Estimation and Filtering With Big Option Data

61 Pages Posted: 31 Dec 2018

See all articles by Kris Jacobs

Kris Jacobs

University of Houston - C.T. Bauer College of Business

Yuguo Liu

University of Houston - C.T. Bauer College of Business

Date Written: December 4, 2018

Abstract

The computational cost of estimating option valuation models is very high, due to model complexity and the abundance of available option data. We propose an approach that addresses these computational constraints by filtering the state variables using particle weights based on model-implied spot volatilities rather than model prices. We show that this approach is reliable. We illustrate our method by estimating the workhorse stochastic volatility and double-jump models using a big option data set. We obtain more precise estimates of variance risk premia and more plausible implied preference parameters, and we show that for these models moneyness and especially maturity restrictions may result in identification problems. The composition of the option sample affects parameter inference and the relative importance of options and returns in joint estimation.

Keywords: Option Valuation; Big Data; Particle Filter; MCMC; Identification; Risk Premia.

JEL Classification: G12

Suggested Citation

Jacobs, Kris and Liu, Yuguo, Estimation and Filtering With Big Option Data (December 4, 2018). Available at SSRN: https://ssrn.com/abstract=3300564 or http://dx.doi.org/10.2139/ssrn.3300564

Kris Jacobs (Contact Author)

University of Houston - C.T. Bauer College of Business ( email )

Houston, TX 77204-6021
United States

Yuguo Liu

University of Houston - C.T. Bauer College of Business ( email )

Houston, TX 77204-6021
United States

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