Likelihood-based Dynamic Asset Pricing: Learning Time-varying Risk Premia from Cross-Sectional Models

Dennis Umlandt

No 2020-06, Working Paper Series from University of Trier, Research Group Quantitative Finance and Risk Analysis

Abstract: This paper proposes a new parametric approach to estimate linear factor pricing mod-els with time-varying risk premia. In contrast to recent contributions to the literature,the framework presented abstains from introducing instrument variables to describethe time variation of risk prices. Instead, time-varying risk prices and exposures followa recursive updating scheme constructed to reduce the one-step ahead prediction errorfrom a cross-sectional factor model at the current observation. This agnostic approachis particularly useful in situations where instrument variables are unavailable or of poorquality. Estimation and inference are done by likelihood maximization. A Monte Carlostudy compares the ability of the method to predict risk prices and returns to that ofa regression-based method that uses noisy signals from true risk price predictors. Ina realistic setting, the two approaches keep pace when the signal contains 80 percentcorrect information. An application to a macro-finance model of currency carry tradesillustrates the novel approach.

Keywords: Dynamic Asset Pricing; Generalized Autoregressive Score Models; Time-varying Risk Premia; Return Predictability (search for similar items in EconPapers)
JEL-codes: C58 G12 G17 (search for similar items in EconPapers)
Pages: 49 pages
Date: 2020
New Economics Papers: this item is included in nep-ecm, nep-mac and nep-upt
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Persistent link: https://EconPapers.repec.org/RePEc:trr:qfrawp:202006

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