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Cross-Sectional Asset Pricing

Paper Session

Sunday, Jan. 6, 2019 1:00 PM - 3:00 PM

Atlanta Marriott Marquis, L505
Hosted By: Econometric Society
  • Chair: Svetlana Bryzgalova, London Business School

Estimating Latent Asset-Pricing Factors

Martin Lettau
,
University of California-Berkeley
Markus Pelger
,
Stanford University

Abstract

We develop an estimator for latent factors in a large-dimensional panel of financial data that can explain expected excess returns. Statistical factor analysis based on Principal Component Analysis (PCA) has problems identifying factors with a small variance that are important for asset pricing. We generalize PCA with a penalty term accounting for the pricing error in expected returns. Our estimator searches for factors that can explain both the expected return and covariance structure. We derive the statistical properties of the new estimator and show that our estimator can find asset-pricing factors, which cannot be detected with PCA, even if a large amount of data is available. Applying the approach to portfolio data we find factors with Sharpe-ratios more than twice as large as those based on conventional PCA and with significantly smaller pricing errors.

Kernel Trick for the Cross Section

Serhiy Kozak
,
University of Michigan

Abstract

Characteristics-based asset pricing implicitly assumes that factor betas or risk prices are linear functions of pre-specified characteristics. Present value identities, such as Campbell-Shiller or clean-surplus accounting, however, clearly predict that expected returns are highly non-linear functions of all characteristics. While basic non-linearities can be easily accommodated by adding non-linear functions to the set of characteristics, the problem quickly becomes infeasible once interactions of characteristics are considered. I propose a method to construct a stochastic discount factor (SDF) when the set of characteristics is extended to an arbitrary---potentially infinitely-dimensional---set of non-linear functions of original characteristics. The method borrows ideas from a machine learning technique known as the "kernel trick" to circumvent the curse of dimensionality. I find that allowing for interactions and non-linearities of characteristics leads to substantially more efficient SDFs; out-of-sample Sharpe ratios for the implied MVE portfolio double.

Dissecting Spurious Factors with Cross-Sectional Regressions

Paolo Zaffaroni
,
Imperial College London
Valentina Raponi
,
Imperial College London

Abstract

This paper develops a methodology for testing for spurious factors within beta-pricing models, specifically designed for when the number of assets N is large but the time series dimension is fixed, and possibly very small. Our approach builds on the conventional OLS CSR procedure, which appears to exhibit many desirable properties when N becomes large.
Discussant(s)
Shrihari Santosh
,
University of Maryland
Michael Weber
,
University of Chicago
Cesare Robotti
,
University of Warwick
JEL Classifications
  • G1 - General Financial Markets
  • C5 - Econometric Modeling