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
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)
New Economics Papers: this item is included in nep-ecm and nep-mst
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Persistent link: https://EconPapers.repec.org/RePEc:aah:create:2018-04
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