2 citations · 4 across the 5 of their papers we have counts for
10 papers
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…
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…