Smart Stochastic Discount Factors

We provide a no-arbitrage framework for stochastic discount factors (SDFs) that satisfy convex pricing constraints in markets characterized by a wide range of trading frictions. We demonstrate a duality relationship connecting minimum dispersion SDFs to portfolio optimization problems with penalty functions directly capturing the underlying frictions. Empirically, we examine how mispricing impacts the SDF’s effectiveness in explaining both cross-sectional and time series variation in asset returns. We find that a minimum-variance SDF, constructed by combining the capital asset pricing model SDF with a portfolio that constrains the mispricing of nonmarket risks, achieves a favorable tradeoff between time series and cross-sectional fit. This paper was accepted by Kay Giesecke, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.05750 .

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Publication Details

Journal
Management Science
Published
2025-11-13
DOI
https://doi.org/10.1287/mnsc.2024.05750
Primary Topic
Financial Markets and Investment Strategies
Type
article
Field-Weighted Citation Impact
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article

Smart Stochastic Discount Factors

Sofonias Alemu Korsaye, Fabio Trojani, Alberto Quaini
Management Science
Financial Markets and Investment Strategies
article

Smart Stochastic Discount Factors

Sofonias Alemu Korsaye, Fabio Trojani, Alberto Quaini
article en

Abstract

We provide a no-arbitrage framework for stochastic discount factors (SDFs) that satisfy convex pricing constraints in markets characterized by a wide range of trading frictions. We demonstrate a duality relationship connecting minimum dispersion SDFs to portfolio optimization problems with penalty functions directly capturing the underlying frictions. Empirically, we examine how mispricing impacts the SDF’s effectiveness in explaining both cross-sectional and time series variation in asset returns. We find that a minimum-variance SDF, constructed by combining the capital asset pricing model SDF with a portfolio that constrains the mispricing of nonmarket risks, achieves a favorable tradeoff between time series and cross-sectional fit. This paper was accepted by Kay Giesecke, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.05750 .

Management Science
Johns Hopkins University (US), Swiss Finance Institute (CH), Erasmus University Rotterdam (NL)
Decent work and economic growth
Openalex Percentile: Top 100%
Financial Markets and Investment Strategies
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