Noncausal AR-GARCH Model and Its Applications in Asset Pricing

Since investors have diverse perspectives and limited information, their expectations can be subjective and prone to inaccuracies. Hence, price fluctuations are influenced by heterogeneous beliefs regarding future expectations, and both surveys and straightforward models can only partially capture the intricate nature of expectations. To address this issue, we employ a noncausal AR-GARCH model with a quasi-maximum likelihood technique to mitigate the impact of heterogeneous beliefs. Our approach allows us to determine the asymptotic distribution of estimated parameters and perform hypothesis tests. These empirical findings indicate that the error term in the US stock market is causal; in contrast, in the Chinese stock market, noncausal errors significantly impact price volatility. Furthermore, our models have the capability to discern nuanced distinctions between Brent and WTI crude oil prices, indicating that the price pattern of WTI may be more influenced by heterogeneous beliefs among market participants