44 research outputs found
Forecasting of gold prices volatility with symmetric and asymmetric volatility models
No full textWith this paper the author forecasts the out-of-sample volatility of gold price changes in Turkey. Looking at the both the symmetric and the asymmetric evaluation criteria, GJR-GARCH model isthe best fitted model for forecasting gold price volatility in Turkey. The GJR-GARCH model findings reveal a negative shock asymmetry for gold prices. Thus, it shows that positive news in the market affects the volatility of gold prices in the next period more than negative news.peer-reviewe
Estimation of value at risk in currency exchange rate portfolio using asymmetric GJR-GARCH Copula
No full textModeling & testing for volatility of the monthly rate of return on the US() exchange rate
No full textThe role of news is found to be fundamentally useful in understanding the behaviour of financial market volatility. It has been found that the Engle’s basic ARCH models are incapable of capturing all observed phenomena, such as asymmetric effect, excess kurtosis and high degree of nonlinearity, which are often the stylized facts exhibited by most financial and economic time series. Bollerslev’s GARCH has the similar cavities as the ARCH. Although the EGARCH and GJR models capture the asymmetric news and nonlinearities, performances of these two models are quite different. News Impact curve approach to the GARCH, EGARCH, and the GJR models & the diagnostic tests of asymmetry of this paper indicate that the EGARCH model is preferred to the other two models in explaining the asymmetric movements in the rate of return on the US/AUS exchange rates
Assessing different modelling concepts for estimating pluvial flooding in urban areas
No full textOur climate is changing. In the Netherlands, higher rainfall intensities and longer periods of drought are expected in the coming years. Urban environments have to be adapted in order to maintain (or improve) the living standards that we have set for today. Models of the urban drainage system play a vital role in the identification of flood-prone areas, participation of stakeholders and the design of effective measures. This research focuses on the modelling of the urban drainage system to assess pluvial flooding. A method is selected to compare the main modelling concepts and differences between the most often used modelling concepts are assessed
A comparative study of F-18 FDG and C-11 Methionine PET in the evaluation of brain tumours
No full textA Bayesian non-parametric approach to asymmetric dynamic conditional correlation model with application to portfolio selection
No full textWe use an asymmetric dynamic conditional correlation (ADCC) GJR-GARCH model
to estimate the time-varying volatilities of financial returns. The ADCC-GJR-GARCH
model takes into consideration the asymmetries in individual assets volatilities, as well
as in the correlations. The errors are modeled using a flexible location-scale mixture of
infinite Gaussian distributions and the inference and estimation is carried out by relying
on Bayesian non-parametrics. Finally, we carry out a simulation study to illustrate the
flexibility of the new method and present a financial application using Apple and
NASDAQ Industrial index data to solve a portfolio allocation problemThe first and second authors are grateful for the financial support from MEC grant
ECO2011-25706. The third author acknowledges financial support from MEC grant
ECO2012-3844
Author's personal copy Forecasting VaR using analytic higher moments for GARCH processes
No full textThis article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. It is widely accepted that some of the most accurate Value-at-Risk (VaR) estimates are based on an appropriately specified GARCH process. But when the forecast horizon is greater than the frequency of the GARCH model, such predictions have typically required time-consuming simulations of the aggregated returns distributions. This paper shows that fast, quasi-analytic GARCH VaR calculations can be based on new formulae for the first four moments of aggregated GARCH returns. Our extensive empirical study compares the CornishFisher expansion with the Johnson SU distribution for fitting distributions to analytic moments of normal and Student t, symmetric and asymmetric (GJR) GARCH processes to returns data on different financial assets, for the purpose of deriving accurate GARCH VaR forecasts over multiple horizons and significance levels
