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A mathematical resurgence of risk management: an extreme modeling of expert opinions

Guegan, Dominique; Hassani, Bertrand - HAL - 2011

The Operational Risk Advanced Measurement Approach requires financial institutions to use scenarios to model these risks and to evaluate the pertaining capital charges. Considering that a banking group is composed of numerous entities (branches and subsidiaries), and that each one of them is...

Persistent link: https://www.econbiz.de/10011025772

Climate policies as a hedge against the uncertainty on future oil supply

Rozenberg, Julie; Hallegatte, Stéphane; Vogt-Schilb, Adrien - HAL - 2010

Despite the inextricable link between oil scarcity and climate change, the interplay between these two issues is paradoxically an underworked area. This article uses a global energy-economy model to address the link between future oil supply and climate change and assesses in a common framework...

Persistent link: https://www.econbiz.de/10009647577

Climate policies as a hedge against the uncertainty on future oil supply

Rozenberg, Julie; Hallegatte, Stéphane; Vogt-Schilb, Adrien - HAL - 2010

Persistent link: https://www.econbiz.de/10010738735

Modeling Multivariate Interest Rates using Time-Varying Copulas and Reducible Stochastic Differential Equations

Bu, Ruijun; Giet, Ludovic; Hadri, Kaddour; Lubrano, Michel - HAL - 2009

structure of the two rates using Patton (2006a) time-varying Symmetrised Joe-Clayton copula. We find evidence of asymmetric …

Persistent link: https://www.econbiz.de/10008793845

Operational risk: A Basel II++ step before Basel III

Guegan, Dominique; Hassani, Bertrand - HAL - 2011

Following Banking Committee on Banking Supervision, operational risk quantification is based on the Basel matrix which enables sorting incidents. In this paper, we deeply analyze these incidents and propose strategies for carrying out the supervisory guidelines proposed by the regulators. The...

Persistent link: https://www.econbiz.de/10010635043

Extreme values of random or chaotic discretization steps

Garcin, Matthieu; Guegan, Dominique - HAL - 2012

By sorting independent random variables and considering the difference between two consecutive order statistics, we get random variables, called steps or spacings, that are neither independent nor identically distributed. We characterize the probability distribution of the maximum value of these...

Persistent link: https://www.econbiz.de/10010635212

Pricing bivariate option under GARCH-GH model with dynamic copula: application for Chinese market

Guegan, Dominique; Zhang, Jing - HAL - 2009

is used for innovations. As the association between the underlying assets may vary over time, the dynamic copula approach …-GH model with time-varying copula differ substantially from the prices implied by the GARCH-Gaussian dynamic copula model …

Persistent link: https://www.econbiz.de/10010738494

Pricing bivariate option under GARCH processes with time-varying copula

Zhang, Jing; Guegan, Dominique - HAL - 2008

Heteroskedastic (GARCH) process. As the association between the underlying assets may vary over time, the dynamic copula with time …-varying parameter offers a better alternative to any static model for dependence structure and even to the dynamic copula model … Shanghai and Shenzhen Stock Composite Indexes. Results show that the option prices obtained by the time-varying copula model …

Persistent link: https://www.econbiz.de/10010738655

Non-stationarity and meta-distribution

Guegan, Dominique - HAL - 2008

In this paper we deal with the problem of non-stationarity encountered in a lot of data sets, mainly in financial and economics domains, coming from the presence of multiple seasonnalities, jumps, volatility, distorsion, aggregation, etc. Existence of non-stationarity involves spurious behaviors...

Persistent link: https://www.econbiz.de/10010750362

Global and local stationary modelling in finance : theory and empirical evidence

Guegan, Dominique - HAL - 2007

In this paper we deal with the problem of non-stationarity encountered in a lot of data sets coming from existence of multiple seasonnalities, jumps, volatility, distorsion, aggregation, etc. We study the problem caused by these non stationarities on the estimation of the sample autocorrelation...

Persistent link: https://www.econbiz.de/10010750670