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Testing The Long-Run Risk Model: A Kalman Filter Approach

Author

Listed:
  • JIANQIU WANG

    (PBC School of Finance, Tsinghua University, Beijing 100083, P. R. China)

  • KE WU

    (#x2020;Hanqing Advanced Institute, Renmin University of China, Beijing 100872, P. R. China)

Abstract

This paper reevaluates the Long-Run Risk model proposed by Bansal and Yaron (2004) using the Kalman filter and Maximum Likelihood estimation method. Our findings show that the persistence of the small long-run predictable component in the consumption growth process is the key for the model performance. In our estimation exercises, if we relax the persistence restriction on the long-run risk parameter and adopt a Maximum Likelihood estimate, the Long-Run Risk model still requires a relative risk aversion at around 70 to fit the US data. However, we do not find strong empirical support for the persistence restriction from the data.

Suggested Citation

  • Jianqiu Wang & Ke Wu, 2018. "Testing The Long-Run Risk Model: A Kalman Filter Approach," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(04), pages 1-15, December.
    • Handle: RePEc:wsi:afexxx:v:13:y:2018:i:04:n:s2010495218500197
    DOI: 10.1142/S2010495218500197
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    References listed on IDEAS

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