Conditional Skewness in Asset Pricing Tests


Book Description

If asset returns have systematic skewness, expected returns should include rewards for accepting this risk. We formalize this intuition with an asset pricing model which incorporates conditional skewness. Our results show that conditional skewness helps explain the cross-sectional variation of expected returns across assets and is significant even when factors based on size and book-to-market are included. Systematic skewness is economically important and commands a risk premium, on average, of 3.60 percent a year. Our results also suggest that the momentum effect is related to systematic skewness. The low expected return momentum portfolios have higher skewness than high expected return portfolios.










Time-Varying Conditional Skewness and the Market Risk Premium


Book Description

Single factor asset pricing models face two major hurdles: the problematic time-series properties of the ex ante market risk premium and the inability of the risk measure to account for a substantial degree of the cross-sectional variation of expected excess returns. We provide an explanation for the first failure using the following intuition: if investors know that the asset returns have conditional skewness given the information known today, the expected excess returns should include rewards for accepting skewness. We formalize this intuition with an asset pricing model which incorporates conditional skewness. We decompose the expected excess returns into components due to conditional variance and skewness. Our results show that conditional skewness is important and, when combined with the economy-wide reward for skewness, helps explain the time-variation of the ex ante market risk premiums. Conditional skewness has greater success in explaining the ex ante risk premium for the world portfolio than for the U.S. portfolio.







Multifactor Asset Pricing Model


Book Description

Numerous studies have shown that stock returns can be predicted over time with the multifactor asset pricing model based on the Arbitrage Pricing Theory (APT). However, the application of the multifactor asset pricing model in emerging markets remains debatable, owing to differences in the economic, cultural, and political structure. Using both the time-series regression approach and machine learning approach, this study finds that Fama-French profitability risk factor is important for describing aggregate stock market returns in Malaysia. Additionally, these market returns are positively correlated with the crude palm oil price and the Singapore stock market index. This study shall thus shed new light on the application of the multifactor asset pricing model in Malaysia.