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...

In this article, a Multivariate Threshold Generalized Autoregressive Conditional Heteroscedasticity model with time-varying correlation (VC-MTGARCH) is proposed. The model extends the idea of Engle (2002) and Tse & Tsui (2002) in a threshold framework. This model retains the interpretation of the univariate threshold GARCH model and allows for dynamic conditional correlations. Extension of Boll...

In this paper we propose a new multivariate GARCH model with timevarying correlations. We adopt the vech representation based on the conditional variances and the conditional correlations. While each conditional-variance term is assumed to follow a univariate GARCH formulation, the conditional-correlation matrix is postulated to follow an autoregressive moving average type of analogue. By impos...

During the last 15 years, several Multivariate GARCH (MGARCH) models have appeared in the literature. Recent research has begun to examine MGARCH specifications in terms of their out-of-sample forecasting performance. We provide an empirical comparison of alternative MGARCH models, namely BEKK, DCC, Corrected DCC (cDCC), CCC, OGARCH Exponentially Weighted Moving Average, and covariance shrinkin...

We propose a simple class of semiparametric multivariate GARCH models, allowing for asymmetric volatilities and time-varying conditional correlations. Estimates for time-varying conditional correlations are constructed by means of a convex combination of estimates for averaged correlations (across all assets) and dynamic realized (historical) correlations. Our model is very parsimonious. Estima...

Tests of causality in variance in multiple time series have been proposed recently, based on residuals of estimated univariate models. Although such tests are applied frequently little is known about their power properties. In this paper we show that a convenient alternative to residual based testing is to specify a multivariate volatility model, such as multivariate GARCH (or BEKK), and constr...

We propose a simple class of multivariate GARCH models, allowing for time-varying conditional correlations. Estimates for time-varying conditional correlations are constructed by means of a convex combination of averaged correlations (across all series) and dynamic realized (historical) correlations. Our model is very parsimonious. Estimation is computationally feasible in very large dimensions...

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...

DAMGARCH extends the VARMA-GARCH model of Ling and McAleer (2003) by introducing multiple thresholds and time-dependent structure in the asymmetry of the conditional variances. DAMGARCH models the shocks affecting the conditional variances on the basis of an underlying multivariate distribution. It is possible to model explicitly asset-specific shocks and common innovations by partitioning the ...

This paper focuses on the effect of mergers and acquisitions (M&As) announcements stocks Latin American banks their rivals between 2000 2019. We evaluate two impacts M&A announcements: cumulative abnormal returns (CAR) event-induced variance (EIV). use GARCH-based event-study method, finding that acquirers target have a statistically significant CAR targets are not affected by announcements. ob...