Predicting Bid-Ask Spreads Using Long Memory Autoregressive Conditional Poisson Models

42 Pages Posted: 15 Jul 2011

See all articles by Axel Groß-Klußmann

Axel Groß-Klußmann

Humboldt Universität zu Berlin

Nikolaus Hautsch

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

Date Written: July 12, 2011

Abstract

We introduce a long memory autoregressive conditional Poisson (LMACP) model to model highly persistent time series of counts. The model is applied to forecast quoted bid-ask spreads, a key parameter in stock trading operations. It is shown that the LMACP nicely captures salient features of bid-ask spreads like the strong autocorrelation and discreteness of observations. We discuss theoretical properties of LMACP models and evaluate rolling window forecasts of quoted bid-ask spreads for stocks traded at NYSE and NASDAQ. We show that Poisson time series models significantly outperform forecasts from ARMA, ARFIMA, ACD and FIACD models. The economic significance of our results is supported by the evaluation of a trade schedule. Scheduling trades according to spread forecasts we realize cost savings of up to 13 % of spread transaction costs.

Keywords: Bid-ask Spreads, Forecasting, High-Frequency Data, Stock Market Liquidity, Count Data Time Series, Long Memory Poisson Autoregression

JEL Classification: G14, C32

Suggested Citation

Groß-Klußmann, Axel and Hautsch, Nikolaus, Predicting Bid-Ask Spreads Using Long Memory Autoregressive Conditional Poisson Models (July 12, 2011). Available at SSRN: https://ssrn.com/abstract=1884237 or http://dx.doi.org/10.2139/ssrn.1884237

Axel Groß-Klußmann (Contact Author)

Humboldt Universität zu Berlin ( email )

Alexanderstr 5
Berlin, Berlin 10178
Germany

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 )

Grüneburgplatz 1
Frankfurt am Main, 60323
Germany

Vienna Graduate School of Finance (VGSF) ( email )

Welthandelsplatz 1
Vienna, 1020
Austria

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