TESTING THE LONG-RUN RISK MODEL: A KALMAN FILTER APPROACH

Jianqiu Wang and Ke Wu ()
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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

Annals of Financial Economics (AFE), 2018, vol. 13, issue 04, 1-15

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.

Keywords: Equity premium puzzle; long-run risk model; Kalman filter (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1142/S2010495218500197

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