Embedded Files
with Patrick Gagliardini
Formerly titled “Extracting Statistical Factors When Betas Are Time-Varying.”
Presented at: SoFiE 2026 (scheduled), AMES-CSW 2026, FinEML 2025, ICEEE 2021, EFA 2020, MFA 2020
Figure: Yearly median number of conditional factors
Figure: Yearly median number of conditional factors
Remarks: This figure plots the yearly median of the estimated number of conditional latent factors for thresholds 𝛼 = 0.10, 0.20, 0.30 (corresponding to explained variance levels of 90%, 80%, and 70%, respectively). All estimates are computed out-of-sample using only the test period. Shaded regions indicate bear-market years following the classification in Lunde and Timmermann (2004).
with Daniele Bianchi and Teng Jiao
Presented at: Frontiers of Factor Investing 2026, MFA 2026, FinEML 2025
Figure: Cost–aware SDFs deliver higher net wealth than frictionless models.
Figure: Cost–aware SDFs deliver higher net wealth than frictionless models.
Presented at: ESSEC Business School, Duke University, University of Bristol, UC Berkeley (Haas), SoFiE Webinar for Graduate Students, EWMES 2022, CFE-CMStatistics 2021, SFI Research Days 2021, Shanghai University of Finance and Economics, Shandong University, Università della Svizzera italiana (USI Lugano)
Figure: Model Structure
Figure: Model Structure
Work In Progress
Work In Progress
Conditional Asset Pricing with Frictional Machine Learning
Conditional Asset Pricing with Frictional Machine Learning
with Sofonias Alemu Korsaye, and Fabio Trojani
Return Prediction is Not Alpha Discovery
Return Prediction is Not Alpha Discovery
Missing Not at Random: Cyber Risk and Asset Prices
Missing Not at Random: Cyber Risk and Asset Prices
with Minh Tri Phan
Market Integration and Heterogeneous Convergence: Evidence from the AH Premium
Market Integration and Heterogeneous Convergence: Evidence from the AH Premium
with Hao Yang
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