This paper considers efficient estimation of copula-based semiparametric strictly stationary Markov models. These models are characterized by nonparametric invariant distributions and parametric copula functions; where the copulas capture all scale-free temporal dependence and tail dependence of...

This paper considers efficient estimation of copula-based semiparametric strictly stationary Markov models. These models are characterized by nonparametric invariant (one-dimensional marginal) distributions and parametric bivariate copula functions; where the copulas capture temporal dependence...

This study investigates the role of probability distribution in forecasting volatility and Value-at-Risk (VaR). We use the Realized GARCH model and high-frequency data from the cryptocurrency market and show that the role of probability distribution varies across different situations. A skewed-t...