English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

A Hawkes process model with a time-varying background rate is developed foranalyzing the high-frequency financial data. In our model, the logarithm of thebackground rate is modeled by a linear model with a relatively large number ofvariable-width basis functions, and the parameters are estimated by a Bayesianmethod. Our model can capture not only the slow time-variation, such as in theintraday seasonality, but also the rapid one, which follows a macroeconomicnews announcement. By analyzing the tick data of the Nikkei 225 mini, we findthat (i) our model is better fitted to the data than the Hawkes models with aconstant background rate or a slowly varying background rate, which have beencommonly used in the field of quantitative finance; (ii) the improvement in thegoodness-of-fit to the data by our model is significant especially for sessionswhere considerable fluctuation of the background rate is present; and (iii) ourmodel is statistically consistent with the data. The branching ratio, whichquantifies the level of the endogeneity of markets, estimated by our model is0.41, suggesting the relative importance of exogenous factors in the marketdynamics. We also demonstrate that it is critically important to appropriatelymodel the time-dependent background rate for the branching ratio estimation.

Hawkes process model with a time-dependent background rate and its application to high-frequency financial data