AbstractBased on the maximum entropy (ME) method, we introduce an information theoretic approach to estimating conditional moment functions with incorporating a theoretical constraint implied from the consumption-based capital asset pricing model (CCAPM). Using the ME conditional mean/variance functions obtained from the ME density, we analyze the relationship between asset returns and consumption growth under the theoretical constraint of the CCAPM. We evaluate the predictability of asset return using consumption growth through in-sample estimation and out-of-sample prediction in the ME mean regression function. We also examine the ME variance regression function for the asset return volatility as a function of the consumption growth. Our findings suggest that incorporating the CCAPM constraint can capture the nonlinear predictability of asset returns in mean especially in tails, and that the consumption growth has an effect on reducing stock return volatility, indicating the counter-cyclical variation of stock market volatility.
<span>This paper focuses on the effects of information arrival on asset return volatility. Rather than examine aggregate patterns in daily variances, this paper exploits the information rich circumstances of merger and tender offer bids to generate hypotheses of varying information arrival, and tests those hypotheses. The results reveal that differing bid circumstances, such as the presence of competing bids, do generate different levels of volatility, and these difference are consistent with the hypothesized information arrival differences.</span>
Maximum Entropy Analysis of Consumption-based Capital Asset Pricing Model and Volatility
