Browsing by Subject "Multivariate GARCH"
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Item Open Access Constrained nonlinear programming for volatility estimation with GARCH models(2003) Altay-Salih, A.; Pınar, M. Ç.; Leyffer, S.This paper proposes a constrained nonlinear programming view of generalized autoregressive conditional heteroskedasticity (GARCH) volatility estimation models in financial econometrics. These models are usually presented to the reader as unconstrained optimization models with recursive terms in the literature, whereas they actually fall into the domain of nonconvex nonlinear programming. Our results demonstrate that constrained nonlinear programming is a worthwhile exercise for GARCH models, especially for the bivariate and trivariate cases, as they offer a significant improvement in the quality of the solution of the optimization problem over the diagonal VECH and the BEKK representations of the multivariate GARCH model.Item Open Access A survey of multivariate GARCH models(Bilkent University, 2008) Taş, Mustafa AnılThis paper reviews the recent developments in the multivariate GARCH literature. Most common multivariate GARCH models and their properties are briefly presented.Item Open Access Testing the effects of oral interventions on the covariance of exchange rates in a state-of-the-art computational environment(Bilkent University, 2009) Çaşkurlu, TolgaIn the last decade, both Federal Reserve System (FED) and European Central Bank (ECB) abandoned direct market interventions and relied on communication as their main policy tool to affect exchange rates. This paper investigates the impacts of officials’ statements (oral intervention) on the covariance of the EUR/USD and JPY/USD. Using generalized autoregressive conditional heteroscedasticity (GARCH) model’s diagonal vector error correction (DVEC) representation, we find that strengthening oral interventions in US and Japan decrease while in Eurozone increase the covariance between EUR/USD and JPY/USD. Also reversely, weakening oral interventions in US and Japan increase while in Eurozone decrease the covariance. Since oral interventions are explanatory variables of the conditional covariance structure of G3 currencies (USD, EUR and JPY), ignoring oral interventions may cause errors in foreign exchange (forex) covariance forecasts. During the estimation procedure, we use a different approach than the commonly practiced in the literature. We solve the resulting optimization problem from maximum likelihood estimation (MLE) of DVEC model in two steps: first by genetic algorithm (GA) and then by sequential quadratic programming (SQP) algorithm. Furthermore, to land at a better local optimal, the experiments are conducted in NEOS Servers1 . Comparing our results with those of benchmark S+ GARCH module (a commercial software), we find that our approach yields much higher objective value than the benchmark does. Hence, we conclude that our computational methodology provides substantial improvement to in-sample forex covariance forecasting. Our results have applications in portfolio management as well.