๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

High-frequency financial data modeling using Hawkes processes

โœ Scribed by V. Chavez-Demoulin; J.A. McGill


Book ID
118470668
Publisher
Elsevier Science
Year
2012
Tongue
English
Weight
914 KB
Volume
36
Category
Article
ISSN
0378-4266

No coin nor oath required. For personal study only.


๐Ÿ“œ SIMILAR VOLUMES


Forecasting using high-frequency data: a
โœ Qi Zhang; Charlie X Cai; Kevin Keasey ๐Ÿ“‚ Article ๐Ÿ“… 2009 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 123 KB

## Abstract The first purpose of this paper is to assess the shortโ€run forecasting capabilities of two competing financial duration models. The forecast performance of the Autoregressive Conditional Multinomialโ€“Autoregressive Conditional Duration (ACMโ€ACD) model is better than the Asymmetric Autore

Forecasting high-frequency financial dat
โœ Michael A. Hauser; Robert M. Kunst ๐Ÿ“‚ Article ๐Ÿ“… 2001 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 168 KB

## Abstract Financial data series are often described as exhibiting two nonโ€standard time series features. First, variance often changes over time, with alternating phases of high and low volatility. Such behaviour is well captured by ARCH models. Second, long memory may cause a slower decay of the

Modeling financial time series using ARM
โœ Benjamin Melamed ๐Ÿ“‚ Article ๐Ÿ“… 2001 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 866 KB

The class of ARM (Auto-Regressive Modular) processes is a versatile class of nonlinear autoregressive schemes with modulo-1 reduction and additional transformations. It generalizes the class of TES (Transform-Expand-Sample) processes in that it admits dependent innovation sequences. Both TES and ARM

Dynamical structures of high-frequency f
โœ Kyungsik Kim; Seong-Min Yoon; SooYong Kim; Ki-Ho Chang; Yup Kim; Sang Hoon Kang ๐Ÿ“‚ Article ๐Ÿ“… 2007 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 156 KB