• Corpus ID: 235266021

A Bayesian realized threshold measurement GARCH framework for financial tail risk forecasting

@inproceedings{Wang2021ABR,
  title={A Bayesian realized threshold measurement GARCH framework for financial tail risk forecasting},
  author={Chao Wang and Richard H. Gerlach},
  year={2021}
}
In this paper, an innovative threshold measurement equation is proposed to be employed in a Realized-GARCH framework. The proposed framework employs a nonlinear threshold regression specification to consider the leverage effect and model the contemporaneous dependence between the observed realized measures and hidden volatility. A Bayesian Markov Chain Monte Carlo method is adapted and employed for the model estimation and forecasting, with its validity assessed via a simulation study. The… 

Figures and Tables from this paper

References

SHOWING 1-10 OF 36 REFERENCES

Bayesian modeling and forecasting of Value‐at‐Risk via threshold realized volatility

TLDR
This threshold realized GARCH model with skew Student‐t innovations outperforms the competing risk models in out‐of‐sample volatility and Value‐at‐Risk forecasting and successfully models the asymmetric dynamic structure.

Forecasting risk via realized GARCH, incorporating the realized range

The realized GARCH framework is extended to incorporate the realized range, and the intra-day range, as potentially more efficient series of information than re- alized variance or daily returns, for

Bayesian Semi-Parametric Realized Conditional Autoregressive Expectile Models for Tail Risk Forecasting

A new model framework called Realized Conditional Autoregressive Expectile is proposed, whereby a measurement equation is added to the conventional Conditional Autoregressive Expectile model. A

Bayesian Tail Risk Forecasting using Realised GARCH

A Realised Volatility GARCH model is developed within a Bayesian framework for the purpose of forecasting Value at Risk and Conditional Value at Risk. Student-t and Skewed Student-t return

Volatility Forecasting Using Threshold Heteroskedastic Models of the Intra-day Range

TLDR
The focus is on parameter estimation, inference and volatility forecasting in a Bayesian framework, and the range-based threshold heteroskedastic models are well supported by the data in terms of finding significant threshold nonlinearity, diagnostic checking and volatility forecast performance under various volatility proxies.

Quantile Forecasts of Financial Returns Using Realized GARCH Models

This article applies the realized GARCH model, which incorporates the GARCH model with realized volatility (RV), to quantile forecasts of financial returns such as Value-at-Risk and expected

Realized GARCH: a joint model for returns and realized measures of volatility

We introduce a new framework, Realized GARCH, for the joint modeling of returns and realized measures of volatility. A key feature is a measurement equation that relates the realized measure to the

Smooth-Transition GARCH Models

The asymmetric response of conditional variances to positive versus negative news has been traditionally modeled with threshold specifications that allow only two possible regimes: low or high

Modeling and Forecasting Realized Volatility

TLDR
A general framework for integration of high-frequency intraday data into the measurement, modeling, and forecasting of daily and lower frequency volatility and return distributions is provided and the links between the conditional covariance matrix and the concept of realized volatility are formally developed.