Performance of information criteria for selection of Hawkes process models of financial data

Chen, J M, Hawkes, A G, Scalas, E and Trinh, M (2018) Performance of information criteria for selection of Hawkes process models of financial data. Quantitative Finance, 18 (2). pp. 225-235. ISSN 1469-7688

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We test three common information criteria (IC) for selecting the order of a Hawkes process with an intensity kernel that can be expressed as a mixture of exponential terms. These processes find application in high-frequency financial data modelling. The information criteria are Akaike's information criterion (AIC), the Bayesian information criterion (BIC) and the Hannan-Quinn criterion (HQ). Since we work with simulated data, we are able to measure the performance of model selection by the success rate of the IC in selecting the model that was used to generate the data. In particular, we are interested in the relation between correct model selection and underlying sample size. The analysis includes realistic sample sizes and parameter sets from recent literature where parameters were estimated using empirical financial intra-day data. We compare our results to theoretical predictions and similar empirical findings on the asymptotic distribution of model selection for consistent and inconsistent IC.

Item Type: Article
Keywords: Hawkes process, self-exciting process, model selection, information criterion, AIC, BIC, HQ
Schools and Departments: School of Mathematical and Physical Sciences > Mathematics
Research Centres and Groups: Probability and Statistics Research Group
Subjects: Q Science > QA Mathematics > QA0273 Probabilities. Mathematical statistics
Depositing User: Enrico Scalas
Date Deposited: 06 Nov 2017 12:08
Last Modified: 21 Sep 2018 11:56

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