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Research output: Research › Working paper

**Option Panels in Pure-Jump Settings.** / Andersen, Torben Gustav; Fusari, Nicola ; Todorov, Viktor; Varneskov, Rasmus T.

Research output: Research › Working paper

Andersen, TG, Fusari, N, Todorov, V & Varneskov, RT 2018 'Option Panels in Pure-Jump Settings' Institut for Økonomi, Aarhus Universitet, Aarhus.

Andersen, T. G., Fusari, N., Todorov, V., & Varneskov, R. T. (2018). *Option Panels in Pure-Jump Settings*. Aarhus: Institut for Økonomi, Aarhus Universitet. CREATES Research Papers, No. 2018-04

Andersen TG, Fusari N, Todorov V, Varneskov RT. 2018. Option Panels in Pure-Jump Settings. Aarhus: Institut for Økonomi, Aarhus Universitet.

Andersen, Torben Gustav et al. *Option Panels in Pure-Jump Settings*. Aarhus: Institut for Økonomi, Aarhus Universitet. (CREATES Research Papers; Journal number 2018-04). 2018., 32 p.

Andersen TG, Fusari N, Todorov V, Varneskov RT. Option Panels in Pure-Jump Settings. Aarhus: Institut for Økonomi, Aarhus Universitet. 2018 Jan 10.

Andersen, Torben Gustav ; Fusari, Nicola ; Todorov, Viktor ; Varneskov, Rasmus T./ **Option Panels in Pure-Jump Settings**. Aarhus : Institut for Økonomi, Aarhus Universitet, 2018. (CREATES Research Papers; No. 2018-04).

@techreport{4b23b41fe4db421abf3bf8a9461fed89,

title = "Option Panels in Pure-Jump Settings",

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",

author = "Andersen, {Torben Gustav} and Nicola Fusari and Viktor Todorov and Varneskov, {Rasmus T.}",

year = "2018",

month = "1",

publisher = "Institut for Økonomi, Aarhus Universitet",

type = "WorkingPaper",

institution = "Institut for Økonomi, Aarhus Universitet",

}

TY - UNPB

T1 - Option Panels in Pure-Jump Settings

AU - Andersen,Torben Gustav

AU - Fusari,Nicola

AU - Todorov,Viktor

AU - Varneskov,Rasmus T.

PY - 2018/1/10

Y1 - 2018/1/10

N2 - 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.

AB - 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.

KW - Inference, Jump Activity, Large Data Sets, Nonlinear Factor Model, Options, Panel Data, Stable Convergence, Stochastic Jump Intensity

M3 - Working paper

BT - Option Panels in Pure-Jump Settings

PB - Institut for Økonomi, Aarhus Universitet

ER -