4 papers
Wasserstein Exponential Smoothing for Distributional Time Series Forecasting
Takuo Matsubara, Peiwen Jiang, Minh-Ngoc Tran +1
Distributional time series arise when each temporal observation is a probability distribution rather than a scalar. We propose Wasserstein exponential smoothing (WES), a one-parame…
Deep Learning Enhanced Multivariate GARCH
Haoyuan Wang, Chen Liu, Minh-Ngoc Tran +1
This paper introduces a novel multivariate volatility modeling framework, named Long Short-Term Memory enhanced BEKK (LSTM-BEKK), that integrates deep learning into multivariate GA…
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…
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…