3 citations · 9 across the 11 of their papers we have counts for
14 papers
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…
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…
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…
Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion
Mahdi Abolghasemi, Richard Gerlach, Garth Tarr +1
The demand for a particular product or service is typically associated with different uncertainties that can make them volatile and challenging to predict. Demand unpredictability…
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…
Bayesian Nonparametric Adaptive Spectral Density Estimation for Financial Time Series
Nick James, Roman Marchant, Richard Gerlach +1
Discrimination between non-stationarity and long-range dependency is a difficult and long-standing issue in modelling financial time series. This paper uses an adaptive spectral te…
Manifold Optimization Assisted Gaussian Variational Approximation
Bingxin Zhou, Junbin Gao, Minh-Ngoc Tran +1
Gaussian variational approximation is a popular methodology to approximate posterior distributions in Bayesian inference especially in high dimensional and large data settings. To…
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…
Fighting Accounting Fraud Through Forensic Data Analytics
Maria Jofre, Richard Gerlach
Accounting fraud is a global concern representing a significant threat to the financial system stability due to the resulting diminishing of the market confidence and trust of regu…
Semiparametric GARCH via Bayesian model averaging
Wilson Ye Chen, Richard H. Gerlach
As the dynamic structure of the financial markets is subject to dramatic changes, a model capable of providing consistently accurate volatility estimates must not make strong assum…
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…
Dynamic Quantile Function Models
Wilson Ye Chen, Gareth W. Peters, Richard H. Gerlach +1
Motivated by the need for effectively summarising, modelling, and forecasting the distributional characteristics of intra-daily returns, as well as the recent work on forecasting h…
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…