Learning and Forecasts about Option Returns through the Volatility Risk Premium

37 Pages Posted: 27 Feb 2019

See all articles by Alejandro Bernales

Alejandro Bernales

Universidad de Chile

Louisa Chen

University of Sussex

Marcela Valenzuela

University of Chile

Date Written: June 15, 2017

Abstract

We use learning in an equilibrium model to explain the puzzling predictive power of the volatility risk premium (VRP) for option returns. In the model, a representative agent follows a rational Bayesian learning process in an economy under incomplete information with the objective of pricing options. We show that learning induces dynamic differences between probability measures P and Q, which produces predictability patterns from the VRP for option returns. The forecasting features of the VRP for option returns, obtained through our model, exhibit the same behaviour as those observed in an empirical analysis with S&P 500 index options.

Keywords: Option Returns, Volatility Risk Premium, Bayesian Learning, Predictability, Dynamic Equilibrium Model

JEL Classification: D83, G12, G13, G14, G17

Suggested Citation

Bernales, Alejandro and Chen, Louisa and Valenzuela, Marcela, Learning and Forecasts about Option Returns through the Volatility Risk Premium (June 15, 2017). Available at SSRN: https://ssrn.com/abstract=3336243 or http://dx.doi.org/10.2139/ssrn.3336243

Alejandro Bernales (Contact Author)

Universidad de Chile ( email )

República 701
Santiago
Chile

HOME PAGE: http://www.alejandrobernales.com

Louisa Chen

University of Sussex ( email )

Falmer, Brighton BN1 9SL
United Kingdom

Marcela Valenzuela

University of Chile ( email )

Pío Nono Nº1, Providencia
Santiago, R. Metropolitana 7520421
Chile

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