Forecasting Using High-Frequency Data: A Comparison of Asymmetric Financial Duration Models

Journal of Forecasting, Vol. 28, No. 5, pp. 371–386, August 2009

35 Pages Posted: 6 Oct 2010

See all articles by Qi Zhang

Qi Zhang

Independent

Kevin Keasey

University of Leeds - Division of Accounting and Finance

Charlie X. Cai

University of Liverpool Management School

Date Written: July 2008

Abstract

The first purpose of this paper is to assess the short-run forecasting capabilities of two competing financial duration models. The forecast performance of the Autoregressive Conditional Multinomial–Autoregressive Conditional Duration (ACM-ACD) model is better than the Asymmetric Autoregressive Conditional Duration (AACD) model. However, the ACM-ACD model is more complex in terms of the computational setting and is more sensitive to starting values. The second purpose is to examine the effects of market microstructure on the forecasting performance of the two models. The results indicate that the forecast performance of the models generally decreases as the liquidity of the stock increases, with the exception of the most liquid stocks. Furthermore, a simple filter of the raw data improves the performance of both models. Finally, the results suggest that both models capture the characteristics of the micro data very well with a minimum sample length of 20 days.

Keywords: Autoregressive Duration Model (ACD), forecasting, high-frequency data, market microstructure

JEL Classification: C1, C41, G1

Suggested Citation

Zhang, Qi and Keasey, Kevin and Cai, Charlie Xiaowu, Forecasting Using High-Frequency Data: A Comparison of Asymmetric Financial Duration Models (July 2008). Journal of Forecasting, Vol. 28, No. 5, pp. 371–386, August 2009. Available at SSRN: https://ssrn.com/abstract=1687000

Qi Zhang

Independent

No Address Available

Kevin Keasey

University of Leeds - Division of Accounting and Finance ( email )

Leeds LS2 9JT
United Kingdom
+44 (0)113 343 2618 (Phone)

Charlie Xiaowu Cai (Contact Author)

University of Liverpool Management School ( email )

University of Liverpool
Liverpool, L69 7ZA
United Kingdom

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