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Market risk management in a Post-Basel II regulatory environment

Urošević, Branko; Drenovak, Mikica; Ranković, Vladimir; … - 2016

We propose a novel method of Mean-Capital Requirement portfolio optimization. The optimization is performed using a parallel framework for optimization based on the Nondominated Sorting Genetic Algorithm II. Capital requirements for market risk include an additional stress component introduced...

Persistent link: https://www.econbiz.de/10011587953

High frequency vs. daily resolution : the economic value of forecasting volatility models

Lilla, Francesca - 2016

Forecasting-volatility models typically rely on either daily or high frequency (HF) data and the choice between these two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer of many limitations. HF data feature microstructure problem,...

Persistent link: https://www.econbiz.de/10011730304

High frequency vs. daily resolution : the economic value of forecasting volatility models 2nd ed

Lilla, Francesca - 2017

Forecasting volatility models typically rely on either daily or high frequency (HF) data and the choice between these two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer from many limitations. HF data feature microstructure problem,...

Persistent link: https://www.econbiz.de/10011674479

Strict stationarity testing and estimation of explosive ARCH models

Francq, Christian; Zakoian, Jean-Michel - Volkswirtschaftliche Fakultät, … - 2010

tests developed for the ARCH(1) model are able to detect non-stationarity in more general GARCH models. A numerical …

Persistent link: https://www.econbiz.de/10008560969

High Frequency vs. Daily Resolution : The Economic Value of Forecasting Volatility Models

Lilla, Francesca - 2016

Persistent link: https://www.econbiz.de/10014124325

Modelling Asymmetric Dependence of Financial Returns with Multivariate Dynamic Copulas

Braun, Valentin - 2016

We propose a multidimensional extension for Patton's (2006) bivariate Dynamic Copulas. We also introduce a Dynamic Mixture Copula whose parameters and weights follow well deﬁned dynamic processes. Both approaches are more ﬂexible to adapt to ﬁnancial data than currently available Copula...

Persistent link: https://www.econbiz.de/10012999941

Impact of Derivative Trading on Currency Market Volatility in India

Singh, Saurabh - 2015

underlying volatility GARCH (1, 1) model has been employed. The results indicate that the introduction of currency futures …

Persistent link: https://www.econbiz.de/10013028617

Modelling Stock Market Return Volatility : Evidence from India

Singh, Saurabh - 2016

Conditional Heteroscedastic (GARCH). For capturing the symmetric and asymmetric volatility GARCH-M (1, 1) and EGARCH (1, 1 … risk premium as per GARCH-M (1, 1) model. The asymmetric leverage effect captured by the parameter of EGARCH (1, 1) and …

Persistent link: https://www.econbiz.de/10012980061

Hybrid Quantile Regression Estimation for Time Series Models with Conditional Heteroscedasticity

Zheng, Yao - 2016

conditional heteroscedastic models, the generalized autoregressive conditional heteroscedastic (GARCH) process is the most popular … GARCH model, the linear GARCH model, owing to its tractable conditional quantile structure. This paper considers the widely … used GARCH model. An easy-to-implement hybrid conditional quantile estimation procedure is developed based on a simple …

Persistent link: https://www.econbiz.de/10012980638

Markov-Switching GARCH Models in R : The MSGARCH Package

Ardia, David - 2019

We describe the package MSGARCH, which implements Markov-switching GARCH models in R with efficient C++ object …-oriented programming. Markov-switching GARCH models have become popular methods to account for regime changes in the conditional variance …/Bayesian estimations of a very large class of Markov-switching GARCH-type models. The package also provides methods to make single-step and …

Persistent link: https://www.econbiz.de/10012902834