Estimating Option Pricing Models Using a Characteristic Function-Based Linear State Space Representation. (joint work with Peter Boswijk and Roger Laeven)
Abstract: We develop a novel filtering and estimation procedure for parametric option pricing models driven by general affine jump-diffusions. Our procedure is based on the comparison between an option-implied, model-free representation of the conditional log-characteristic function and the model-implied conditional log-characteristic function, which is functionally affine in the model’s state vector. We formally derive an associated linear state space representation and establish the asymptotic properties of the corresponding measurement errors. The state space representation allows us to use a suitably modified Kalman filtering technique to learn about the latent state vector and a quasi-maximum likelihood estimator of the model parameters, which brings important computational advantages. We analyze the finite-sample behavior of our procedure in Monte Carlo simulations. The applicability of our procedure is illustrated in two case studies that analyze S&P 500 option prices and the impact of exogenous state variables capturing Covid-19 reproduction and economic policy uncertainty.
Jump Contagion among Stock Market Indices: Evidence from Option Markets.
(joint work with Peter Boswijk, Roger Laeven and Andrei Lalu)
Abstract: This paper explores the contagious propagation of jumps among international stock market indices by exploiting a rich panel of options data. We propose a multivariate option pricing model designed to allow for, but not superimpose, time and space amplification of jumps in option markets. We develop a semi-parametric estimation procedure employing a continuum of moments conditions in GMM with implied states. We introduce a partial-information approach to reduce the computational complexity arising in the multivariate setting, derive the asymptotic properties of our estimators, and provide a detailed treatment of the standard errors. Our empirical results reveal evidence of jump contagion in option markets, both from the US to Europe and vice versa. We illustrate the importance of capturing jump contagion for risk management, pricing, and scenario analysis.
Work in progress
iCOS: Option-Implied COS Method.
Abstract: This paper proposes the option-implied Fourier-cosine method, iCOS, for non-parametric estimation of risk-neutral density, option prices, and option sensitivities. The iCOS method leverages the Fourier-based COS technique, proposed by Fang and Oosterlee (2008), by utilizing the option-implied cosine series coefficients. This procedure does not require any model assumptions on the underlying asset price dynamics, it is fully non-parametric, and it does not involve any numerical optimizations. All this makes it rather general and computationally very attractive. Furthermore, the derived asymptotic results allow us to construct confidence bounds for the quantities of interest.
Characteristic Function-Based Factor Modelling of Affine Jump Diffusions Using Options.
(joint work with Peter Boswijk, Roger Laeven and Niels Marijnen)
Autoencoder Option Pricing Models.
(joint work with Gustavo Freire)
Mapping the stocks in MICEX: Who is central in the Moscow Stock Exchange? Economics of Transition and Institutional Change, 28(4), 581-620, 2020 (joint work with Hakan Eratalay)
Systemic Risk of the Russian Economy, Finance and Business, 2017 (in Russian)