We propose a new method for multivariate forecasting which combines Dynamic Factor and multivariate GARCH models. We call the model Dynamic Factor GARCH, as the information contained in large macroeconomic or financial datasets is captured by a few dynamic common factors, which we assume being conditionally heteroskedastic. After describing the estimation of the model, we present simulation res...

The aim of this paper is to analyse the out-of-sample performance of SETAR models relative to a linear AR and a GARCH model using daily data for the Euro effective exchange rate. The evaluation is conducted on point, interval and density forecasts, unconditionally, over the whole forecast period, and conditional on specific regimes. The results show that overall the GARCH model is better able t...

The volatility dynamics of foreign exchanges have been the focus of research since Bollerslev’s (1986) seminal work on the generalized autoregressive conditional heteroscedasticity (GARCH) modelling. Several well-established empirical regularities may be highlighted as follows: [a] evidence of volatility clustering is detected in the exchange rates returns; [b] asymmetric effects in exchange ra...

This paper examines the hedging effectiveness of gold futures for the stock market in minimizing variance and downside risks, including value at risk and expected shortfall using data from the Iran emerging capital market during four different sub-periods from December 2008 to August 2018. We employ dynamic conditional correlation models including VARMA-BGARCH (DCC, ADCC, BEKK, and ABEKK) and c...

In this paper we propose a consistent Integrated Conditional Moment (ICM) test of the functional form of a conditional heteroskedasticity model, for example a GARCH specification, which is asymptotically independent of the ICM test of the specification of the underlying conditional expectation model, under the null hypothesis that both models are correctly specified.

In the presence of generalized conditional heteroscedasticity (GARCH) in the residuals of a vector error correction model (VECM), maximum likelihood (ML) estimation of the cointegration parameters has been shown to be efficient. On the other hand, full ML estimation of VECMs with GARCH residuals is computationally difficult and may not be feasible for larger models. Moreover, ML estimation of V...

This paper introduces a novel quantile approach to harness the high-frequency information and improve daily conditional estimation. Specifically, we model standard deviation as realized GARCH employ deviation, volatility, quantile, absolute overnight return innovations in proposed dynamic models. We devise two-step estimation procedure estimate parameters. The first step applies quasi-maximum l...

We analyze the time-dependence of exchange rate correlations using a new multivariate GARCH model. This model consists of two parts. First, we transform the exchange rate changes into their principal components and specify univariate GARCH models for all components. Second, we use the inverse of the principal components construction to transform the conditional component moments back into those...

In this paper we study a new class of nonlinear GARCH models. Special interest is devoted to models that are similar to previously introduced smooth transition GARCH models except for the novel feature that a lagged value of conditional variance is used as the transition variable. This choice of the transition variable is mainly motivated by the desire to find useful models for highly persisten...