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Investors’ Risk Preference Characteristics and Conditional Skewness

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  • Fenghua Wen
  • Zhifang He
  • Xiaohong Chen

Abstract

Perspective on behavioral finance, we take a new look at the characteristics of investors’ risk preference, building the D-GARCH-M model, DR-GARCH-M model, and GARCHC-M model to investigate their changes with states of gain and loss and values of return together with other time-varying characteristics of investors’ risk preference. Based on a full description of risk preference characteristic, we develop a GARCHCS-M model to study its effect on the return skewness. The top ten market value stock composite indexes from Global Stock Exchange in 2012 are adopted to make the empirical analysis. The results show that investors are risk aversion when they gain and risk seeking when they lose, which effectively explains the inconsistent risk-return relationship. Moreover, the degree of risk aversion rises with the increasing gain and that of risk seeking improves with the increasing losses. Meanwhile, we find that investors’ inherent risk preference in most countries displays risk seeking, and their current risk preference is influenced by last period’s risk preference and disturbances. At last, investors’ risk preferences affect the conditional skewness; specifically, their risk aversion makes return skewness reduce, while risk seeking makes the skewness increase.

Suggested Citation

  • Fenghua Wen & Zhifang He & Xiaohong Chen, 2014. "Investors’ Risk Preference Characteristics and Conditional Skewness," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-14, January.
  • Handle: RePEc:hin:jnlmpe:814965
    DOI: 10.1155/2014/814965
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    Cited by:

    1. Chen, Lin & Wen, Fenghua & Li, Wanyang & Yin, Hua & Zhao, Lili, 2022. "Extreme risk spillover of the oil, exchange rate to Chinese stock market: Evidence from implied volatility indexes," Energy Economics, Elsevier, vol. 107(C).
    2. Zhao, Lili & Wen, Fenghua, 2022. "Risk-return relationship and structural breaks: Evidence from China carbon market," International Review of Economics & Finance, Elsevier, vol. 77(C), pages 481-492.
    3. Li, Ning & Li, Jiaojiao & Wang, Qizhou & Yan, Dairong & Wang, Liguan & Jia, Mingtao, 2024. "A novel copper price forecasting ensemble method using adversarial interpretive structural model and sparrow search algorithm," Resources Policy, Elsevier, vol. 91(C).
    4. He, Zhifang & Sun, Hao & Chen, Jiaqi & Yang, Xin & Yin, Zhujia, 2023. "Dynamic interaction of risk–return trade-offs between oil market and China’s stock market: An analysis from the risk preferences perspective," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
    5. He, Zhifang, 2022. "Asymmetric impacts of individual investor sentiment on the time-varying risk-return relation in stock market," International Review of Economics & Finance, Elsevier, vol. 78(C), pages 177-194.
    6. Zhifang He & Jiaqi Chen & Fangzhao Zhou & Guoqing Zhang & Fenghua Wen, 2022. "Oil price uncertainty and the risk‐return relation in stock markets: Evidence from oil‐importing and oil‐exporting countries," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 1154-1172, January.

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