Modelling and forecasting with financial duration data using non-linear model

Pooi, Ah Hin * and Ng, Kok Haur and Soo, Huei Ching * (2016) Modelling and forecasting with financial duration data using non-linear model. Economic Computation and Economic Cybernetics Studies and Research, 50 (2). pp. 79-92. ISSN 1842–3264

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The class of autoregressive conditional duration (ACD) models plays an important role in modelling the duration data in economics and finance. This paper presents a non-linear model to allow the first four moments of the duration to depend nonlinearly on past information variables. Theoretically the model is more general than the linear ACD model. The proposed model is fitted to the data given by the 3534 transaction durations of IBM stock on five consecutive trading days. The fitted model is found to be comparable to the Weibull ACD model in terms of the in-sample and out-of-sample mean squared prediction errors and mean absolute forecast deviations. In addition, the Diebold-Mariano test shows that there are no significant differences in forecast ability for all models.

Item Type: Article
Additional Information: 2nd author is with Institute of Mathematical Sciences, University of Malaya; 3rd author is with School of Mathematical and Computer Sciences, Heriot-Watt University, Malaysia
Uncontrolled Keywords: Autoregressive conditional duration, multivariate quadratic-normal distribution, nonlinear dependence structure, duration model.
Subjects: H Social Sciences > HA Statistics
H Social Sciences > HG Finance
Divisions: Others > Non Sunway Academics
Sunway University > School of Mathematical Sciences > Department of Applied Statistics
Depositing User: Dr Janaki Sinnasamy
Date Deposited: 20 Dec 2016 09:07
Last Modified: 10 May 2019 08:55

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