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

By Axel Groß-Klußmann and Nikolaus Hautsch

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

Topics: forecasting, high-frequency data, Bid-ask spreads, count data time series, long memory Poisson autoregression, stock market liquidity, 330 Wirtschaft, ddc:330
Publisher: Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
Year: 2011
DOI identifier: 10.18452/4332
OAI identifier: oai:edoc.hu-berlin.de:18452/4984
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