BP-CVaR: A novel model of estimating CVaR with back propagation algorithm

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, DI...

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Bibliographic Details
Published in:Economics letters Vol. 209; p. 110125
Main Authors: Wang, Gang-Jin, Zhu, Chun-Long
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.12.2021
Elsevier Science Ltd
Subjects:
Algorithms
Back propagation
Back-testing
BP-CVaR
CVaR
Estimating techniques
Indexes
Markets
Markov analysis
Monte Carlo method
Monte Carlo simulation
Risk measure
Risk measure
G10
C58
Back propagation
CVaR
BP-CVaR
Back-testing
ISSN:0165-1765, 1873-7374
Online Access:Get full text
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Summary: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.
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ISSN:0165-1765
1873-7374
DOI:10.1016/j.econlet.2021.110125