Time-Varying Factor Selection : A Sparse Fused GMM Approach

Research output: Working PapersPreprint

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Original languageEnglish
PublisherSocial Science Research Network (SSRN)
Number of pages54
Publication statusOnline published - 11 Aug 2023


Empirical asset pricing studies evaluate and select risk factors solely based on their historical aggregate performance, implicitly assuming a time-invariant model specification, and overlooking potential time variations of specification in the stochastic discount factor (SDF) model. This paper presents a new method for capturing the time-varying sparsity of factor models by identifying heterogeneous structural breaks instrumented by macroeconomic regimes. Our empirical findings highlight that factor model specification changes over time. We identify time-invariant factors, such as REG and STR, as well as time-varying factors, such as IMD, BAB, and IVOL, selected in different periods in response to macroeconomic-targeted regime switching. The collective explanatory power of these 20 risk factors is high during periods of high interest rates or low market valuation, but their effectiveness declines when market liquidity is high. Finally, we evaluate factors by modeling unsynchronized factor discovery using unbalanced panel data to account for heterogeneous academic publication timings.

Research Area(s)

  • conditional asset pricing, heterogeneous structural breaks, macroeconomic regimes, sparsity, time-varying model specifications

Citation Format(s)

Time-Varying Factor Selection: A Sparse Fused GMM Approach. / Cui, Liyuan; Feng, Guanhao; Hong, Yongmiao et al.
Social Science Research Network (SSRN), 2023.

Research output: Working PapersPreprint