GMM Estimation of Stochastic Volatility Models Using Transform-Based Moments of Derivatives Prices
Posted: 7 Jan 2021
Date Written: November 13, 2020
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
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