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Deep learning of value at risk through generative neural network models: The case of the Variational auto encoder [PDF]

open access: yesMethodsX, 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
doaj   +2 more sources

Value-at-Risk for Country Risk Ratings [PDF]

open access: yesSSRN Electronic Journal, 2009
The country risk literature argues that country risk ratings have a direct impact on the cost of borrowings as they reflect the probability of debt default by a country. An improvement in country risk ratings, or country creditworthiness, will lower a country's cost of borrowing and debt servicing obligations, and vice versa.
Mcaleer, M.   +2 more
core   +12 more sources

Variation in Medicaid and commercial coverage of cell and gene therapies

open access: yesHealth 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
doaj   +1 more source

Energy risk measurement and hedging analysis by nonparametric conditional value at risk model

open access: yesFrontiers 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
doaj   +1 more source

Individual Investors’ Attention to Left Tail Risk [PDF]

open access: yesJournal 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
doaj   +1 more source

Cyber Risk Quantification: Investigating the Role of Cyber Value at Risk

open access: yesRisks, 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
doaj   +1 more source

Does the COVID-19 pandemic matter for market risks across sectors in Vietnam?

open access: yesHeliyon, 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
doaj   +1 more source

Tsallis value-at-risk: generalized entropic value-at-risk

open access: yesProbability in the Engineering and Informational Sciences, 2022
AbstractMotivated by Ahmadi-Javid (Journal of Optimization Theory Applications, 155(3), 2012, 1105–1123) and Ahmadi-Javid and Pichler (Mathematics and Financial Economics, 11, 2017, 527–550), the concept of Tsallis Value-at-Risk (TsVaR) based on Tsallis entropy is introduced in this paper. TsVaR corresponds to the tightest possible upper bound obtained
Zhenfeng Zou, Zichao Xia, Taizhong Hu
openaire   +2 more sources

Long‐Term Value at Risk [PDF]

open access: yesThe Journal of Risk Finance, 2004
One of the most significant recent developments in the risk measurement and management area has been the emergence of value at risk (VaR). The VaR of a portfolio is the maximum loss that the portfolio will suffer over a defined time horizon, at a specified level of probability known as the VaR confidence level.
Blake, D., Cairns, A., Dowd, K.
openaire   +3 more sources

On Partial Stochastic Comparisons Based on Tail Values at Risk

open access: yesMathematics, 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
doaj   +1 more source

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