On conditional covariance modelling: An approach using state space models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F16%3A10329024" target="_blank" >RIV/00216208:11320/16:10329024 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.csda.2014.09.019" target="_blank" >http://dx.doi.org/10.1016/j.csda.2014.09.019</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.csda.2014.09.019" target="_blank" >10.1016/j.csda.2014.09.019</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On conditional covariance modelling: An approach using state space models

Popis výsledku v původním jazyce

A novel approach to conditional covariance modelling is introduced in the context of multivariate financial time series analysis. In particular, a class of multivariate generalized autoregressive conditional heteroscedasticity models is proposed. The suggested modelling technique is based on a specific dynamic orthogonal transformation derived by the LDL factorization of the conditional covariance matrix. An observed time series is transformed into a particular form that can be further treated by means of a discrete-time state space model under corresponding assumptions. The calibration can be performed by the associated Kalman recursive formulas, which are numerically effective. The introduced procedure has been investigated by extensive Monte Carlo experiments and empirical financial applications; it has been compared with other methods commonly used in this framework. The outlined methodology has demonstrated its capabilities, and it seems to be at least competitive in this field of research.

Název v anglickém jazyce

On conditional covariance modelling: An approach using state space models

Popis výsledku anglicky

A novel approach to conditional covariance modelling is introduced in the context of multivariate financial time series analysis. In particular, a class of multivariate generalized autoregressive conditional heteroscedasticity models is proposed. The suggested modelling technique is based on a specific dynamic orthogonal transformation derived by the LDL factorization of the conditional covariance matrix. An observed time series is transformed into a particular form that can be further treated by means of a discrete-time state space model under corresponding assumptions. The calibration can be performed by the associated Kalman recursive formulas, which are numerically effective. The introduced procedure has been investigated by extensive Monte Carlo experiments and empirical financial applications; it has been compared with other methods commonly used in this framework. The outlined methodology has demonstrated its capabilities, and it seems to be at least competitive in this field of research.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BA - Obecná matematika

OECD FORD obor

—

Projekt

<a href="/cs/project/GBP402%2F12%2FG097" target="_blank" >GBP402/12/G097: DYME-Dynamické modely v ekonomii</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Computational Statistics and Data Analysis

ISSN

0167-9473

e-ISSN

—

Svazek periodika

2016

Číslo periodika v rámci svazku

100

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

14

Strana od-do

304-317

Kód UT WoS článku

000378368100019

EID výsledku v databázi Scopus

2-s2.0-84979729497