Robust recursive estimation for financial time series

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F18%3A10386862" target="_blank" >RIV/00216208:11320/18:10386862 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Robust recursive estimation for financial time series

Popis výsledku v původním jazyce

The generalized autoregressive conditional heteroscedasticity (GARCH) process is a particular modelling scheme, which is capable of forecasting the current level of volatility of financial time series. Recently, recursive estimation methods suitable for this class of stochastic processes have been introduced in the literature. They undoubtedly represent attractive alternatives to the standard non-recursive estimation procedures with many practical applications. It is truly advantageous to adopt numerically effective estimation techniques that can estimate and control such models in real time. However, abnormal observations (outliers) may occur in data. They may be caused by many reasons, e.g. by additive errors, measurement failures or management actions. Exceptional data points will influence the model estimation considerably if no specific action is taken. The aim of this contribution is to propose and examine a robust recursive estimation algorithm suitable for GARCH models. It seems to be useful for various financial time series, in particular for (high-frequency) financial returns contaminated by additive outliers. The introduced algorithm can be effective in the risk control and regulation when the prediction of volatility is the main concern since it distinguishes and corrects outlaid bursts of volatility. Real data examples are presented.

Název v anglickém jazyce

Robust recursive estimation for financial time series

Popis výsledku anglicky

The generalized autoregressive conditional heteroscedasticity (GARCH) process is a particular modelling scheme, which is capable of forecasting the current level of volatility of financial time series. Recently, recursive estimation methods suitable for this class of stochastic processes have been introduced in the literature. They undoubtedly represent attractive alternatives to the standard non-recursive estimation procedures with many practical applications. It is truly advantageous to adopt numerically effective estimation techniques that can estimate and control such models in real time. However, abnormal observations (outliers) may occur in data. They may be caused by many reasons, e.g. by additive errors, measurement failures or management actions. Exceptional data points will influence the model estimation considerably if no specific action is taken. The aim of this contribution is to propose and examine a robust recursive estimation algorithm suitable for GARCH models. It seems to be useful for various financial time series, in particular for (high-frequency) financial returns contaminated by additive outliers. The introduced algorithm can be effective in the risk control and regulation when the prediction of volatility is the main concern since it distinguishes and corrects outlaid bursts of volatility. Real data examples are presented.

Druh

D - Stať ve sborníku

CEP obor

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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

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

Conference Proceedings

ISBN

978-80-87990-14-8

ISSN

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e-ISSN

neuvedeno

Počet stran výsledku

9

Strana od-do

563-571

Název nakladatele

Melandrium

Místo vydání

Praha

Místo konání akce

Praha

Datum konání akce

6. 9. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000455809400056