The purpose of the paper is to discuss ten things potential users should know about the limits of the Dynamic Conditional Correlation (DCC) representation for estimating and forecasting time-varying conditional correlations. The reasons given for caution about the use of DCC include the following: DCC represents the dynamic conditional covariances of the standardized residuals, and hence does n...

The purpose of the paper is to discuss ten things potential users should know about the limits of the Dynamic Conditional Correlation (DCC) representation for estimating and forecasting time-varying conditional correlations. The reasons given for caution about the use of DCC include the following: DCC represents the dynamic conditional covariances of the standardized residuals, and hence does n...

This paper aims to investigate and measure Bitcoin the five largest stablecoin market volatilities by incorporating various range-based volatility estimators BEKK- GARCH Copula-DCC-GARCH models. Specifically, we further Bitcoins’ related major stablecoins examine connectedness between stablecoins. Our empirical findings document that behaviors exhibits presence of stable interconnection. study ...

The asymmetric price volatility transmission issue in agricultural supply chains has been ignored the previous literature. This paper applies an asymmetrical MGARCH-BEKK model to investigate with application Chinese pork market. Additionally, we use Zivot–Andrews unit root test a structural break examine whether piglet, hog, and prices have breaks. results show that pork’s market breakpoint 200...

This study analyzes the volatility spillover effects in US stock market (S&P500) and cryptocurrency (BGCI) using intraday data during COVID-19 pandemic. As potential drivers of portfolio diversification, we measure asymmetric transmission on both markets. We apply MGARCH-BEKK algorithm-based GA2M machine learning model. The negative shocks to returns impact S&P500 more than positive als...

This paper investigates the forecasting ability of four different GARCH models and the Kalman filter method. The four GARCH models applied are the bivariate GARCH, BEKK GARCH, GARCH-GJR and the GARCH-X model. The paper also compares the forecasting ability of the non-GARCH model the Kalman method. Forecast errors based on twenty UK company weekly stock return (based on timevary beta) forecasts ...

W.D. Apel, J. C. Arteaga-Velàzquez, K. Bekk, M. Bertaina, J. Blümer, H. Bozdog, I.M. Brancus, E. Cantoni,* A. Chiavassa, F. Cossavella, K. Daumiller, V. de Souza, F. Di Pierro, P. Doll, R. Engel, J. Engler, M. Finger, B. Fuchs, D. Fuhrmann, H. J. Gils, R. Glasstetter, C. Grupen, A. Haungs, D. Heck, J. R. Hörandel, D. Huber, T. Huege, K.-H. Kampert, D. Kang, H.O. Klages, K. Link, P. Łuczak, M. L...