学术讲座

Forecasting Bond Returns with Real Time Macroeconomic Data: A Predictive Principal Component Approach

时间:2017-12-06

Finance Seminar2017-21

 

Topic: Forecasting Bond Returns with Real Time Macroeconomic Data: A Predictive Principal Component Approach

Speaker: Fuwei Jiang, Central University of Finance and Economics

Time: Wednesday, 6 December, 10:00-11:30

Place: Room 217, Guanghua Building 2

 

Abstract:

Ghysels, Horan, and Moench (2017) show that extracting principal component (PC) factors from real time as opposed to revised macro variables substantially reduces their power in forecasting bond excess returns. In this paper, we propose a predictive principal component (PPC) approach to extract factors from expected bond excess returns predicted by real time macro variables. In so doing, the new PPC factors remove common noises in real time data and exhibit significant bond return predictability. The in- and out-ofsample R2s improve by more than 50% relative to the PC factors. Moreover, the forecasted bond excess returns are countercyclical, consistent with standard asset pricing models.

 

Introduction:

Dr. Jiang is an Associate Professor in Finance in Central University of Finance and Economics. He obtained his Ph.D in Finance from Singapore Management University (SMU) in 2014. His research interest is primarily in asset pricing and behavioral finance with focus on return predictability. He have published several papers in leading academic journals such as Review of Financial Studies, Journal of Financial Economics, Journal of Banking and Finance, Journal of International Money and Finance,《金融研究》, among others. I have received competing research grants from the National Natural Science Foundation of China, and Beijing Natural Science Foundation. His research win several research awards including the Highly Cited and Most Read Articles published in RFS in 2015-2016. And He have been nominated as the Chang-Jiang Young Scholar (in Finance) candidate by CUFE in 2017.

http://fuweijiang.weebly.com/

 

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