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Option Panels in Pure-Jump Settings

Torben Andersen (), Nicola Fusari (), Viktor Todorov () and Rasmus T. Varneskov ()
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Nicola Fusari: The Johns Hopkins University Carey Business School, Postal: The Johns Hopkins University Carey Business School, Baltimore, MD 21202, USA
Viktor Todorov: Northwestern University, Postal: Department of Finance, Kellogg School of Management, Northwestern University, Evanston, IL 60208, USA
Rasmus T. Varneskov: Northwestern University and CREATES, Postal: Department of Finance, Kellogg School of Management, Northwestern University, Evanston, IL 60208; CREATES, Aarhus, Denmark; Multi Assets at Nordea Asset Management, Copenhagen, Denmark

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

Abstract: We develop parametric inference procedures for large panels of noisy option data in the setting where the underlying process is of pure-jump type, i.e., evolve only through a sequence of jumps. The panel consists of options written on the underlying asset with a (different) set of strikes and maturities available across observation times. We consider the asymptotic setting in which the cross-sectional dimension of the panel increases to infinity while its time span remains fixed. The information set is further augmented with high-frequency data on the underlying asset. Given a parametric specification for the risk-neutral asset return dynamics, the option prices are nonlinear functions of a time-invariant parameter vector and a time-varying latent state vector (or factors). Furthermore, no-arbitrage restrictions impose a direct link between some of the quantities that may be identified from the return and option data. These include the so-called jump activity index as well as the time-varying jump intensity. We propose penalized least squares estimation in which we minimize L_2 distance between observed and model-implied options and further penalize for the deviation of model-implied quantities from their model-free counterparts measured via the highfrequency returns. We derive the joint asymptotic distribution of the parameters, factor realizations and high-frequency measures, which is mixed Gaussian. The different components of the parameter and state vector can exhibit different rates of convergence depending on the relative informativeness of the high-frequency return data and the option panel.

Keywords: Inference; Jump Activity; Large Data Sets; Nonlinear Factor Model; Options; Panel Data; Stable Convergence; Stochastic Jump Intensity (search for similar items in EconPapers)
JEL-codes: C51 C52 G12 (search for similar items in EconPapers)
Pages: 32
Date: 2018-01-10
New Economics Papers: this item is included in nep-ecm and nep-mst
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