TY - JOUR

T1 - INFERENCE for OPTION PANELS in PURE-JUMP SETTINGS

AU - Andersen, Torben G.

AU - Fusari, Nicola

AU - Todorov, Viktor

AU - Varneskov, Rasmus T.

N1 - Funding Information:
Andersen Torben G. 1 Fusari Nicola 2 Todorov Viktor 1 * Varneskov Rasmus T. 1 1 Northwestern University 2 The Johns Hopkins University Carey Business School * Address correspondence to Viktor Todorov, Department of Finance, Kellogg School of Management, Northwestern University, Evanston, IL 60208, USA; e-mail: v-todorov@northwestern.edu . 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. 19 10 2018 10 2019 35 5 901 942 Copyright © Cambridge University Press 2018 2018 Cambridge University Press 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 L 2 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. pdf S0266466618000373a.pdf
Funding Information:
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.
Publisher Copyright:
Copyright © Cambridge University Press 2018.

PY - 2019/10/1

Y1 - 2019/10/1

N2 - 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.

AB - 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.

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U2 - 10.1017/S0266466618000373

DO - 10.1017/S0266466618000373

M3 - Article

AN - SCOPUS:85072225198

SN - 0266-4666

VL - 35

SP - 901

EP - 942

JO - Econometric Theory

JF - Econometric Theory

IS - 5

ER -