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...

In this paper, we study the cross-market effects of Brexit on the stock and bond markets of nine major countries in the world. By incorporating information theory, we introduce the time-varying impact weights based on symbolic transfer entropy to improve the traditional GARCH model. The empirical results show that under the influence of Brexit, flight-to-quality not only commonly occurs between...

Volatility forecasting is a challenging task that has attracted the attention of market practitioners, regulators and academics in recent years. This paper proposes an evolving fuzzyGARCH approach to model and forecast the volatility of S&P 500 and Ibovespa indexes. The model comprises both the concept of evolving fuzzy systems and GARCH modeling approach in order to consider the principles of ...

In this article, we investigated the volatility of Chinese open-end funds market by using Zhongxin open-end funds index. According to the characteristics of different GARCH models, we empirically studied GARCH, EGARCH and GARCH_M model. The result indicated that GARCH (1, 1) model and GARCH_M (1, 1) model could better fit the characteristics of the index return rate. At the same time, the resul...

This research estimates portfolio VaR (Value-at-Risk) on G7 exchange rates using a GJR-GARCH-EVT (extreme value theory)-Copula based approach. We first extracts the filtered residuals from each return series via an asymmetric GJR-GARCH model, then constructs the semi-parametric empirical marginal cumulative distribution function (CDF) of each asset using a Gaussian kernel estimate for the inter...

this paper empirically investigates the relationship between cpi inflation uncertainty, and private investment in the iranian economy from 1988 to 2010 by using quarterly data. we employ a bivariate var(5)-garch(1,1)-in-mean with diagonal bekk model to discover in a unified framework how are the interactions between the variables. in the model, conditional variance of inflation and private inve...

In this paper we provide a unified methodology for conducting likelihood-based inference on the unknown parameters of a general class of discrete-time stochastic volatility (SV) models, characterized by both a leverage effect and jumps in returns. Given the nonlinear/non-Gaussian state-space form, approximating the likelihood for the parameters is conducted with output generated by the particle...

The three most popular univariate conditional volatility models are the generalized autoregressive conditional heteroskedasticity (GARCH) model of Engle (1982) and Bollerslev (1986), the GJR (or threshold GARCH) model of Glosten, Jagannathan and Runkle (1992), and the exponential GARCH (or EGARCH) model of Nelson (1990, 1991). The underlying stochastic specification to obtain GARCH was demonstr...

This paper focuses on the diagnostic checking of vector ARMA (VARMA) models with multivariate GARCH errors. For a fitted VARMA-GARCH model with Gaussian or Student-t innovations, we derive the asymptotic distributions of autocorrelation matrices of the cross-product vector of standardized residuals. This is different from the traditional approach that employs only the squared series of standard...

This paper provides a new empirical guidance for modeling a skewed and fat-tailed error distribution underlying the traditional GARCH models for equity returns based on empirical findings on Realized Volatility (RV), constructed from the summation of higher-frequency squared (demeaned) returns. Based on an 80-year sample of U.S. daily stock market returns, I find that the distribution of monthl...