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Research Article Open access CC BY 4.0

Empirical Evidence on the Validity of the Unconditional Higher Moment CAPM in the Bombay Stock Exchange

Akash Asthana, Syed Shafi Ahmed, Anjana Tiwari

Asian Journal of Economics, Business and Accounting · pp. 146–153 · Published 26 Mar 2024

10.9734/ajeba/2024/v24i51299

Abstract

The traditional Capital Asset Pricing Model (CAPM) assumed a normal distribution of returns, which was criticized by various researchers who recognized the non-normal distribution of returns in the Sharpe-Lintner CAPM. The introduction of unconditional higher moments, namely co-skewness and co-kurtosis, as additional measures of systematic risk may enhance the model's explanatory power, especially when the distribution function of stock returns is asymmetric. The present study empirically investigates the applicability of unconditional higher order moment CAPM and the impact of higher moments in the Indian stock market i.e. Bombay Stock Exchange using the data of sectoral indices for the period from April 2011 to March 2021. To test the four moment CAPM empirically, the specification given by Fang and Lai has been used in the study. The findings of the present study revealed that the higher moments (coskewness and cokurtosis) are significantly priced and have impact on the returns in the Indian stock market. The market risk premium for covariance was found to be insignificant. Further the hypothesis related to intercept term was accepted and market risk premiums were rejected. The results showed an increase in the explanatory power of the model as compared to the unconditional CAPM as the R-square value of the model was obtained better than the latter model. The mixed and inconclusive findings contradicted the model in the Indian context. The alternate models like the Fama-French three factor and five factor model should be exploited in the Indian context as those models have been rarely used in the Indian context.

Covariance coskewness cokurtosis Indian stock market Bombay stock exchange (BSE)

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