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Score-driven asset pricing: Predicting time-varying risk premia based on cross-sectional model performance

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  • Umlandt, Dennis

Abstract

This paper proposes a new parametric approach for estimating linear factor pricing models with dynamic risk premia. Time-varying risk prices and exposures follow an observation-driven updating scheme that reduces the one-step-ahead prediction error from a cross-sectional factor model at the current observation. This agnostic approach is particularly useful in situations where predictors are unknown or of uncertain quality. Updating schemes for elliptically distributed returns are derived and propose cross-sectional regression errors as driving sequence for the parameter dynamics. Estimation and inference are performed by likelihood maximization. A simulation study confirms that the novel method is capable of filtering and predicting substantial risk price movements. The empirical performance of the method is illustrated by an application to a panel of equity portfolios.

Suggested Citation

  • Umlandt, Dennis, 2023. "Score-driven asset pricing: Predicting time-varying risk premia based on cross-sectional model performance," Journal of Econometrics, Elsevier, vol. 237(2).
    • Handle: RePEc:eee:econom:v:237:y:2023:i:2:s0304407623001641
    DOI: 10.1016/j.jeconom.2023.05.007
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    More about this item

    Keywords

    Dynamic asset pricing; Generalized autoregressive score models; Time-varying risk premia; Return predictability;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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