2 citations · 3 across the 3 of their papers we have counts for
7 papers
Modelling uncertainty in financial tail risk: a forecast combination and weighted quantile approach
Giuseppe Storti, Chao Wang
A novel forecast combination and weighted quantile based tail-risk forecasting framework is proposed, aiming to reduce the impact of modelling uncertainty in tail-risk forecasting.…
Tail risk forecasting using Bayesian realized EGARCH models
Vica Tendenan, Richard Gerlach, Chao Wang
This paper develops a Bayesian framework for the realized exponential generalized autoregressive conditional heteroskedasticity (realized EGARCH) model, which can incorporate multi…
Nonparametric Expected Shortfall Forecasting Incorporating Weighted Quantiles
Giuseppe Storti, Chao Wang
A new semi-parametric Expected Shortfall (ES) estimation and forecasting framework is proposed. The proposed approach is based on a two-step estimation procedure. The first step in…
Semi-parametric Realized Nonlinear Conditional Autoregressive Expectile and Expected Shortfall
Chao Wang, Richard Gerlach
A joint conditional autoregressive expectile and Expected Shortfall framework is proposed. The framework is extended through incorporating a measurement equation which models the c…
A Semi-parametric Realized Joint Value-at-Risk and Expected Shortfall Regression Framework
Chao Wang, Richard Gerlach, Qian Chen
A new realized conditional autoregressive Value-at-Risk (VaR) framework is proposed, through incorporating a measurement equation into the original quantile regression model. The f…
Semi-parametric Dynamic Asymmetric Laplace Models for Tail Risk Forecasting, Incorporating Realized Measures
Richard Gerlach, Chao Wang
The joint Value at Risk (VaR) and expected shortfall (ES) quantile regression model of Taylor (2017) is extended via incorporating a realized measure, to drive the tail risk dynami…