Self-weighted recursive estimation of GARCH models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F18%3A10382855" target="_blank" >RIV/00216208:11320/18:10382855 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1080/03610918.2015.1053924" target="_blank" >https://doi.org/10.1080/03610918.2015.1053924</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/03610918.2015.1053924" target="_blank" >10.1080/03610918.2015.1053924</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Self-weighted recursive estimation of GARCH models

Popis výsledku v původním jazyce

The generalized autoregressive conditional heteroscedasticity (GARCH) processes are frequently used to investigate and model financial returns. They are routinely estimated by computationally complex off-line estimation methods, for example, by the conditional maximum likelihood procedure. However, in many empirical applications (especially in the context of high-frequency financial data), it seems necessary to apply numerically more effective techniques to calibrate and monitor such models. The aims of this contribution are: (i)to review the previously introduced recursive estimation algorithms and to derive self-weighted alternatives applying general recursive identification instruments, and (ii)to examine these methods by means of simulations and an empirical application.

Název v anglickém jazyce

Self-weighted recursive estimation of GARCH models

Popis výsledku anglicky

The generalized autoregressive conditional heteroscedasticity (GARCH) processes are frequently used to investigate and model financial returns. They are routinely estimated by computationally complex off-line estimation methods, for example, by the conditional maximum likelihood procedure. However, in many empirical applications (especially in the context of high-frequency financial data), it seems necessary to apply numerically more effective techniques to calibrate and monitor such models. The aims of this contribution are: (i)to review the previously introduced recursive estimation algorithms and to derive self-weighted alternatives applying general recursive identification instruments, and (ii)to examine these methods by means of simulations and an empirical application.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

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

2018

Kód důvěrnosti údajů

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

Název periodika

Communications in Statistics Part B: Simulation and Computation

ISSN

0361-0918

e-ISSN

—

Svazek periodika

47

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

315-328

Kód UT WoS článku

000424159000001

EID výsledku v databázi Scopus

2-s2.0-85038364267