Stable Modeling of Market and Credit Value at Risk

Rachev, Svetlozar T.; Khindanova, Irina

doi:10.1016/b978-044450896-6.50009-1

Cited by 38 publications

(12 citation statements)

References 39 publications

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“…Recently, Rachev, Schwartz and Khindanova (2000) have proposed a conditional model for the vector of centered continuously compounded returns Zt = [Z1 ,t, ..., Zn,tl' which is assumed to be in the domain of attraction of an (eq , ..., an)-stable law. In particular they assume that…”

Section: K-k (Zit-k+k-biyt-k+k) (Zjt-k+k-bjyt-k+k)(p-i))mentioning

confidence: 99%

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Handbook of Computational and Numerical Methods in Finance

Rachev¹

2004

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Section: K-k (Zit-k+k-biyt-k+k) (Zjt-k+k-bjyt-k+k)(p-i))mentioning

confidence: 99%

“…Rachev and Mittnik [2000], Rachev, SchwartzandKhindanova [2000] are among others that have appliedstable Paretianmodels to financial asset returns.4 discuss this in detail.…”

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confidence: 99%

Handbook of Computational and Numerical Methods in Finance

Rachev¹

2004

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“…Whereas the backtesting exceptions are determined by the one-step VaR, the regulatory capital is determined by the 10-step VaR in the Basel II framework. The normal and the stable distributions are the only distributions that fulfill the additivity property (Rachev et al 2003(Rachev et al , 2007. Due to this property, the 1% quantile of the 10-step return distribution can also be determined exactly at Q theo 0:01,10 ¼ À0:2150 for the stable and Q theo 0:01,10 ¼ À0:1132 for the normal distribution.…”

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confidence: 97%

The impact of the choice of VaR models on the level of regulatory capital according to Basel II

Hermsen

2010

Quantitative Finance

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The Basel II framework allows the calculation of the capital requirements for market risk with Value-at-Risk models. Since no special model is prescribed in the framework, banks may use simple models with questionable assumptions concerning their underlying distributions. Our numerical analysis reveals that simple VaR models that perform noticeably worse than comparable simple models with more realistic assumptions may lead to a lower level of regulatory capital for banks. For this reason, banks have a major incentive to implement bad models. This is obviously contrary to the interests of regulatory authorities.Value at Risk, Non-Gaussian distributions, Structure of financial markets, Risk management, Risk measures, Numerical simulation, Extreme risk and insurance, Market risk,

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“…There is a huge literature on this subject [22], and we give here the main lines with a focus on univariate returns. Regarding stochastic processes in continuous time, various authors considered jump diffusion models [23], variance Gamma processes [24] and subordinated processes [11], and SDE whose invariant distributions are fat-tailed [25,26] or present moments explosions [27,Chapter 7].…”

Section: Commonly Used Models For Heavy Tailsmentioning

confidence: 99%

Regime switching model for financial data: Empirical risk analysis

Salhi

Deaconu

Lejay

et al. 2016

Physica A: Statistical Mechanics and its Applications

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h i g h l i g h t s• This paper introduces a regime switching model for Value-at-Risk estimation.• Hidden Markov models and extreme value theory are combined into a hybrid model.• The regime switching model is applied to real data NYSE Euronext stocks.• Classifying data in two states permits a fast detection of regime switching. This paper constructs a regime switching model for the univariate Value-at-Risk estimation. Extreme value theory (EVT) and hidden Markov models (HMM) are combined to estimate a hybrid model that takes volatility clustering into account. In the first stage, HMM is used to classify data in crisis and steady periods, while in the second stage, EVT is applied to the previously classified data to rub out the delay between regime switching and their detection. This new model is applied to prices of numerous stocks exchanged on NYSE Euronext Paris over the period 2001-2011. We focus on daily returns for which calibration has to be done on a small dataset. The relative performance of the regime switching model is benchmarked against other well-known modeling techniques, such as stable, power laws and GARCH models. The empirical results show that the regime switching model increases predictive performance of financial forecasting according to the number of violations and tail-loss tests. This suggests that the regime switching model is a robust forecasting variant of power laws model while remaining practical to implement the VaR measurement.

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Stable Modeling of Market and Credit Value at Risk

Cited by 38 publications

References 39 publications

Handbook of Computational and Numerical Methods in Finance

Handbook of Computational and Numerical Methods in Finance

The impact of the choice of VaR models on the level of regulatory capital according to Basel II

Regime switching model for financial data: Empirical risk analysis

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