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Value-at-Risk-Schätzung mit Gauß'schen Mischverteilungen und künstlichen neuronalen Netzen

Prinzler, Ralf - 2001

Persistent link: https://www.econbiz.de/10001583953

Extreme value statistics using related variables

Ahmed, Hanan - 2022

Persistent link: https://www.econbiz.de/10013263346

Efficient density estimation and value at risk using Fejér-type kernel functions

Kosta, Olga; Stepanova, Natalia - In: Journal of mathematical finance 5 (2015) 5, pp. 480-504

Persistent link: https://www.econbiz.de/10011440699

Estimating risk on the capital market with VaR method

Bogdan, Sinisa; Baresa, Suzana; Ivanovic, Zoran - In: UTMS journal of economics / University of Tourism and … 6 (2015) 1, pp. 165-175

The two basic questions that every investor tries to answer before investment are questions about predicting return and risk. Risk and return are generally considered two positively correlated sizes, during the growth of risk it is expected increase of return to compensate the higher risk. The...

Persistent link: https://www.econbiz.de/10011299237

A Bayesian encompassing test using combined value-at-risk estimates

Tsiotas, Georgios - In: Quantitative finance 18 (2018) 3, pp. 395-417

Persistent link: https://www.econbiz.de/10011906387

Bootstrap consistency and bias correction in the nonparametric estimation of risk measures of collective risks

Lauer, Alexandra; Zähle, Henryk - In: Insurance / Mathematics & economics 74 (2017), pp. 99-108

Persistent link: https://www.econbiz.de/10011712409

Comparison of methods for estimating the uncertainty of value at risk?

Gamba Santamaría, Santiago; Jaulín Méndez, Oscar Fernando - 2016

Persistent link: https://www.econbiz.de/10011580566

GARCH modelling of cryptocurrencies

Chu, Jeffrey; Chan, Stephen; Nadarajah, Saralees; … - In: Journal of risk and financial management : JRFM 10 (2017) 4, pp. 1-15

With the exception of Bitcoin, there appears to be little or no literature on GARCH modelling of cryptocurrencies. This paper provides the first GARCH modelling of the seven most popular cryptocurrencies. Twelve GARCH models are fitted to each cryptocurrency, and their fits are assessed in terms...

Persistent link: https://www.econbiz.de/10011854856

The use of the tail dependence function for high quantile risk measure analysis : an application to portfolio optimization

Salazar Flores, Yuri; Díaz Hernández, Adán; … - In: Applied economics 55 (2023) 37, pp. 4289-4303

Persistent link: https://www.econbiz.de/10014301231

Structural electricity models and asymptotically normal estimators to quantify parameter risk

Harms, Cord; Kiesel, Rüdiger - In: Applied mathematical finance 26 (2019) 5, pp. 475-522

Persistent link: https://www.econbiz.de/10012210416