Reduced-rank structures arise naturally in many economic and financial models, yet empirical implementations typically treat them as static. This paper introduces the Generalized Autoregressive Reduced-Rank Regression framework, in which reduced-rank coefficient matrices evolve through score-driven updating rules and thus allow low-dimensional structures to evolve over time. The framework nests a range of existing multivariate score-driven specifications as special cases. We investigate conditions for stationarity and invertibility of the score-driven filter and establish consistency and asymptotic normality of the maximum likelihood estimator under both statistical and economically motivated identification schemes. The empirical relevance of the framework is illustrated in an asset pricing application in which the dynamic low-rank structure arises from no-arbitrage and factor dimensionality considerations.
Presentations scheduled at: Score-driven and nonlinear time series conference (Venice), QFFE 2026 (Marseille), IAAE 2026 (Carcavelos), EEA-ESEM 2026 (Dublin)
We study the factor structure of currency characteristics using a tensor factor model that jointly captures their variation across currencies, characteristics, and time. The resulting compressed characteristics factors (CCFs) price a broad cross-section of currency portfolios with lower pricing errors than traditional factor models and PCA-based alternatives while formal spanning tests show that they subsume existing currency factors. We identify two dominant factors. The first is remarkably stable over time and closely resembles the classical carry factor. The second is a dynamic value-momentum spread factor whose relative weights gradually evolve over time. This time-variation helps reconcile differing conclusions in the recent literature on which additional factors drive currency risk premia beyond dollar and carry. The dynamic spread factor generates large positive returns precisely when the carry trade suffers its largest drawdowns, acting as a natural counterweight during crash episodes. This pattern is consistent with a financial-conditions channel under which the spread factor structurally builds positions opposed to carry-related crowding. We find that our carry-like and value-momentum factors load on intermediary capital risk shocks with opposite signs, lending empirical support to this channel.
Presentations: QFFE 2025 (Marseille), IRMC 2025 (Bari, scheduled), IAAE 2025 (Torino), VfS 2025 (Cologne), German Finance Association DGF 2025 (Hagen), CFE 2025 (London)
This paper proposes a Generalized Method of Moments (GMM)-based filtering approach to estimate conditional factor pricing models based on a set of central asset pricing moments. Unlike traditional methods that assume constant risk premia or rely on predictive regressions, this approach dynamically adjusts risk prices and exposures through a recursive process aimed at reducing conditional moment violations. Estimation and inference are performed using standard GMM procedures. Monte Carlo results show that the proposed approach effectively filters different types of risk premium dynamics. Applied to the Fama-French 5-factor model, the GMM-based procedure can substantially reduce pricing errors compared to other dynamic and static approaches. The results suggest that premium dynamics vary across factors, and while they are generally countercyclical, they show significant declines at the beginning of crisis periods. Moreover, adding a momentum factor reduces pricing but not prediction errors, indicating that momentum can be partly explained by the dynamics of other factors.
Presentations: Statistical Week 2023 (Dortmund), German Finance Association DGF 2023 (Hohenheim), University of Liechtenstein, University of Graz, CFE 2023 (Berlin), Midwest Finance Association 2024 (Chicago), SNDE Symposium 2024 (Padova), QFFE 2024 (Marseille), SoFiE (Pre-)Conference 2024 (Rio de Janeiro), IAAE 2024 (Thessaloniki), EEA-ESEM 2024 (Rotterdam), VfS 2024 (Berlin), IRMC 2025 (Bari), Econometric Society World Congress 2025 (Seoul).
Journal of Applied Econometrics, 2025, 40, no. 4: 455–70 . https://doi.org/10.1002/jae.3119.
Economics Letters, 2025, 247: 112119. https://doi.org/10.1016/j.econlet.2024.112119.
Journal of Econometrics, 2023, 237 2C: 105470. https://doi.org/10.1016/j.jeconom.2023.05.007.
Best Doctoral Paper Award DGF 2019
Journal of International Economics, 2021, 133: 103541. https://doi.org/10.1016/j.jinteco.2021.103541.