A Skewed Student-t Realised DCC copula model using Realised Volatility GARCH marginal functions is developed within a Bayesian framework for the purpose of forecasting portfolio Value at Risk and Conditional Value at Risk. The use of copulas is implemented so that the marginal distributions can be separated from the dependence structure to produce tail forecasts. This is compared to using tradi...

Over the past years, cryptocurrencies have drawn substantial attention from media while attracting many investors. Since then, cryptocurrency prices experienced high fluctuations. In this paper, we forecast high-frequency 1 min volatility of four widely traded cryptocurrencies, i.e., Bitcoin, Ethereum, Litecoin, and Ripple, by modeling to select best model. We propose various generalized autore...

This paper introduces the scalar DCC-HEAVY and DECO-HEAVY models for conditional variances correlations of daily returns based on measures realized built from intraday data. Formulas multi-step forecasts are provided. Asymmetric versions developed. An empirical study shows that in terms HEAVY outperform BEKK-HEAVY model covariances BEKK, DCC, DECO multivariate GARCH exclusively

This paper examines the spillover effect from Chinese stock market to select emerging economies check diversification opportunities. The study analysed data in three different periods including full period January 3, 2000 February 7, 2020; first sub October 18, 2009 and second 19 2020. We applied Granger Causality Dynamic Conditional Correlation Generalized Autoregressive Heteroscedasticity (DC...

Time varying correlations are often estimated with Multivariate Garch models that are linear in squares and cross products of returns. 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 estim...

This paper addresses the question of the selection of multivariate GARCH models in terms of variance matrix forecasting accuracy with a particular focus on relatively large scale problems. We consider 10 assets from the NYSE and compare 125 model based one, five and twenty-day ahead conditional variance forecasts over a period of 10 years using the Model Confidence Set (MCS) and the Superior Pr...

One of the most widely-used multivariate conditional volatility models is the dynamic conditional correlation (or DCC) specification. However, the underlying stochastic process to derive DCC has not yet been established, which has made problematic the derivation of asymptotic properties of the Quasi-Maximum Likelihood Estimators (QMLE). To date, the statistical properties of the QMLE of the DCC...