Bayesian realized-GARCH models for financial tail risk forecasting incorporating the two-sided Weibull distribution

The Two-sided Weibull Distribution and Forecasting Financial Tail Risk

A two-sided Weibull is developed to model the conditional financial return distribution, for the purpose of forecasting Value at Risk (VaR) and conditional VaR. A range of conditional return distributions are combined with four volatility specifications to forecast tail risk in four international markets, two exchange rates and one individual asset series, over a four year forecast period that ...

Bayesian Tail Risk Forecasting using Realised Volatility DCC-Copula-GARCH Models

A Skewed Student-t Realised DCC copula model using Realised Volatility GARCH marginal functions is developed within a Bayesian framework for the purpose of forecasting portfolio Value at Risk and Conditional Value at Risk. The use of copulas is implemented so that the marginal distributions can be separated from the dependence structure to produce tail forecasts. This is compared to using tradi...

two- sided power distribution

In this paper, a new family of distributions with many applications in financial engineering have been introduced. This distribution contains important statistical distributions such as the triangular, exponential and uniform distribution. Initially considered a special case of this distribution And then survey The important features of it. How to calculate maximum likelihood estimates are pres...

Bayesian Semiparametric GARCH Models

This paper aims to investigate a Bayesian sampling approach to parameter estimation in the semiparametric GARCH model with an unknown conditional error density, which we approximate by a mixture of Gaussian densities centered at individual errors and scaled by a common standard deviation. This mixture density has the form of a kernel density estimator of the errors with its bandwidth being the ...

Forecasting Value-at-risk by Using Garch Models