This paper introduces a new model for panel data with Markov-switching GARCH effects. The incorporates series-specific hidden Markov chain process that drives the parameters. To cope high-dimensionality of parameter space, exploits cross-sectional clustering series by first assuming soft pooling through hierarchical prior distribution two-step procedure, and then introducing effects in space no...

This paper introduces a unified approach for modeling high-frequency financial data that can accommodate both the continuous-time jump–diffusion and discrete-time realized GARCH model by embedding discrete structure in continuous instantaneous volatility process. The key feature of proposed is corresponding conditional daily integrated adopts an autoregressive structure, where jump variation se...

This paper introduces a novel quantile approach to harness the high-frequency information and improve daily conditional estimation. Specifically, we model standard deviation as realized GARCH employ deviation, volatility, quantile, absolute overnight return innovations in proposed dynamic models. We devise two-step estimation procedure estimate parameters. The first step applies quasi-maximum l...

In practice, Financial Time Series have serious volatility cluster, that is large volatility tend to be concentrated in a certain period of time, and small volatility tend to be concentrated in another period of time. While GARCH models can well describe the dynamic changes of the volatility of financial time series, and capture the cluster and heteroscedasticity phenomena. At the beginning of ...

We estimate the risk spillover among European banks from equity log-return data via Conditional Value at Risk (CoVaR). The joint dynamic of returns is modeled with a spatial DCC-GARCH which allows conditional variance log-returns each bank to depend on past volatility shocks other and their squared in parsimonious way. backtesting resulting measures provides evidence that (i) multivariate GARCH...

GARCH model has gained popularity during the last two decades, because of their ability to capture non-linear dynamics in the real life data which we often observe especially in financial markets. This paper discuss four common information criteria (AIC, AICc, BIC and HQ) and their ability of correct selection in the presence of GARCH effect, based on their probability of correct selection as a...