6 papers
Using quantile time series and historical simulation to forecast financial risk multiple steps ahead
Richard Gerlach, Antonio Naimoli, Giuseppe Storti
A method for quantile-based, semi-parametric historical simulation estimation of multiple step ahead Value-at-Risk (VaR) and Expected Shortfall (ES) models is developed. It uses th…
Financial Volatility and Risk Forecasting Incorporating a Larger Number of Realized Measures
Qianli Zhao, Chao Wang, Richard Gerlach +2
Realised volatility has become increasingly prominent in volatility forecasting due to its ability to capture intraday price fluctuations. With a growing variety of realised volati…
Loss-based Bayesian Sequential Prediction of Value at Risk with a Long-Memory and Non-linear Realized Volatility Model
Rangika Peiris, Minh-Ngoc Tran, Chao Wang +1
A long memory and non-linear realized volatility model class is proposed for direct Value at Risk (VaR) forecasting. This model, referred to as RNN-HAR, extends the heterogeneous a…
Semi-parametric financial risk forecasting incorporating multiple realized measures
Rangika Peiris, Chao Wang, Richard Gerlach +1
A semi-parametric joint Value-at-Risk (VaR) and Expected Shortfall (ES) forecasting framework employing multiple realized measures is developed. The proposed framework extends the…
Global Neural Networks and The Data Scaling Effect in Financial Time Series Forecasting
Chen Liu, Minh-Ngoc Tran, Chao Wang +2
Neural networks have revolutionized many empirical fields, yet their application to financial time series forecasting remains controversial. In this study, we demonstrate that the…
Estimating Quantile Families of Loss Distributions for Non-Life Insurance Modelling via L-moments
Gareth W. Peters, Wilson Y. Chen, Richard H. Gerlach
This paper discusses different classes of loss models in non-life insurance settings. It then overviews the class Tukey transform loss models that have not yet been widely consider…