Investigating ICAPM with Dynamic Conditional Correlations

Turan G. Bali, Robert F. Engle
2008 Social Science Research Network  
This paper examines the intertemporal relation between expected return and risk for 30 stocks in the Dow Jones Industrial Average. The mean-reverting dynamic conditional correlation model of Engle (2002) is used to estimate a stock's conditional covariance with the market and test whether the conditional covariance predicts time-variation in the stock's expected return. The risk-aversion coefficient, restricted to be the same across stocks in panel regression, is estimated to be between two and
more » ... be between two and four and highly significant. This result is robust across different market portfolios, different sample periods, alternative specifications of the conditional mean and covariance processes, different data sets including book-to-market portfolios and stocks in the S&P 100 index, and including a wide variety of state variables that proxy for the intertemporal hedging demand component of the ICAPM. The risk premium induced by the conditional covariation of individual stocks with the market portfolio remains economically and statistically significant after controlling for risk premia induced by conditional covariation with macroeconomic variables (federal funds rate, default spread, and term spread), financial factors (size, book-to-market, and momentum), and volatility measures (implied, GARCH, and range volatility). JEL classifications: G12; G13; C51. ABSTRACT This paper examines the intertemporal relation between expected return and risk for 30 stocks in the Dow Jones Industrial Average. The mean-reverting dynamic conditional correlation model of Engle (2002) is used to estimate a stock's conditional covariance with the market and test whether the conditional covariance predicts time-variation in the stock's expected return. The risk-aversion coefficient, restricted to be the same across stocks in panel regression, is estimated to be between two and four and highly significant. This result is robust across different market portfolios, different sample periods, alternative specifications of the conditional mean and covariance processes, different data sets including book-to-market portfolios and stocks in the S&P 100 index, and including a wide variety of state variables that proxy for the intertemporal hedging demand component of the ICAPM. The risk premium induced by the conditional covariation of individual stocks with the market portfolio remains economically and statistically significant after controlling for risk premia induced by conditional covariation with macroeconomic variables (federal funds rate, default spread, and term spread), financial factors (size, book-to-market, and momentum), and volatility measures (implied, GARCH, and range volatility). JEL classifications: G12; G13; C51. Modeling the coherence in short-run nominal exchange rates: a multivariate generalized ARCH model. Review of Economics and Statistics 72, 498-505. Bollerslev, T., Engle, R. F., Wooldridge, J. M., 1988. A capital asset pricing model with time-varying covariances. Journal of Political Economy 96, 116-131. Bollerslev, T., Wooldridge, J. M., 1992. Quasi-maximum likelihood estimation and inference in dynamic models with time varying covariances. Econometric Reviews 11, 143-172. Bollerslev, T., Zhou, H., 2006. Volatility puzzles: a simple framework for gauging return-volatility regressions Journal of Econometrics 131, 123-150.
doi:10.2139/ssrn.1089559 fatcat:heea6prpg5dgrlows2b6hdwiiu