This paper employs a bivaraite vector autoregressive-generalized autoregressive conditional heteroscedasticity (VAR-GARCH) model recently developed by Ling and McAleer (2003) to examine the impact of oil price fluctuations on stock market returns in the Kingdom of Saudi Arabia over the period from January 1, 2007 to December 31, 2011. The proposed model is estimated using maximum likelihood method under the assumption of multivariate normal distributed error terms. The log likelihood function is maximized using Marquardt’s numerical iterative algorithm to search for optimal parameters. Empirical evidence from daily returns on the Saudi stock market (Tadawul) index and daily crude oil prices suggests that crude oil price fluctuations leads to increase stock market returns volatility during the period of the study.