A New Formula for the Expected Excess Return of the Market

54 Pages Posted: 15 Oct 2019 Last revised: 20 Dec 2020

See all articles by Gurdip Bakshi

Gurdip Bakshi

Temple University-Fox School of Business

John Crosby

University of Maryland - Robert H. Smith School of Business

Xiaohui Gao

Temple University-Fox School of Business

Wei Zhou

University of Maryland - Robert H. Smith School of Business

Date Written: September 1, 2019

Abstract

Key to deriving the lower bound to the expected excess return of the market in Martin (2017) is the assumption of the negative correlation condition (NCC). We improve on the lower bound characterization by proposing an exact formula for the conditional expected excess return of the market. In our formula, each risk-neutral return central moment contributes to the expected excess return and is representable in terms of known option prices. To interpret theoretical and empirical distinctions between our formula and the lower bound, we develop and study the asset-pricing restrictions of the NCC.

Keywords: expected excess return of the market, negative correlation condition, lower bound, market risk premium

JEL Classification: G11, G12, G13

Suggested Citation

Bakshi, Gurdip S. and Crosby, John and Gao, Xiaohui and Zhou, Wei, A New Formula for the Expected Excess Return of the Market (September 1, 2019). Fox School of Business Research Paper, Available at SSRN: https://ssrn.com/abstract=3464298 or http://dx.doi.org/10.2139/ssrn.3464298

Gurdip S. Bakshi

Temple University-Fox School of Business ( email )

PA 19122
United States

John Crosby (Contact Author)

University of Maryland - Robert H. Smith School of Business ( email )

College Park, MD 20742-1815
United States
+447979901892 (Phone)

HOME PAGE: http://www.john-crosby.co.uk/

Xiaohui Gao

Temple University-Fox School of Business ( email )

PA 19122
United States

Wei Zhou

University of Maryland - Robert H. Smith School of Business ( email )

College Park, MD
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
387
Abstract Views
2,503
Rank
149,119
PlumX Metrics