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...

This study examines whether Islamic gold-backed cryptocurrencies (Onegram and X8X) provide any diversification benefits to the investors of Indonesia. We co-movements between return volatility Indonesian equity indices during pre-COVID-19 COVID-19 periods. employ Multivariate Generalized Autoregressive Conditional Heteroscedastic-Dynamic Correlation (M-GARCH-DCC) Continuous Wavelet Transforms (...

Time varying correlations are often estimated with Multivariate Garch models that are linear in squares and cross products of the data. A new class of multivariate models called dynamic conditional correlation (DCC) models is proposed. These have the flexibility of univariate GARCH models coupled with parsimonious parametric models for the correlations. They are not linear but can often be esti...

We propose a generalization of the Dynamic Conditional Correlation multivariate GARCH model of Engle (2002) and of the Asymmetric Dynamic Conditional Correlation model of Cappiello et al. (2006). The model we propose introduces a block structure in parameter matrices that allows for interdependence with a reduced number of parameters. Our model nests the Flexible Dynamic Conditional Correlation...

China’s introduction of CSI300 futures in 2010 has aroused widespread attention to whether the stock index futures market has effectively stabilized price fluctuations of its spot market in the past four years. Since the prices of CSI300 futures and CSI300 contain numerous noises and fluctuate drastically over time, this paper applies discrete wavelet transform to denoise these series by decomp...

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...