A Descriptive Study of High-Frequency Trade and Quote Option Data

55 Pages Posted: 7 Sep 2019

See all articles by Torben G. Andersen

Torben G. Andersen

Northwestern University - Kellogg School of Management; National Bureau of Economic Research (NBER); Aarhus University - CREATES

Ilya Archakov

European University Institute

Leon Eric Grund

University of Vienna

Nikolaus Hautsch

University of Vienna - Department of Statistics and Operations Research; Center for Financial Studies (CFS); Vienna Graduate School of Finance (VGSF)

Sergey Nasekin

Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Ingmar Nolte

Lancaster University - Department of Accounting and Finance

Manh Cuong Pham

Lancaster University - Department of Accounting and Finance

Stephen J. Taylor

Lancaster University - Department of Accounting and Finance

Viktor Todorov

Independent

Date Written: August 30, 2019

Abstract

This paper provides a guide to high frequency option trade and quote data disseminated by the Options Price Reporting Authority (OPRA). First, we present a comprehensive overview of the fragmented U.S. option market, including details on market regulation and the trading processes for all 15 constituent option exchanges. Then, we review the general structure of the OPRA dataset and present a thorough empirical description of the observed option trades and quotes for a selected sample of underlying assets that contains more than 25 billion records. We outline several types of irregular observations and provide recommendations for data filtering and cleaning. Finally, we illustrate the usefulness of the high frequency option data with two empirical applications: option-implied variance estimation and risk-neutral density estimation. Both applications highlight the richer information content of the high frequency OPRA data relative to the widely used end-of-day OptionMetrics data.

Keywords: Options Data, High Frequency Data, Market Microstructure

JEL Classification: C55, G10

Suggested Citation

Andersen, Torben G. and Archakov, Ilya and Grund, Leon Eric and Hautsch, Nikolaus and Nasekin, Sergey and Nolte, Ingmar and Pham, Manh Cuong and Taylor, Stephen J. and Todorov, Viktor, A Descriptive Study of High-Frequency Trade and Quote Option Data (August 30, 2019). Available at SSRN: https://ssrn.com/abstract=3446690 or http://dx.doi.org/10.2139/ssrn.3446690

Torben G. Andersen

Northwestern University - Kellogg School of Management ( email )

2001 Sheridan Road
Evanston, IL 60208
United States

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Aarhus University - CREATES ( email )

School of Economics and Management
Building 1322, Bartholins Alle 10
DK-8000 Aarhus C
Denmark

Ilya Archakov

European University Institute ( email )

Villa Schifanoia
133 via Bocaccio
Firenze (Florence), Tuscany 50014
Italy

Leon Eric Grund

University of Vienna ( email )

Bruenner Strasse 72
Vienna, Vienna 1090
Austria

Nikolaus Hautsch

University of Vienna - Department of Statistics and Operations Research ( email )

Oskar-Morgenstern-Platz 1
Vienna, A-1090
Austria

Center for Financial Studies (CFS) ( email )

Gr├╝neburgplatz 1
Frankfurt am Main, 60323
Germany

Vienna Graduate School of Finance (VGSF) ( email )

Welthandelsplatz 1
Vienna, 1020
Austria

Sergey Nasekin

Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE) ( email )

Spandauer Strasse 1
Berlin, D-10178
Germany

Ingmar Nolte (Contact Author)

Lancaster University - Department of Accounting and Finance ( email )

Lancaster, Lancashire LA1 4YX
United Kingdom

Manh Cuong Pham

Lancaster University - Department of Accounting and Finance ( email )

Lancaster, Lancashire LA1 4YX
United Kingdom

Stephen J. Taylor

Lancaster University - Department of Accounting and Finance ( email )

The Management School
Lancaster LA1 4YX
United Kingdom
+ 44 15 24 59 36 24 (Phone)
+ 44 15 24 84 73 21 (Fax)

HOME PAGE: http://www.lancs.ac.uk/staff/afasjt

Viktor Todorov

Independent ( email )

No Address Available

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