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INFERENCE FOR OPTION PANELS IN PURE-JUMP SETTINGS

Published online by Cambridge University Press:  19 October 2018

Torben G. Andersen,
Nicola Fusari,
Viktor Todorov and
Rasmus T. Varneskov
Torben G. Andersen
Affiliation:
Northwestern University
Nicola Fusari
Affiliation:
The Johns Hopkins University Carey Business School
Viktor Todorov*
Affiliation:
Northwestern University
Rasmus T. Varneskov
Affiliation:
Northwestern University
*
*Address correspondence to Viktor Todorov, Department of Finance, Kellogg School of Management, Northwestern University, Evanston, IL 60208, USA; e-mail: v-todorov@northwestern.edu.
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Abstract

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We develop parametric inference procedures for large panels of noisy option data in a setting, where the underlying process is of pure-jump type, i.e., evolves 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 the observation times. We consider an asymptotic setting in which the cross-sectional dimension of the panel increases to infinity, while the time span remains fixed. The information set is 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 the L2 distance between observed and model-implied options. In addition, we penalize for the deviation of the model-implied quantities from their model-free counterparts, obtained from the high-frequency 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 exhibit different rates of convergence, depending on the relative (asymptotic) informativeness of the high-frequency return data and the option panel.

Information

Type
ARTICLES
Information
Econometric Theory , Volume 35 , Issue 5 , October 2019, pp. 901 - 942
Copyright
Copyright © Cambridge University Press 2018 

Footnotes

Andersen and Varneskov gratefully acknowledge support from CREATES, Center for Research in Econometric Analysis of Time Series (DNRF78), funded by the Danish National Research Foundation. The work is partially supported by NSF Grant SES-1530748. We would like to thank the Editor (Peter C.B. Phillips), Co-Editor (Dennis Kristensen), and anonymous referees for many useful comments and suggestions.

References

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