against unforeseen risks as well as for dynamic option pricing. Therefore, appropriate modelling of volatility is of importance due to several reasons such as it becomes a key input to many investment decisions and portfolio creations, the pricing of derivative securities and financial risk management. Thus, in this paper, we aim to estimate and forecast conditional volatility of and correlations between daily returns of the seven selected Dow Jones Islamic and conventional price indexes spanning from 01/1/2003 to 31/3/2013. The sample period from January 30, 2003 to December, 13, 2010 amounting to 2053 daily observations are used for estimation and the remaining sample period is used for evaluation, through the application of the recently-developed Dynamic Multivariate GARCH approach to investigate empirical questions of the time-varying volatility parameters of these selected Dow Jones stock indices and time varying correlation among them. The contribution of this work is an improvement on others‘ works particularly in terms of time-varying volatility and correlation of assets incorporating Islamic assets. We find that all volatility parameters are highly significant, with the estimates very close to unity implying a gradual 2 volatility decay. The t-distribution appears to be more appropriate in capturing the fat-tailed nature of the distribution of stock returns and the conditional correlations of returns of all Dow Jones Islamic Markets, Dow Jones Islamic UK, and Dow Jones Islamic US Indices with other indices are not found constant but changing. The policy implications of this finding are that the shariah investors should monitor these correlations and mange their investment portfolios accordingly. In addition to this, the different financial markets offer different opportunities for portfolio diversification.

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File URL: https://mpra.ub.uni-muenchen.de/58862/1/MPRA_paper_58862.pdf
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 58862.

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Date of creation: 26 Aug 2014
Handle: RePEc:pra:mprapa:58862
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  1. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
  2. Becker, Kent G & Finnerty, Joseph E & Gupta, Manoj, 1990. " The Intertemporal Relation between the U.S. and Japanese Stock Markets," Journal of Finance, American Finance Association, vol. 45(4), pages 1297-1306, September.
  3. Sheng-Yung Yang, 2005. "A DCC analysis of international stock market correlations: the role of Japan on the Asian Four Tigers," Applied Financial Economics Letters, Taylor and Francis Journals, vol. 1(2), pages 89-93, March.
  4. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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