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Bond Risk Premia Forecasting: A Simple Approach for Extracting¨Macroeconomic Information from a Panel of Indicators

Francesco Audrino (), Fulvio Corsi () and Kameliya Filipova ()

University of St. Gallen Department of Economics working paper series 2010 from Department of Economics, University of St. Gallen

Abstract: We propose a simple but effective estimation procedure to extract the level and the volatility dynamics of a latent macroeconomic factor from a panel of observable indicators. Our approach is based on a multivariate conditionally heteroskedastic exact factor model that can take into account the heteroskedasticity feature shown by most macroeconomic variables and relies on an iterated Kalman filter procedure. In simulations we show the unbiasedness of the proposed estimator and its superiority to different approaches introduced in the literature. Simulation results are confirmed in applications to real inflation data with the goal of forecasting long-term bond risk premia. Moreover, we find that the extracted level and conditional variance of the latent factor for inflation are strongly related to NBER business cycles.

Keywords: Macroeconomic variables; Exact factor model; Kalman filter; Heteroskedasticity; Forecasting bond risk premia; Inflation measures; Business cycles (search for similar items in EconPapers)
JEL-codes: C13 C33 C53 C82 E31 E47 (search for similar items in EconPapers)
Pages: 24 pages
Date: 2010-03
New Economics Papers: this item is included in nep-ecm, nep-for and nep-upt
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Journal Article: Bond Risk Premia Forecasting: A Simple Approach for Extracting Macroeconomic Information from a Panel of Indicators (2016) Downloads
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