Comparing traditional VaR with copula based VaR in extreme market volatility : Empirical Evidence from Finland between 2004 and 2009
Haverinen, Patrik (2024-05-07)
Comparing traditional VaR with copula based VaR in extreme market volatility : Empirical Evidence from Finland between 2004 and 2009
(07.05.2024)
Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.
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Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2024051430562
https://urn.fi/URN:NBN:fi-fe2024051430562
Tiivistelmä
This thesis examines the effectiveness of copula-based Value at Risk (VaR) compared to
traditional VaR methods under extreme market volatility within the Finnish financial
markets from 2004 to 2009. Traditional VaR has been widely used in risk management
due to its simplicity; however, it often fails to capture the nonlinear dependencies,
tail risks and stylized facts in financial time series. This inadequacy is critical during periods of significant market stress, such as the 2007-2009 financial crisis.
To address these limitations, this study employs copula-based VaR, which integrates copulas to model dependency structures from the marginal distributions, potentially offering enhanced accuracy in risk assessment during volatile periods. Utilizing historical data from Finnish stock and bond markets, the research applies ARMA-GARCH models to estimate the marginal distributions and Monte Carlo simulations to compute VaR. The VaR forecasts are done in rolling fashion manner, imitating a more practical approach. The performance of both copula-based and traditional VaR models is tested with two backtesting methods.
The results indicate that copula-based VaR, particularly the
Student’s t copula, provides a more accurate and reliable measure of risk under extreme
market conditions compared to traditional VaR. These results not only underscore the potential limitations of traditional VaR in extreme market conditions but also highlight the advantages of copula-based approaches in enhancing risk assessment frameworks.
traditional VaR methods under extreme market volatility within the Finnish financial
markets from 2004 to 2009. Traditional VaR has been widely used in risk management
due to its simplicity; however, it often fails to capture the nonlinear dependencies,
tail risks and stylized facts in financial time series. This inadequacy is critical during periods of significant market stress, such as the 2007-2009 financial crisis.
To address these limitations, this study employs copula-based VaR, which integrates copulas to model dependency structures from the marginal distributions, potentially offering enhanced accuracy in risk assessment during volatile periods. Utilizing historical data from Finnish stock and bond markets, the research applies ARMA-GARCH models to estimate the marginal distributions and Monte Carlo simulations to compute VaR. The VaR forecasts are done in rolling fashion manner, imitating a more practical approach. The performance of both copula-based and traditional VaR models is tested with two backtesting methods.
The results indicate that copula-based VaR, particularly the
Student’s t copula, provides a more accurate and reliable measure of risk under extreme
market conditions compared to traditional VaR. These results not only underscore the potential limitations of traditional VaR in extreme market conditions but also highlight the advantages of copula-based approaches in enhancing risk assessment frameworks.