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q-fin.RM2020
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…
stat.ML2020
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…