GMM Estimation of Stochastic Volatility Models Using Transform-Based Moments of Derivatives Prices

Posted: 7 Jan 2021

See all articles by Yannick Dillschneider

Yannick Dillschneider

Goethe University Frankfurt - Department of Finance

Raimond Maurer

Goethe University Frankfurt - Finance Department

Date Written: November 13, 2020

Abstract

Derivatives, especially equity and volatility options, contain valuable and oftentimes essential information for estimating stochastic volatility models. Absent strong assumptions, their typically highly nonlinear pricing dependence on the state vector prevents or at least severely impedes their inclusion into standard estimation approaches. This paper develops a novel and unified methodology to incorporate moments involving derivatives prices into a GMM-type estimation procedure. Invoking new results from generalized transform analysis, we derive analytically tractable expressions for exact moments and devise a computationally efficient approximation procedure. We exemplify our methodology with an estimation problem that jointly accounts for stock returns as well as prices of equity and volatility options.

Keywords: generalized transform analysis, stochastic volatility models, option pricing, GMM estimation

JEL Classification: C32, C51, C58, G12, G13

Suggested Citation

Dillschneider, Yannick and Maurer, Raimond, GMM Estimation of Stochastic Volatility Models Using Transform-Based Moments of Derivatives Prices (November 13, 2020). Available at SSRN: https://ssrn.com/abstract=3730044

Yannick Dillschneider (Contact Author)

Goethe University Frankfurt - Department of Finance ( email )

Theodor-W.-Adorno-Platz 3
Frankfurt, 60629
Germany

Raimond Maurer

Goethe University Frankfurt - Finance Department ( email )

Gr├╝neburgplatz 1
House of Finance
Frankfurt, 60323
Germany

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