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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Veröffentlicht in:Economics letters Jg. 209; S. 110125
Hauptverfasser: Wang, Gang-Jin, Zhu, Chun-Long
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 01.12.2021
Elsevier Science Ltd
Schlagworte:
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
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Abstract 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.
AbstractList 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.
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.
ArticleNumber 110125
Author Zhu, Chun-Long
Wang, Gang-Jin
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10.1016/j.eswa.2021.114952
10.2307/2527341
10.1016/j.eswa.2011.09.048
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10.1016/j.eswa.2015.04.023
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Keywords Risk measure
G10
C58
Back propagation
CVaR
BP-CVaR
Back-testing
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Snippet 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...
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SubjectTerms Algorithms
Back propagation
Back-testing
BP-CVaR
CVaR
Estimating techniques
Indexes
Markets
Markov analysis
Monte Carlo method
Monte Carlo simulation
Risk measure
Title BP-CVaR: A novel model of estimating CVaR with back propagation algorithm
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