Liquidity Guided Machine Learning: The Case of the Volatility Risk Premium

34 Pages Posted:

See all articles by Eric Ghysels

Eric Ghysels

University of North Carolina Kenan-Flagler Business School; University of North Carolina (UNC) at Chapel Hill - Department of Economics

Ruslan Goyenko

McGill University - Desautels Faculty of Management

Chengyu Zhang

McGill University - Desautels Faculty of Management

Date Written: November 7, 2020

Abstract

The financial industry has eagerly adopted machine learning algorithms to improve on traditional predictive models. In this paper we caution against blindly applying such techniques. We compare forecasting ability of machine learning methods in evaluating future payoffs on synthetic variance swaps. Standard machine learning methods tend to identify contracts which are illiquid, and hard to trade. The most successful strategies turn out to be those where we pair machine learning with institutional and market/traders inputs and insights. We show that liquidity guided pre-selection of inputs to machine learning results in trading strategies with improved pay-offs to the writers of variance swap contract replicating portfolio.

Keywords: Machine learning, Option pricing

JEL Classification: C2, C5, G1

Suggested Citation

Ghysels, Eric and Goyenko, Ruslan and Zhang, Chengyu, Liquidity Guided Machine Learning: The Case of the Volatility Risk Premium (November 7, 2020). Available at SSRN: https://ssrn.com/abstract=

Eric Ghysels (Contact Author)

University of North Carolina Kenan-Flagler Business School ( email )

Kenan-Flagler Business School
Chapel Hill, NC 27599-3490
United States

University of North Carolina (UNC) at Chapel Hill - Department of Economics ( email )

Gardner Hall, CB 3305
Chapel Hill, NC 27599
United States
919-966-5325 (Phone)
919-966-4986 (Fax)

HOME PAGE: http://www.unc.edu/~eghysels/

Ruslan Goyenko

McGill University - Desautels Faculty of Management ( email )

1001 Sherbrooke St. West
Montreal, Quebec H3A1G5 H3A 2M1
Canada

Chengyu Zhang

McGill University - Desautels Faculty of Management ( email )

1001 Sherbrooke St. West
Montreal, Quebec H3A1G5 H3A 2M1
Canada

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
11
Abstract Views
118
PlumX Metrics