Iterated Combination Forecast and Treasury Bond Predictability

52 Pages Posted: 13 Aug 2018

See all articles by Hai Lin

Hai Lin

Victoria University of Wellington - School of Economics & Finance

Wen-Rang Liu

National Taiwan University - Department of Finance

Chunchi Wu

SUNY at Buffalo - School of Management

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School

Date Written: July 27, 2018

Abstract

Using a large number of predictors and based on an extended iterated combination approach of Lin, Wu, and Zhou (2017), we document both statistical and economic significance of Treasury bond return predictability. Macroeconomic and aggregate liquidity variables contain predictive information for bond returns and combining them with term structure and Ludvigson-Ng macro factors significantly improve out-of-sample forecast gains. We also find that variance forecasts can be substantially improved with our approach, yielding significant gains in asset allocation decision. Our results show that information from a large number of predictors collectively contributes to the time-varying Treasury bond premia, and this is robust to different return measures, horizons and sample periods.

Keywords: Treasury; Iterated Combination Forecast; Predictability; Utility Gain

JEL Classification: G12; G14

Suggested Citation

Lin, Hai and Liu, Wen-Rang and Wu, Chunchi and Zhou, Guofu, Iterated Combination Forecast and Treasury Bond Predictability (July 27, 2018). Available at SSRN: https://ssrn.com/abstract=3220751 or http://dx.doi.org/10.2139/ssrn.3220751

Hai Lin (Contact Author)

Victoria University of Wellington - School of Economics & Finance ( email )

P.O. Box 600
Wellington 6001
New Zealand

Wen-Rang Liu

National Taiwan University - Department of Finance ( email )

1, Sec. 4, Roosevelt Road
Taipei, 106
Taiwan

Chunchi Wu

SUNY at Buffalo - School of Management ( email )

Jacobs Management Center
Buffalo, NY 14222
United States

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School ( email )

Washington University
Campus Box 1133
St. Louis, MO 63130-4899
United States
314-935-6384 (Phone)
314-658-6359 (Fax)

HOME PAGE: http://apps.olin.wustl.edu/faculty/zhou/

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