Using the high-frequency data of Bitcoin, this study aims to model time-varying volatility identified in residuals heterogeneous autoregressive (HAR) realized using symmetric, asymmetric and long-memory generalized conditional heteroscedastic models (GARCH) models. We further extended these by incorporating jumps continuous components estimators investigating impact inverse leverage effect. The...

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 note investigates impacts of multivariate generalised autoregressive conditional heteroskedasticity (GARCH) errors on hypothesis testing for cointegrating vectors. The study reviews a cointegrated vector autoregressive model incorporating multivariate GARCH innovations and a regularity condition required for valid asymptotic inferences. Monte Carlo experiments are then conducted on a test ...

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...

Empirical Mode Decomposition (EMD), recently proposed by Huang et al. [12], appears to be a novel data analysis method for nonlinear and non-stationary time series. By decomposing a time series into a small number of independent and concretely implicational intrinsic modes based on scale separation, EMD explains the generation of time series data from a novel perspective. This paper presents an...

This paper proposes structured parametrizations for multivariate volatility models, which use spatial weight matrices induced by economic proximity. These structured speci…cations aim at solving the curse of dimensionality problem, which limits feasibility of model-estimation to small cross-sections for unstructured models. Structured parametrizations possess the following four desirable proper...

Measuring and forecasting volatility of asset returns is very important for asset trading and risk management. There are various forms of volatility estimates, including implied volatility, realized volatility and volatility assumed under stochastic volatility models and GARCH models. Research has shown that these different methods are closely related but have different perspectives, strengths ...

The Covid-19 pandemic increased uncertainty in the Indonesian stock market. This paper aims to investigate foreign and domestic investors' behavior market, especially during pandemic. method used was E-GARCH DCC-GARCH. results showed that impact of shocks from bad good news greater on return volatility had a positive increase spillover effect NFI's short long term before pandemic, but this did ...