Arbeitspapier
Predicting bid-ask spreads using long memory autoregressive conditional poisson models
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: SFB 649 Discussion Paper ; No. 2011-044
- Klassifikation
-
Wirtschaft
Information and Market Efficiency; Event Studies; Insider Trading
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- Thema
-
bid-ask spreads
forecasting
high-frequency data
stock market liquidity
count data time series
long memory Poisson autoregression
Bid-Ask Spread
Prognoseverfahren
Zeitreihenanalyse
Statistische Verteilung
Autokorrelation
Theorie
Schätzung
Aktienmarkt
Marktliquidität
USA
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Groß-Klußmann, Axel
Hautsch, Nikolaus
- Ereignis
-
Veröffentlichung
- (wer)
-
Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
- (wo)
-
Berlin
- (wann)
-
2011
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Groß-Klußmann, Axel
- Hautsch, Nikolaus
- Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk
Entstanden
- 2011
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