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Predicting Bid–Ask Spreads Using Long‐Memory Autoregressive Conditional Poisson Models
Author(s) -
GroßKlußMann Axel,
Hautsch Nikolaus
Publication year - 2013
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2267
Subject(s) - autoregressive fractionally integrated moving average , econometrics , bid price , autoregressive model , ask price , poisson distribution , transaction cost , economics , stock (firearms) , poisson regression , autocorrelation , computer science , financial economics , volatility (finance) , long memory , statistics , mathematics , finance , mechanical engineering , population , demography , sociology , engineering
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 AR, 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 14 % of spread transaction costs. Copyright © 2013 John Wiley & Sons, Ltd.