Realized GARCH model explains VIX and VRP dynamics.
problem Understanding VIX and VRP dynamics in financial markets.
method Developed Realized GARCH model with two shocks.
result Realized GARCH model outperforms conventional GARCH models.
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11 results for “Realized-GARCH”
Bayesian realized-GARCH models forecast financial tail risks using two-sided Weibull distribution.
problem Forecasting financial tail risks in volatile markets.
method Adaptive Bayesian Markov Chain Monte Carlo for estimation and forecasting, incorporating sub-sampled realized range and variance.
result Realized-GARCH models with two-sided Weibull distribution outperform other models in tail risk forecasting.
New volatility model for option pricing with time-varying risk premium.
problem Volatility risk premium is time-varying and not well captured by existing models.
method Combines Markov switching with Realized GARCH framework to derive a state-dependent pricing kernel.
result The model reduces option pricing errors by 15% or more compared to competing models.
A Bayesian realized threshold measurement GARCH framework for financial tail risk forecastingq-fin.RM
Bayesian framework forecasts financial tail risks using realized volatility and nonlinear thresholds.
problem Forecasting financial tail risks using realized volatility and nonlinear thresholds.
method Bayesian Markov Chain Monte Carlo method for model estimation; nonlinear threshold regression specification.
result The proposed framework produces competitive tail risk forecasts compared to GARCH and Realized-GARCH models.
Enhanced volatility model using LSTM and realized volatility.
problem Volatility modeling in financial markets.
method Combining deep learning (LSTM) and realized volatility measures in a Bayesian framework.
result Superior predictive performance compared to benchmark models.
A new model framework called Realized Conditional Autoregressive Expectile (Realized-CARE) is proposed, through incorporating a measurement equation into the conventional CARE model, in a manner analogous to the Realized-GARCH model. Competing realized measures (e.g. Realized Variance and Realized Range) are employed a…
Oil is perceived as a good diversification tool for stock markets. To fully understand this potential, we propose a new empirical methodology that combines generalized autoregressive score copula functions with high frequency data and allows us to capture and forecast the conditional time-varying joint distribution of …
A new model forecasts Value-at-Risk using NIG distribution and dynamic scores.
problem Forecasting Value-at-Risk (VaR) in financial markets.
method Proposes a parametric forecasting model based on the normal inverse Gaussian distribution (NIG) incorporating intraday information.
result The model outperforms traditional GARCH models, especially in high-risk scenarios.
This paper proposes an enhanced approach to modeling and forecasting volatility using high frequency data. Using a forecasting model based on Realized GARCH with multiple time-frequency decomposed realized volatility measures, we study the influence of different timescales on volatility forecasts. The decomposition of …
Improved tail risk forecasting using realized measures and Bayesian methods.
problem Forecasting tail risk in financial markets.
method Extended Taylor's model with realized measures, using maximum likelihood and Bayesian MCMC methods.
result Bayesian approach outperforms maximum likelihood in forecasting accuracy.
Using quantile time series and historical simulation to forecast financial risk multiple steps aheadq-fin.ST
The paper develops a method to forecast financial risk multiple steps ahead using quantile time series and historical simulation.
problem Forecasting financial risk multiple steps ahead with accurate estimation of Value-at-Risk (VaR) and Expected Shortfall (ES).
method Quantile-based, semi-parametric historical simulation estimation of VaR and ES models, using quantile loss function and resampling.
result The proposed method accurately forecasts VaR and ES one and multiple steps ahead, superior to existing methods.