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Extreme value behavior of aggregate dependent risks
Chen, Die; Mao, Tiantian; Pan, Xiaoqing; Hu, Taizhong - In: Insurance / Mathematics & economics 50 (2012) 1, pp. 99-108
Persistent link: https://www.econbiz.de/10009501695
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Extreme value theory in emerging markets : evidence from the Montenegrin stock exchange
Cerovic Smolovic, Julija; Karadžić, Vesna - In: Economic annals 60 (2015) 206, pp. 87-116
Persistent link: https://www.econbiz.de/10011421685
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Estimating the tails of loss severity via conditional risk measures for the family of symmetric generalised hyperbolic distributions
Ignatieva, Ekaterina; Landsman, Zinoviy - In: Insurance / Mathematics & economics 65 (2015), pp. 172-186
Persistent link: https://www.econbiz.de/10011428649
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Value-at-risk and extreme returns
Daníelsson, Jón; Vries, Casper G. de - 1997
Accurate prediction of the frequency of extreme events is of primary importance in many financialapplications such as Value-at-Risk (VaR) analysis. We propose a semi-parametric method for VaRevaluation. The largest risks are modelled parametrically, while smaller risks are captured by the...
Persistent link: https://www.econbiz.de/10010533206
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Forecasting VaR models under different volatility processes and distributions of return innovations
Dendramis, Yiannis; Spungin, Giles E.; Tzavalis, Elias - In: Journal of forecasting 33 (2014) 7, pp. 515-531
Persistent link: https://www.econbiz.de/10011282095
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Abnormal returns, risk, and options in large data sets
Caserta, Silvia; Daníelsson, Jón; Vries, Casper G. de - 1998
Large data sets in finance with millions of observations have becomewidely available. Such data sets enable the construction of reliablesemi-parametric estimates of the risk associated with extreme pricemovements. Our approach is based on semi-parametric statisticalextreme value analysis, and...
Persistent link: https://www.econbiz.de/10011299966
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High-order moments and extreme value approach for value-at-risk
Lin, Chu-Hsiung; Changchien, Chang-Cheng; Kao, Tzu-Chuan; … - In: Journal of empirical finance 29 (2014), pp. 421-434
Persistent link: https://www.econbiz.de/10011300450
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Tail behavior of credit loss distributions for general latent factor models
Lucas, André; Klaassen, Pieter; Spreij, Peter; … - 2001
distributions of both risk factors are thin-tailed,the credit loss distribution may have a finite tail index (polynomial tails … loss. This unconventionalbehaviour of the credit loss density has not been reported earlier in theliterature. We also … derive analytically the interaction between portfolioquality and credit loss tail behavior and find a striking difference …
Persistent link: https://www.econbiz.de/10011316891
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Construction and backtesting of a multi-factor stress-scenario for the stock market
Boldyrev, Kirill; Andrianov, Dmitry; Ivliev, Sergey - In: Financial econometrics and empirical market microstructure, (pp. 37-45). 2015
Persistent link: https://www.econbiz.de/10011326724
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GARCH models, tail indexes and error distributions : an empirical investigation
Šopov, Boril; Horváth, Roman - 2015
We perform a large simulation study to examine the extent to which various generalized autoregressive conditional heteroskedasticity (GARCH) models capture extreme events in stock market returns. We estimate Hill's tail indexes for individual S&P 500 stock market returns ranging from 1995-2014...
Persistent link: https://www.econbiz.de/10010529886