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The Short-Run and Long-Run Components of Idiosyncratic Volatility and Stock Returns

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  • Yunting Liu

    (Department of Finance, School of Economics, Peking University, 100871 Beijing, China)

Abstract

To capture the dynamics of idiosyncratic volatility of stock returns over different horizons and investigate the relationship between idiosyncratic volatility and expected stock returns, this paper develops and estimates a parsimonious model of idiosyncratic volatility consisting of a short-run and a long-run component. The conditional short-run and long-run components are found to be positively and negatively related to expected stock returns, respectively. The positive relation between the short-run component and stock returns may be caused by investors requiring compensation for bearing idiosyncratic volatility risk when facing trading frictions and hold underdiversified portfolios. The negative relationship between the long-run component and stock returns may reflect the fact that stocks with high long-run idiosyncratic volatility are less exposed to systematic risk factors and, hence, earn lower returns. Moreover, the low-risk exposure of stocks characterized by high idiosyncratic volatility lends support to real-option-based mechanisms to explain this negative relation. In particular, the systematic risk of a firm with abundant growth options crucially depends upon the risk exposure of these options. The value of growth options could rise significantly because of convexity when the increase in idiosyncratic volatility occurs over long horizons. And growth options’ systematic risk could fall because the relative magnitude of their value in relation to systematic risk factors decreases.

Suggested Citation

  • Yunting Liu, 2022. "The Short-Run and Long-Run Components of Idiosyncratic Volatility and Stock Returns," Management Science, INFORMS, vol. 68(2), pages 1573-1589, February.
    • Handle: RePEc:inm:ormnsc:v:68:y:2022:i:2:p:1573-1589
    DOI: 10.1287/mnsc.2020.3884
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    2. Ke, Shuyao & Phillips, Peter C.B. & Su, Liangjun, 2024. "Robust inference of panel data models with interactive fixed effects under long memory: A frequency domain approach," Journal of Econometrics, Elsevier, vol. 241(2).

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