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Global idiosyncratic risk moments

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Abstract

We investigate a global cross-sectional relation between idiosyncratic risk moments and expected stock returns by suggesting three global idiosyncratic volatility, skewness, and kurtosis risk factors. We also suggest two global small minus big and high minus low risk proxies for estimating return residuals of the test assets from a global asset pricing model. To perform robustness checks, we suggest other four global risk factors of momentum, leverage, bid-ask spread, and liquidity. We find a significant negative relation between stock portfolio returns and the global moments, and the cross section of stock returns reflects a significant negative price of risk for global idiosyncratic skewness (−0.13%) and idiosyncratic volatility (−1.85%) and a positive and significant price of risk for global idiosyncratic kurtosis. We find that our suggested risk factors are key drivers of risk premia in stock market and are robust to various checks. These factors also can forecast the gross domestic product growth over the sample period.

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Notes

  1. The reasons are in Fu (2009) and Hueng and Yau (2013).

  2. This implies that there is not a relationship between expected returns and idiosyncratic risk moments.

  3. There is no consensus in the literature on the time-series behavior and the forecasting power of idiosyncratic risk. Goyal and Santa-Clara (2003) find a positive relationship between future market return and equal-weighted IV, whereas Bali et al. (2005) do not find the relation for value-weighted IV. Campbell et al. (2001) find an increase in IV since 1962 and Brandt et al. (2010) find that this increase is an episodic phenomenon. In our paper, we tend to investigate not only the time-series behavior and the forecasting power of risk moments, but the idiosyncratic risk premiums and their price.

  4. The results are also reported by Barberis and Huang (2008) and Mitton and Vorkink (2007).

  5. The intertemporal CAPM presents this prediction (Merton 1973; Campbell 1996; Chen 2003). Stocks that deliver low returns during high volatility add negative skewness to a portfolio. Thus, investors have preferences on skewness so that stocks with a largely negative return sensitivity to volatility demand a larger return in equilibrium.

  6. We label the residuals from these regressions and ignore the U.S. risk and market return components that are uncorrelated to the world risk and market return. We call them as the US-specific risk and market return components, \({\textit{MKT}}_t^{{\textit{M, specific}}} =\widehat{{\alpha }_1}+ \widehat{\varepsilon } _{1t} \), \({\textit{SMB}}_t^{M,{\textit{specific}}} = \widehat{{\alpha }_2}+\widehat{\varepsilon }_{2t} \), and \({\textit{HML}}_t^{{\textit{M, specific}}} =\widehat{{\alpha }_3 } +\widehat{\varepsilon }_{3t} \).

  7. The advantages of selecting these indices are in Atanasov and Nitschka (2014).

  8. A contemporaneous factor model shows high returns over a time period with high contemporaneous co-variation in factor loadings over the same period if the factor constructs a positive risk premium.

  9. This is a lagged factor in the first step of FMB regression. We thus use the coefficients of this factor that is already lagged for the second step regression.

  10. As stated in Campbell et al. (2001), the quarterly series behave very identical to the monthly ones.

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Authors and Affiliations

  1. School of Business, Western Sydney University, Sydney, Australia

    Mohammadreza Tavakoli Baghdadabad & Girijasankar Mallik

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  1. Mohammadreza Tavakoli Baghdadabad
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  2. Girijasankar Mallik
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Correspondence to Mohammadreza Tavakoli Baghdadabad.

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Tavakoli Baghdadabad, M., Mallik, G. Global idiosyncratic risk moments. Empir Econ 55, 731–764 (2018). https://doi.org/10.1007/s00181-017-1301-y

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