Quantitative trading and investment decision making are intricate financial tasks that rely on
accurate stock selection. Despite advances in deep learning that have made significant …

A temporal point process is a mathematical model for a time series of discrete events, which
covers various applications. Recently, recurrent neural network (RNN) based models have …

Social goods, such as healthcare, smart city, and information networks, often produce
ordered event data in continuous time. The generative processes of these event data can be …

We study statistical aspects of state-dependent Hawkes processes, which are an extension
of Hawkes processes where a self-and cross-exciting counting process and a state process …

The endo–exo problem lies at the heart of statistical identification in many fields of science,
and is often plagued by spurious strong-and-long memory due to improper treatment of …

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 …

We investigate the valuation of volatility index (VIX) options by developing a model with a
self‐exciting Hawkes process that allows for clustering in the VIX. In the proposed …

The statistical estimate of the branching ratio η of the Hawkes model, when fitted to windows
of mid-price changes, has been reported to approach criticality (η= 1) as the fitting window …

Transactions in financial markets are not evenly spaced but can be concentrated within a
short period of time. In this study, we investigated the factors that determine the transaction …

Here we used the recursive point process and SEIR model to describe the transmission
dynamics of pertussis. The recursive point process is a refined version of the Hawkes point …