Market Risk Premium Violations in Asset Pricing Models – A Higher Order Moments Approach

Abstract

Conventional asset pricing models like Capital Asset Pricing Model (CAPM) are not efficient in estimating return on traded assets in various emerging markets including India. Non-normality of returns distributions coupled with investors desire to maximize returns in volatile markets has accentuated the need for modeling portfolios based on higher order moments like skewness and kurtosis. We examine the relevance of higher moments in selection of portfolios in Indian stock markets using weekly returns of 100 stocks listed on Bombay Stock Exchange for the period April, 2012 to March, 2017 that includes the volatile periods and captures major fundamental events. Results of the optimization and higher moments regression models indicate that investors expect a high return to compensate them for additional risk of holding equities and place negative market risk premium for systemic variance. The investors in Indian stock market are demanding negative risk premiums for market risk in terms of variance while they demand positive (negative) risk premium for positive (negative) skewness. Our results are therefore opposite to the basic propositions of Modern Portfolio Theory (MPT). We also establish that Indian investors are highly risk averse to the effect of systematic kurtosis