Statistical Modeling of High Frequency Financial Data: Facts, Models and Challenges

12 Pages Posted: 26 Jan 2011 Last revised: 15 Mar 2011

Rama Cont

Imperial College London; CNRS; Norges Bank Research

Date Written: March 1, 2011

Abstract

The availability of high-frequency data on transactions, quotes and order flow in electronic order-driven markets has revolutionized data processing and statistical modeling techniques in finance and brought up new theoretical and computational challenges. Market dynamics at the transaction level cannot be characterized solely in terms the dynamics of a single price and one must also take into account the interaction between buy and sell orders of different types by modeling the order flow at the bid price, ask price and possibly other levels of the limit order book.

We outline the empirical characteristics of high-frequency financial time series and provide an overview of stochastic models for the continuous-time dynamics of a limit order book, focusing in particular on models which describe the limit order book as a queuing system. We describe some applications of such models and point to some open problems.

Keywords: high frequency data, order book, market microstructure, queueing systems, limit order markets, transaction data, trades and quotes, heavy traffic limit, diffusion, price impact, TAQ

JEL Classification: G1

Suggested Citation

Cont, Rama, Statistical Modeling of High Frequency Financial Data: Facts, Models and Challenges (March 1, 2011). Available at SSRN: https://ssrn.com/abstract=1748022 or http://dx.doi.org/10.2139/ssrn.1748022

Rama Cont (Contact Author)

Imperial College London ( email )

London, SW7 2AZ
United Kingdom

HOME PAGE: http://www3.imperial.ac.uk/people/r.cont

CNRS ( email )

Laboratoire de Probabilites & Modeles aleatoires
Universite Pierre & Marie Curie (Paris VI)
Paris, 75252
France

HOME PAGE: http://rama.cont.perso.math.cnrs.fr/

Norges Bank Research ( email )

P.O. Box 1179
Oslo, N-0107
Norway

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