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SMU SOE Seminar Series (May 13, 2025): Learning the Stochastic Discount Factor

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TOPIC:

LEARNING THE STOCHASTIC DISCOUNT 
FACTOR

ABSTRACT

We develop a statistical framework to learn the high-dimensional stochastic discount
factor (SDF) from a large set of characteristic-based portfolios. Specifically, we provide statistical support to use the MAXSER method proposed in Ao Li Zheng (2019) to screen for potentially useful factors and develop a statistical inference theory for further factor selection to construct the SDF portfolio. Applying our approach to a large number of characteristic-based portfolios, we find that our SDF estimator performs well in achieving a high Sharpe ratio and explaining the cross-section of expected returns of various portfolios.

Keywords: Stochastic Discount Factor; Factor Models; High Dimensions; Sparse Regressions; Maximum Sharpe Ratio Regression.

JEL: C55, C58, G11, G12.

Click here to view the CV.
Click here to view the paper.

PRESENTER

Yingying Li
Hong Kong University of Science and Technology

RESEARCH FIELDS

Statistical Learning for Financial Big Data
Large Portfolio Analytics Individualized Asset Allocation High-dimensional Financial Data Vast Volatility Matrix Modeling and Inference
High-frequency Financial Data Volatility Estimation and Prediction 

DATE:

13 May 2025 (Tuesday)

TIME:

2:00pm - 3:30pm

VENUE:

Meeting Room 5.1, Level 5
School of Economics
Singapore Management University
90 Stamford Road
Singapore 178903

 
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