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Title:Predicting bid-ask spreads using long memory autoregressive conditional poisson models PDF Logo
Authors:Groß-Klußmann, Axel
Hautsch, Nikolaus
Issue Date:2011
Series/Report no.:SFB 649 discussion paper 2011-044
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.
Subjects:bid-ask spreads
forecasting
high-frequency data
stock market liquidity
count data time series
long memory Poisson autoregression
JEL:G14
C32
Document Type:Working Paper
Appears in Collections:SFB 649 Discussion Papers, HU Berlin

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