3 citations · 13 across the 13 of their papers we have counts for
13 papers
Deep Learning Enhanced Realized GARCH
Chen Liu, Chao Wang, Minh-Ngoc Tran +1
We propose a new approach to volatility modeling by combining deep learning (LSTM) and realized volatility measures. This LSTM-enhanced realized GARCH framework incorporates and di…
A semi-parametric dynamic conditional correlation framework for risk forecasting
Giuseppe Storti, Chao Wang
We develop a novel multivariate semi-parametric framework for joint portfolio Value-at-Risk (VaR) and Expected Shortfall (ES) forecasting. Unlike existing univariate semi-parametri…
Portfolio optimization with idiosyncratic and systemic risks for financial networks
Yajie Yang, Longfeng Zhao, Lin Chen +2
In this study, we propose a new multi-objective portfolio optimization with idiosyncratic and systemic risks for financial networks. The two risks are measured by the idiosyncratic…
A Bayesian realized threshold measurement GARCH framework for financial tail risk forecasting
Chao Wang, Richard Gerlach
This paper proposes an innovative threshold measurement equation to be employed in a Realized-GARCH framework. The proposed framework incorporates a nonlinear threshold regression…
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…
A Bayesian Long Short-Term Memory Model for Value at Risk and Expected Shortfall Joint Forecasting
Zhengkun Li, Minh-Ngoc Tran, Chao Wang +2
Value-at-Risk (VaR) and Expected Shortfall (ES) are widely used in the financial sector to measure the market risk and manage the extreme market movement. The recent link between t…
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…
Bayesian Realized-GARCH Models for Financial Tail Risk Forecasting Incorporating Two-sided Weibull Distribution
Chao Wang, Qian Chen, Richard Gerlach
The realized GARCH framework is extended to incorporate the two-sided Weibull distribution, for the purpose of volatility and tail risk forecasting in a financial time series. Furt…
Bayesian Semi-parametric Realized-CARE Models for Tail Risk Forecasting Incorporating Realized Measures
Richard Gerlach, Chao Wang
A new model framework called Realized Conditional Autoregressive Expectile (Realized-CARE) is proposed, through incorporating a measurement equation into the conventional CARE mode…