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...

For change-point analysis of high dimensional time series, we consider a semiparametric model with dynamic structural break factors. The observations are described by a few low dimensional factors with time-invariate loading functions of covariates. The unknown structural break in time models...

In this paper, we consider a wide class of time-varying multivariate causal processes which nests many classic and new examples as special cases. We first prove the existence of a weakly dependent stationary approximation for our model which is the foundation to initiate the theoretical...

Firstly, we use the Multi-Scale LPPLS Confidence Indicator approach to detectboth positive and negative bubbles at short-, medium- and long-term horizons forthe stock markets of the G7 and the BRICS countries. We were able to detect majorcrashes and rallies in the 12 stock markets over the...

Volatility forecasting is important in financial econometrics and is mainly based on the application of various GARCH-type models. However, it is difficult to choose a specific GARCH model that works uniformly well across datasets, and the traditional methods are unstable when dealing with...