Performance of information criteria for selection of Hawkes process models of financial data
测试三种常见信息准则（AIC、BIC、Hannan-Quinn）在霍克斯过程模型阶数选择中的表现，通过模拟数据衡量正确选择模型的成功率，并分析样本量对选择准确性的影响。
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, the Bayesian information criterion and the Hannan–Quinn criterion. 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.