Recursive estimation of the multivariate EWMA process

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10400163" target="_blank" >RIV/00216208:11320/19:10400163 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.18267/pr.2019.los.186.46" target="_blank" >http://dx.doi.org/10.18267/pr.2019.los.186.46</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18267/pr.2019.los.186.46" target="_blank" >10.18267/pr.2019.los.186.46</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Recursive estimation of the multivariate EWMA process

Popis výsledku v původním jazyce

Recursive estimation methods suitable for univariate GARCH models have been recently studied in the literature. They undoubtedly represent attractive alternatives to the standard non-recursive estimation procedures with many practical applications (especially in the context of high-frequency financial data). It might be truly advantageous to adopt numerically effective techniques that can estimate, monitor, and control such models in real time. The aim of this contribution is to extend this methodology to the multivariate EMWA process by applying general recursive estimation instruments. The multivariate exponentially weighted moving average (MEWMA) model is a particular modelling scheme advocated by RiskMetrics that is capable of predicting the current level of financial time series covolatilities. In particular, the suggested approach seems to be useful for various multivariate financial time series with (conditionally) correlated components. Monte Carlo experiments are performed in order to investigate statistic features of the proposed estimation algorithm. Moreover, an empirical financial analysis demonstrates its capability.

Název v anglickém jazyce

Recursive estimation of the multivariate EWMA process

Popis výsledku anglicky

Recursive estimation methods suitable for univariate GARCH models have been recently studied in the literature. They undoubtedly represent attractive alternatives to the standard non-recursive estimation procedures with many practical applications (especially in the context of high-frequency financial data). It might be truly advantageous to adopt numerically effective techniques that can estimate, monitor, and control such models in real time. The aim of this contribution is to extend this methodology to the multivariate EMWA process by applying general recursive estimation instruments. The multivariate exponentially weighted moving average (MEWMA) model is a particular modelling scheme advocated by RiskMetrics that is capable of predicting the current level of financial time series covolatilities. In particular, the suggested approach seems to be useful for various multivariate financial time series with (conditionally) correlated components. Monte Carlo experiments are performed in order to investigate statistic features of the proposed estimation algorithm. Moreover, an empirical financial analysis demonstrates its capability.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA17-00676S" target="_blank" >GA17-00676S: Dynamické modely rizika ve financích a pojišťovnictví</a><br>

Návaznosti

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

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

The 13th International Days of Statistics and Economics

ISBN

978-80-87990-18-6

ISSN

—

e-ISSN

—

Počet stran výsledku

10

Strana od-do

464-473

Název nakladatele

Melandrium

Místo vydání

Prague

Místo konání akce

Prague

Datum konání akce

5. 9. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000589182000047