BP-CVaR: A novel model of estimating CVaR with back propagation algorithm - Wang, Gang-Jin; Zhu, Chun-Long (Economics letters, 2021)
We propose a more flexible and useful model, BP-CVaR, to estimate conditional value-at-risk (CVaR) using back propagation (BP) algorithm, which can capture the change of markets and use the information to adjust the next CVaR result. We use three samples including S&P 500 index, Nasdaq index, DIJA index and compare the results by back-testing approaches. We find that (i) BP-CVaR is more reliable and has higher accuracy than Monte Carlo CVaR model and (ii) BP-CVaR can react to the change of markets more quickly than traditional CVaR model. •We introduce BP algorithm to calculate CVaR, i.e., BP-CVaR.•BP-CVaR performs better than Monte Carlo CVaR.•BP-CVaR is more sensitive to the change of markets.