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

Chen, J. ORCID: https://orcid.org/0000-0001-7135-2116, 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. 10.1080/14697688.2017.1403140

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Abstract

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
Date Type:	Publication
Status:	Published
Schools:	Mathematics
Subjects:	H Social Sciences > HA Statistics H Social Sciences > HG Finance Q Science > QA Mathematics
Additional Information:	Special Issue on ‘Hawkes Processes in Finance’
Publisher:	Taylor & Francis (Routledge)
ISSN:	1469-7688
Date of First Compliant Deposit:	15 December 2017
Date of Acceptance:	1 November 2017
Last Modified:	07 Nov 2023 05:51
URI:	https://orca.cardiff.ac.uk/id/eprint/107375

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