Predicting bid-ask spreads using long memory autoregressive conditional poisson models

Axel Groß-Klußmann and Nikolaus Hautsch

No 2011-044, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk

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 (search for similar items in EconPapers)
JEL-codes: C32 G14 (search for similar items in EconPapers)
Date: 2011
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Journal Article: Predicting Bid–Ask Spreads Using Long‐Memory Autoregressive Conditional Poisson Models (2013)
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