The Extreme Value Theory and Copulas as a Tool to Measure Market Risk

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11230%2F12%3A10109686" target="_blank" >RIV/00216208:11230/12:10109686 - 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

The Extreme Value Theory and Copulas as a Tool to Measure Market Risk

Popis výsledku v původním jazyce

Assessing the extreme events is crucial in financial risk management. All risk managers and financial institutions want to know the risk of their portfolio under rare events scenarios. We illustrate a multivariate market risk estimating method which employs Monte Carlo simulations to estimate Value-at-Risk (VaR) for a portfolio of 4 stock exchange indexes from Central Europe. The method uses the non-parametric empirical distribution to capture small risks and the parametric Extreme Value Theory to capture large and rare risks. We compare estimates of this method with historical simulation and variance-covariance method under low and high volatility samples of data. In general historical simulation method gives higher estimates of VaR for extreme events, while variance-covariance lower. The method that we illustrate gives a result in between the two because it considers historical performance of the stocks and also corrects for the heavy tails of the distribution. We conclude that the e

Název v anglickém jazyce

The Extreme Value Theory and Copulas as a Tool to Measure Market Risk

Popis výsledku anglicky

Assessing the extreme events is crucial in financial risk management. All risk managers and financial institutions want to know the risk of their portfolio under rare events scenarios. We illustrate a multivariate market risk estimating method which employs Monte Carlo simulations to estimate Value-at-Risk (VaR) for a portfolio of 4 stock exchange indexes from Central Europe. The method uses the non-parametric empirical distribution to capture small risks and the parametric Extreme Value Theory to capture large and rare risks. We compare estimates of this method with historical simulation and variance-covariance method under low and high volatility samples of data. In general historical simulation method gives higher estimates of VaR for extreme events, while variance-covariance lower. The method that we illustrate gives a result in between the two because it considers historical performance of the stocks and also corrects for the heavy tails of the distribution. We conclude that the e

Druh

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

CEP obor

AH - Ekonomie

OECD FORD obor

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Projekt

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Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název periodika

Bulletin of the Czech Econometric Society

ISSN

1212-074X

e-ISSN

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Svazek periodika

19

Číslo periodika v rámci svazku

29

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

21

Strana od-do

70-90

Kód UT WoS článku

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EID výsledku v databázi Scopus

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