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This paper is concerned with efficient GMM estimation and inference in GARCH models. Sufficient conditions for the estimator to be consistent and asymptotically normal are established for the GARCH(1,1) conditional variance process. In addition efficiency results are obtained in the general...
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Estimating a density function over a bounded domain can be very complicated and resulting in an unsatisfactory or unrealistic density estimate. In many cases a one-to-one transformation can be applied to the considered data set, but there are also situations where such a unique transformation...
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This paper contains a forecasting exercise on 30 time series, ranging on several fields, from economy to ecology. The statistical approach to artificial neural networks modelling developed by the author is compared to linear modelling and to other three well-known neural network modelling...
Persistent link: https://www.econbiz.de/10001645582
Properties of three well-known and frequently applied first-order models for modelling and forecasting volatility in … Conditional Heteroskedasticity (GARCH), the Exponential GARCH and the Autoregressive Stochastic Volatility model. The focus is on … heteroskedasticity ; evaluation of volatility models ; exponential GARCH ; GARCH ; modelling return series ; stochastic volatility …
Persistent link: https://www.econbiz.de/10002199620
The full Bayesian treatment of error component models typically relies on data augmentation to produce the required inference. Never stricly necessary a direct approach is always possible though not necessarily practical. The mechanics of direct sampling are outlined and a template for including...
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