Estimation of an Order Book Dependent Hawkes Process for Large Datasets.
A point process for event arrivals in high-frequency trading is presented. The intensity is the product of a Hawkes process and high-dimensional functions of covariates derived from the order book. Conditions for stationarity of the process are stated. An algorithm is presented to estimate the model...
| Publicado en: | Journal of Financial Econometrics Vol. 22; no. 4; pp. 1098 - 1130 |
|---|---|
| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Oxford University Press / USA
Fall2024
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=179375730&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 179375730 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 14798409 T2Y jtl: Journal of Financial Econometrics issn: 14798409 maglogo: N pubinfo: dt: Fall2024 vid: 22 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 179375730 10.1093/jjfinec/nbad021 ppf: 1098 ppct: 32 formats: tig: atl: Estimation of an Order Book Dependent Hawkes Process for Large Datasets. aug: au: Mucciante, Luca Sancetta, Alessio affil: Department of Economics, Royal Holloway University of London , Egham, TW20 0EX, UK su: Forecasting Sampling (Process) Point processes Sample size (Statistics) Stocks (Finance) sug: subj: Forecasting Sampling (Process) Point processes Sample size (Statistics) Stocks (Finance) keyword: C13 C32 C55 counting process forecast evaluation high-dimensional estimation high-frequency trading one-hot encoding trade arrival C13 C32 C55 counting process forecast evaluation high-dimensional estimation high-frequency trading one-hot encoding trade arrival ab: A point process for event arrivals in high-frequency trading is presented. The intensity is the product of a Hawkes process and high-dimensional functions of covariates derived from the order book. Conditions for stationarity of the process are stated. An algorithm is presented to estimate the model even in the presence of billions of data points, possibly mapping covariates into a high-dimensional space. Large sample sizes can be common for high-frequency data applications using multiple instruments. Consistency results under weak conditions are established. A test statistic to assess out of sample performance of different model specifications is suggested. The methodology is applied to the study of four stocks that trade on the New York Stock Exchange. The out of sample testing procedure suggests that capturing the nonlinearity of the order book information adds value to the self-exciting nature of high-frequency trading events. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|