The Conditional Expected Return and Autocorrelation from the Derivatives

59 Pages Posted: 7 Apr 2021 Last revised: 4 Feb 2022

See all articles by Yueliang (Jacques) Lu

Yueliang (Jacques) Lu

University of North Carolina (UNC) at Charlotte - Finance

Weidong Tian

University of North Carolina (UNC) at Charlotte - The Belk College of Business Administration

Date Written: March 23, 2021

Abstract

We express conditional expected future returns and stock market autocorrelations with publicly available derivatives data. Our approach is model-free, robust to pricing kernel process choice, and provides a real-time conditional point of view. We demonstrate a moderate short-term reversal of market returns with this approach. Furthermore, our approach implies comparable autocorrelation by statistical inference model with a gradually fading memory feature. We construct a reversal signal based on this approach and show that the corresponding market timing strategy outperforms the buy-and-hold strategy overall. Finally, we demonstrate that the term structure of one-month future returns is pro-cyclical.

Keywords: Autocorrelation, conditional expected return, derivatives, market spanning, recovery

JEL Classification: G1, G12, G13

Suggested Citation

Lu, Yueliang and Tian, Weidong, The Conditional Expected Return and Autocorrelation from the Derivatives (March 23, 2021). Available at SSRN: https://ssrn.com/abstract=3810878 or http://dx.doi.org/10.2139/ssrn.3810878

Yueliang Lu (Contact Author)

University of North Carolina (UNC) at Charlotte - Finance ( email )

9201 University City Boulevard
Charlotte, NC 28223
United States

HOME PAGE: http://JacquesYL.github.io

Weidong Tian

University of North Carolina (UNC) at Charlotte - The Belk College of Business Administration ( email )

9201 University City Boulevard
Charlotte, NC 28223-0001
United States

HOME PAGE: http://belkcollegeofbusiness.uncc.edu/wtian1/

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