Empirical Evidence on the Validity of the Conditional Higher Moment Capm in the Bombay Stock Exchange

Akash Asthana, Syed Shafi Ahmed, Anjana Tiwari
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Abstract

Various researchers criticized the assumption of a normal distribution of returns in the traditional Capital Asset Pricing Model (CAPM), leading to the recognition of the non-normal distribution of returns and the mean and variance were insufficient to characterize the distribution of returns completely which led the researchers to look for higher order moments. The present study investigated empirical validity of the conditional higher order moment Capital Asset Pricing Model (CAPM) in the context of the Indian stock market, specifically the Bombay Stock Exchange utilizing the data from sectoral indices for the period from April 2011 to March 2021. To test the four moment CAPM empirically, the specification derived from Bollerslev et al. (1988) has been used in the study. The study's findings reveals that the higher moment i.e. conditional coskewness and cokurtosis hold significant pricing implications and do have an impact on returns in the Indian stock market. The explanatory power of the model showed an increase as compared to the unconditional CAPM as the R-square value obtained was greater than the latter model. The results of the study were found to be mixed and inconclusive. Hence, the conditional higher moment CAPM cannot be validated in the Indian context.
孟买证券交易所条件高矩阵 Capm 有效性的经验证据
许多研究人员批评了传统资本资产定价模型(CAPM)中收益率正态分布的假设,从而认识到收益率的非正态分布以及均值和方差不足以完全描述收益率的分布特征,这促使研究人员寻找高阶矩。本研究利用 2011 年 4 月至 2021 年 3 月期间的行业指数数据,以印度股票市场(特别是孟买证券交易所)为背景,研究了条件高阶矩资本资产定价模型(CAPM)的实证有效性。为了对四阶 CAPM 进行实证检验,本研究采用了 Bollerslev 等人(1988 年)提出的规范。研究结果表明,较高的矩,即条件余弦和峰度,具有重要的定价意义,对印度股市的回报率确实有影响。与无条件 CAPM 相比,该模型的解释力有所提高,因为其 R 方值大于后者。研究结果喜忧参半,没有定论。因此,有条件高矩阵 CAPM 无法在印度得到验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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