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Forecasting VaR using analytic higher moments for GARCH processes

Alexander, Carol; Lazar, Emese; Stanescu, Silvia - In: International review of financial analysis 30 (2013), pp. 36-45

Persistent link: https://www.econbiz.de/10010460001

Does high-frequency crude oil futures data contain useful information for predicting volatility in the US stock market? : new evidence

Wang, Jiqian; Huang, Yisu; Ma, Feng; Chevallier, Julien - In: Energy economics 91 (2020), pp. 1-13

Persistent link: https://www.econbiz.de/10012518664

Trading and non-trading period realized market volatility : does it matter for forecasting the volatility of US stocks?

Lyócsa, Štefan; Todorova, Neda - In: International journal of forecasting 36 (2020) 2, pp. 628-645

Persistent link: https://www.econbiz.de/10012415313

Autoregressive conditional heteroscedasticity and USA inflation

Bairam, Erkin İbrahim - 1992

Persistent link: https://www.econbiz.de/10000836507

Likelihood-based estimation of latent general ARCH structures

Fiorentini, Gabriele (contributor); … - 2004 - [Elektronische Ressource]

Persistent link: https://www.econbiz.de/10003179153

Extremal behavior of finite EGARCH processes

Lindner, Alexander M. (contributor); … - 2003

Extreme value theory for a class of EGARCH processes is developed. It is shown that the EGARCH process as well as the logarithm of its conditional variance lie in the domain of attraction of the Gumbel distribution. Norming constants are obtained and it is shown that the considered processes...