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Deep learning of value at risk through generative neural network models: The case of the Variational auto encoder [PDF]
MethodsX, 2023 We present in this paper a method to compute, using generative neural networks, an estimator of the “Value at Risk” for a financial asset. The method uses a Variational Auto Encoder with an 'energy' (a.k.a. Radon-Sobolev) kernel.
Pierre Brugière, Gabriel Turinici
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Risk-Sensitive Safety Analysis Using Conditional Value-at-Risk [PDF]
IEEE Transactions on Automatic Control, 2021 This article develops a safetyanalysis method for stochastic systems that is sensitive to the possibility and severity of rare harmful outcomes. We define risk-sensitive safe sets as sublevel sets of the solution to a nonstandard optimal control problem,
Margaret P. Chapman +5 more
semanticscholar +1 more source
Variation in Medicaid and commercial coverage of cell and gene therapies
Health Policy Open, 2023 Growth in the availability of cell and gene therapies (CGTs) promises significant innovation in the treatment of serious diseases, but the high cost and one-time administration of CGTs has also raised concern about strain on health plan budgets and ...
Molly T. Beinfeld +4 more
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Energy risk measurement and hedging analysis by nonparametric conditional value at risk model
Frontiers in Energy Research, 2022 The accurate measurement and management of energy risk have become important issues of the economic development and energy security for all countries. The existing literature generally adopts the Value at Risk (VaR).
Ling Li, Guopeng Hu
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Cyber Risk Quantification: Investigating the Role of Cyber Value at Risk
Risks, 2021 The aim of this paper is to deepen the application of value at risk in the cyber domain, with particular attention to its potential role in security investment valuation. Cyber risk is a fundamental component of the overall risk faced by any organization.
Albina Orlando
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Individual Investors’ Attention to Left Tail Risk [PDF]
Journal of Asset Management and Financing, 2020 Objective: Left tail risk shows the probability of the occurrence of undesirable events. Investors who undergo the left tail risk are likely to experience considerable negative returns since the left tail risk oftentimes continues to the next period ...
Mahshid Shahrzadi, Daryoosh Forooghi
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Does the COVID-19 pandemic matter for market risks across sectors in Vietnam?
Heliyon, 2021 Vietnam has been considered one of the few countries that put the COVID-19 pandemic under control and successfully achieved solid economic growth in 2020. However, the national economy has been hit hard by the pandemic in 2021.
Chi Minh Ho +3 more
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On Partial Stochastic Comparisons Based on Tail Values at Risk
Mathematics, 2020 The tail value at risk at level p, with p ∈ ( 0 , 1 ) , is a risk measure that captures the tail risk of losses and asset return distributions beyond the p quantile. Given two distributions, it can be used to decide which is riskier.
Alfonso J. Bello +3 more
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Forecast combinations for value at risk and expected shortfall
International Journal of Forecasting, 2020 Combining provides a pragmatic way of synthesising the information provided by individual forecasting methods. In the context of forecasting the mean, numerous studies have shown that combining often leads to improvements in accuracy.
James W. Taylor
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Dynamic semiparametric models for expected shortfall (and Value-at-Risk) [PDF]
Journal of Econometrics, 2017 Expected Shortfall (ES) is the average return on a risky asset conditional on the return being below some quantile of its distribution, namely its Value-at-Risk (VaR).
Andrew J. Patton, J. Ziegel, Rui Chen
semanticscholar +1 more source