Relevant bibliographies by topics / Value at risk

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Value at risk'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 June 2025

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Journal articles on the topic "Value at risk"

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Zelinková, Kateřina, and Aleš Kresta. "DETERMINATION OF VALUE AT RISK AND CONDITIONAL VALUE AT RISK BY ASSUMING ELLIPTICAL DISTRIBITION." Acta academica karviniensia 16, no. 2 (June 30, 2016): 95–105. http://dx.doi.org/10.25142/aak.2016.017.

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Park, Juyeun, Eunjoo Choi, and Kihun Han. "The Effect of Hair Beauty Shop Customers' Perception of General Risks and Beauty Shop Risks on Consumer Sentiment." J-Institute 8, no. 1 (June 30, 2023): 43–55. http://dx.doi.org/10.22471/value.2023.8.1.43.

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Purpose: As the COVID-19 pandemic began, the concepts that have been used in Korean society are “With Corona” and “Post Corona.” In Korea, social distancing and mask wearing were mostly lifted, but the sense of crisis did not disappear. As consumer sentiment shrank, productivity decreased, and the entire industry was af-fected, the beauty industry (mainly face-to-face services) is also subject to many negative effects. Consumer sen-timent appears irrational and rationally regulates consumption habits. The general risk perception and beauty shop risk perception of COVID-19 were set as factors that affect consumer sentiment of hair beauty service con-sumers. This study aims to provide basic data to present a differentiated strategy that is different from other service fields for the development of the beauty industry.
 Method: The data of this study were statistically analyzed using SPSS 25.0, and first, an exploratory factor analysis was performed to analyze the validity of the measurement tool. Reliability was analyzed using Cronbach's alpha coefficient. Frequency analysis and descriptive statistical analysis were performed, and chi-square test and multiple regression analysis were performed.
 Results: Social and environmental risk and economic risk, sub-factors of general risk perception, appeared to have a significant positive (+) effect on consumer sentiment, and human risk, a sub-factor of beauty shop risk perception, had a significant negative (-) effect on consumer sentiment. Was found to have an effect on In other words, it can be said that the higher the social and environmental risk and economic risk, sub-factors of general risk perception, the higher consumer sentiment, and the higher the human risk, the sub-factor of beauty shop risk perception, the lower consumer sentiment. Economic risk (β=.290, p<.001), human risk (β=-.231, p<.01), and social and environmental risk (β=.118, p<.05) affects consumer sentiment in the order has been shown to affect.
 Conclusion: ‘Presence of regular beauty shops’, ‘Beauty shop selection criteria’, and ‘Reason for reduction in beauty expenditure and items after COVID-19’ according to the age and gender of research subjects differ. There-fore, different marketing methods should be sought for each generation or gender. Since the relationship be-tween risk perception and consumer sentiment has been revealed, it can be utilized to help recover and improve hair beauty shop business in the future.
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Stuchlíková, Zuzana. "Value-at-Risk and Dynamic Risk Measures." Acta Oeconomica Pragensia 13, no. 1 (March 1, 2005): 63–68. http://dx.doi.org/10.18267/j.aop.137.

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Longia, François M. "Value at Risk and Extreme Values." IFAC Proceedings Volumes 31, no. 16 (June 1998): 45–49. http://dx.doi.org/10.1016/s1474-6670(17)40457-5.

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Misankova, Maria, and Erika Spuchlakova. "Application of conditional value at risk for credit risk optimization." New Trends and Issues Proceedings on Humanities and Social Sciences 3, no. 4 (March 22, 2017): 146–52. http://dx.doi.org/10.18844/gjhss.v3i4.1540.

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Angelidis, Timotheos, and Alexandros Benos. "Value-at-Risk for Greek Stocks." Multinational Finance Journal 12, no. 1/2 (June 1, 2008): 67–104. http://dx.doi.org/10.17578/12-1/2-4.

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Mangiero, Susan M. "Risk2: Measuring the Risk in Value at Risk." CFA Digest 27, no. 3 (August 1997): 68–69. http://dx.doi.org/10.2469/dig.v27.n3.125.

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Jorion, Philippe. "Risk2: Measuring the Risk in Value at Risk." Financial Analysts Journal 52, no. 6 (November 1996): 47–56. http://dx.doi.org/10.2469/faj.v52.n6.2039.

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Hwang, Jaehak. "Climate Value at Risk of Korean corporations." Journal of Market Economy 51, no. 3 (October 31, 2022): 57–84. http://dx.doi.org/10.38162/jome.51.3.3.

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von Balduin, Alexander. "Was ist der„Value at Risk”?" RISKNEWS 1, no. 2 (April 2004): 50–51. http://dx.doi.org/10.1002/risk.200490034.

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Dissertations / Theses on the topic "Value at risk"

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Tran, Manh. "Value-at-risk estimates." Thesis, Aston University, 2018. http://publications.aston.ac.uk/37813/.

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This thesis consists of three empirical essays on the Value-at-Risk (VaR) estimates. The first empirical study (Chapter 2) evaluates the performance of bank VaRs. The second empirical study (Chapter 3) investigates the predictive power of various VaR models using bank data. The third empirical study (Chapter 4) explores VaR estimates with high-frequency data. The first study examines the performance of VaR estimates at seven international banks from 2001 to 2012. Using statistical tests, we find that bank VaRs were conservatively estimated in pre-crisis and post-crisis periods. During financial crisis, while some banks continued to overstate their VaRs, the others significantly underestimated their risk. The potential causes of the poor performance of bank VaRs are also discussed. The second study investigates the predictive power of various VaR models using bank data. We find that the GARCH-based models are superior in estimating bank VaRs in both normal and crisis periods. We conclude that good VaR estimates at banks can be obtained using simple, accessible models rather than the complicated approach or banks’ internal model. Thus, we argue that VaR should not be blamed for misleading risk estimates during financial crisis. The third study evaluates VaR estimates using 5-minute sampling data of WTI Futures. First, we acknowledge the value of high-frequency data on the measure of volatility to characterize the quantile forecast of asset returns. Second, we find that quantile combination can improve the forecast accuracy. With the VaR implication, we show that VaR combination provides more accurate and robust results than individual VaR estimates.
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Novák, Martin. "Value at Risk models for Energy Risk Management." Master's thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-71889.

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The main focus of this thesis lies on description of Risk Management in context of Energy Trading. The paper will predominantly discuss Value at Risk and its modifications as a main overall indicator of Energy Risk.
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Heidrich, Matthias [Verfasser]. "Conditional Value-at-Risk Optimization for Credit Risk Using Asset Value Models / Matthias Heidrich." München : Verlag Dr. Hut, 2012. http://d-nb.info/1020299681/34.

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Hager, Peter. "Corporate Risk Management : Cash Flow at Risk und Value at Risk /." Frankfurt am Main : Bankakademie-Verl, 2004. http://www.gbv.de/dms/zbw/378196367.pdf.

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Samiei, Saeid. "Studies in value-at-risk." Thesis, Cardiff University, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.273586.

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CARVALHO, RENATO RANGEL LEAL DE. "EXTREME VALUE THEORY: VALUE AT RISK FOR FIXED-INCOME ASSETS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2006. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8245@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>A partir da década de 90, a metodologia Value at Risk (VaR) se difundiu pelo mundo, tanto em instituições financeiras quanto em não financeiras, como uma boa prática de mensuração de riscos. Em geral, abordagens paramétricas são muito utilizadas pelo mercado, apesar de freqüentemente não levarem em conta uma característica muito encontrada nas distribuições dos retornos de ativos financeiros: a presença de caudas pesadas. Uma abordagem baseada na Teoria dos Valores Extremos (TVE) é uma boa solução quando se deseja modelar caudas de distribuições probabilísticas que possuem tal característica. Em contra partida, poucos são os trabalhos que procuram desenvolver a TVE aplicada a ativos de renda-fixa. Com base nisto, este estudo propõe uma abordagem de simples implementação de cálculo de VaR para ativos de renda-fixa baseado na Teoria dos Valores Extremos.<br>Since the 90 decade, the use of Value at Risk (VaR) methodology has been disseminated among both financial and non-financial institutions around the world, as a good practice in terms of risks management. In spite of the fact that it does not take into account one of the most important characteristics of financial assets returns distribution - fat tails (excess of kurtosis), the parametric approach is the most used method for Value at Risk measurement. The Extreme Value Theory (EVT) is an alternative method that could be used to avoid the underestimation of Value at Risk, properly modeling the characteristics of probability distribution tails. However, there are few works that applied EVT to fixed-income market. Based on that, this study implements a simple approach to VaR calculation, in which the Extreme Value Theory is applied to fixed-income assets.
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PIRES, GUSTAVO LOURENÇO GOMES. "EXTREME VALUE THEORY: VALUE AT RISK FOR VARIABLE-INCOME ASSETS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2008. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=11850@1.

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COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>A partir da década de 90, a metodologia de Valor em Risco (VaR) se difundiu pelo mundo, tanto em instituições financeiras quanto em não financeiras, como uma boa prática de mensuração de riscos. Um dos fatos estilizados mais pronunciados acerca das distribuições de retornos financeiros diz respeito à presença de caudas pesadas. Isso torna os modelos paramétricos tradicionais de cálculo de Valor em Risco (VaR) inadequados para a estimação de VaR de baixas probabilidades, dado que estes se baseiam na hipótese de normalidade para as distribuições dos retornos. Sendo assim, o objetivo do presente trabalho é investigar o desempenho de modelos baseados na Teoria dos Valores Extremos para o cálculo do VaR. Os resultados indicam que os modelos baseados na Teoria dos Valores Extremos são adequados para a modelagem das caudas, e consequentemente para a estimação de Valor em Risco quando os níveis de probabilidade de interesse são baixos.<br>Since the 90 decade, the use of Value at Risk (VaR) methodology has been disseminated among both financial and non-financial institutions around the world, as a good practice in terms of risks management. The existence of fat tails is one of the striking stylized facts of financial returns distributions. This fact makes the use of traditional parametric models for Value at Risk (VaR) estimation unsuitable for the estimation of low probability events. This is because traditional models are based on the conditional normality assumption for financial returns distributions. The main purpose of this dissertation is to investigate the performance of VaR models based on Extreme Value Theory. The results indicates that Extreme Value Theory based models are suitable for low probability VaR estimation.
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Sampid, Marius Galabe. "Refining Value-at-Risk estimates : an extreme value theory approach." Thesis, University of Essex, 2018. http://repository.essex.ac.uk/22776/.

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This thesis proposes new approaches to Value-at-Risk estimation using (1) Multivariate GARCH Dynamic Conditional Correlation volatility model with skewed Student’s-t distributions, (2) Bayesian GARCH model with Student’s-t distribution, and (3) Bayesian Markov-Switching GJR-GARCH model with skewed Student’s-t distributions, incorporating copula functions and extreme value theory. A new approach for selecting a proper threshold in the Peaks Over Threshold method for extreme value theory analysis called the hybrid method is also proposed. The proposed Value-at-Risk models are compared to the traditional Value-at-Risk models commonly used by banks. Back-testing results following Kupiec (1995) unconditional coverage test, Christoffersen (1998) independent and conditional coverage test, Basel traffic light test, Santos and Alves (2012) new independent test, Dowd (2002) bootstrap back-test, and Engle and Manganelli (2004) Dynamic Quantile test show that Value-at-Risk models constructed following extreme value theory produced reliable Value-at-Risk estimates. Furthermore, Value-at-Risk models incorporating the hybrid method for threshold selection produced more stable Value-at-Risk estimates compared to the traditional Value-at-Risk models.
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Karlsson, Malin, and Jonna Flodman. "Value at Risk : A comparison of Value at Risk models during the 2007/2008 financial crisis." Thesis, Örebro universitet, Handelshögskolan vid Örebro universitet, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-16023.

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The financial crisis of 2007/2008 brought about a debate concerning the quality of risk management models, such as Value at Risk (VaR) models. Several studies have tried to make conclusions about multiple VaR models in periods around the crisis. The conclusions differ, but the Extreme Value Theory (EVT) is considered to be a good prediction model in times of unstable financial markets.  In this thesis, the VaR for six financial instruments; the OMXS 30, the OMX Stockholm Financials PI, the OMX Stockholm Materials PI and the currencies USD/SEK, GBP/SEK and EUR/SEK are estimated with the Historical Simulation, the Monte Carlo Simulation and the Variance- Covariance Method, with a 95 percent confidence interval. The risk is estimated both for single instruments as well as portfolios in times before, during and after the crisis with the purpose of concluding which of the VaR models more accurately predict risk for specific instruments/portfolios in different time periods of the crisis.   No direct conclusions can be made about the accuracy of the models before, during or after the crisis. The only clear conclusion can be drawn for the single instruments regarding the EUR. All methods predict more accurate results for this instrument compared to the other instruments. The clearest conclusion for the portfolios is that portfolios holding larger weights of indexes show on larger VaR estimations. Also, the modified Monte Carlo Simulation and the Variance-Covariance Method estimate lower risk in general than the Historical Simulation.
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Weisner, Torben. "Value-at-Risk and Extreme Events." Thesis, Uppsala University, Department of Mathematics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-130471.

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<p>The purpose of this thesis is to test the risk-measure Value-at-Riskand techniques for calculating it on data from the Financial Crisis of2007–2010. Different “pre-Financial Crisis” approaches to calculatingValue-at-Risk are considered, and tested on data from the period ofthe Financial Crisis. Also combinations of different approaches aretested.</p><p>Estimation of Value-at-Risk is done using the two different frame-works: Historical simulation (regular and the Hybrid approach) andparametric (conditional heteroscedastic) models.</p><p>The conditional heteroscedastic models considered are the EGARCHand the APARCH, calibrated using QMLE-methods. They are applied to the normal and Student’s t-distributions, Generalized ErrorDistribution and a non-parametric distribution. Consequently, a semi-parametric approach consisting of a non-parametric distribution alongwith an ARCH model is considered.</p><p>Quantile regression as by Koenker (1978) is used for the parameterestimation of the Historical simulation models used.</p><p>The Value-at Risk models are validated using Christoffersen’s con-ditional coverage test.Four stock indices (NIKKEI 225, NASDAQ 100, FTSE 100 andISEQ-overall) are evaluated, selected based on location and the re-gional effect of the Financial Crisis. Models are calibrated based ondata from before the Financial Crisis of 2007–2010, as the crisis isknown at present (April 2010).</p><p>It is found that the present approach to Value-at-Risk estimationcan not be considered redundant due to the extreme events of theFinancial Crisis.</p>
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Books on the topic "Value at risk"

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Rogers, Jamie. Strategy, Value and Risk. London: Palgrave Macmillan UK, 2009. http://dx.doi.org/10.1057/9780230353930.

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Rogers, Jamie. Strategy, Value and Risk. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21978-9.

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Rogers, Jamie. Strategy, Value and Risk. London: Palgrave Macmillan UK, 2013. http://dx.doi.org/10.1057/9780230392687.

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Wong, Max C. Y., ed. Bubble Value at Risk. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781119198925.

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Best, Philip. Implementing Value at Risk. Chichester, UK: John Wiley & Sons, Ltd, 1998. http://dx.doi.org/10.1002/0470013303.

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Dallas, Michael, ed. Value and Risk Management. Oxford, UK: Blackwell Publishing Ltd, 2006. http://dx.doi.org/10.1002/9780470759448.

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Inc, ebrary, ed. Derivatives, risk management & value. Hackensack, N.J: World Scientific, 2010.

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Dallas, Michael. Value and Risk Management. New York: John Wiley & Sons, Ltd., 2007.

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K, Bansal Vipul, ed. Measuring market risk with value at risk. New York: John Wiley, 2001.

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Dempster, M. A. H. 1938-, ed. Risk management: Value at risk and beyond. Cambridge: Cambridge University Press, 2002.

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Book chapters on the topic "Value at risk"

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Rabinowicz, Wlodek. "Incommensurability Meets Risk." In Value Incommensurability, 201–30. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003148012-15.

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Rogers, Jamie. "Risk." In Strategy, Value and Risk, 63–69. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21978-9_3.

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Rogers, Jamie. "Risk." In Strategy, Value and Risk, 49–56. London: Palgrave Macmillan UK, 2013. http://dx.doi.org/10.1057/9780230392687_4.

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Rogers, Jamie. "Value." In Strategy, Value and Risk, 29–62. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21978-9_2.

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Rogers, Jamie. "Value." In Strategy, Value and Risk, 23–48. London: Palgrave Macmillan UK, 2013. http://dx.doi.org/10.1057/9780230392687_3.

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Lee, Hongmu. "Value at Risk." In Risk Management, 75–87. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3468-0_7.

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Qingyi, Su. "GVC Risk Measurement." In Global Value Chains, 46–69. London: Routledge, 2023. http://dx.doi.org/10.4324/9781032670225-4.

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Rogers, Jamie. "Investment Risk." In Strategy, Value and Risk, 16–18. London: Palgrave Macmillan UK, 2009. http://dx.doi.org/10.1057/9780230353930_4.

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Rogers, Jamie. "Risk Management." In Strategy, Value and Risk, 68–70. London: Palgrave Macmillan UK, 2009. http://dx.doi.org/10.1057/9780230353930_9.

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Broll, Udo, and Jack E. Wahl. "Value at Risk." In Risikomanagement im Unternehmen, 35–48. Wiesbaden: Springer Fachmedien Wiesbaden, 2012. http://dx.doi.org/10.1007/978-3-8349-4047-6_4.

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Conference papers on the topic "Value at risk"

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Alvarado, Greg, Lynne Kaley, and Ricardo R. Valbuena. "Risk Based Inspection Demonstrating Value." In CORROSION 1999, 1–10. NACE International, 1999. https://doi.org/10.5006/c1999-99388.

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Abstract Tougher environmental and safety regulations and cost cutting are forcing refiners and petrochemical companies to search for new tools to manage the integrity of aging equipment. A Risk Based approach to equipment management can be used to quantify and compare the relative cost benefits of inspection practices and policies. In the refining and petrochemical industries, the use of risk analysis as a decision-making tool is gaining industry acceptance. Currently, the American Petroleum Institute (API) is developing a recommended practice for the use of Risk Based Inspection methodology.
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Ni, Xinyi, and Lifeng Lai. "Robust Risk-Sensitive Reinforcement Learning with Conditional Value-at-Risk." In 2024 IEEE Information Theory Workshop (ITW), 520–25. IEEE, 2024. https://doi.org/10.1109/itw61385.2024.10806953.

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Nowosielski, Ryan. "Managing Value Stream Risk." In General Aviation Technology Conference & Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2006. http://dx.doi.org/10.4271/2006-01-2389.

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Dash, Jan. "Stressed Value-at-Risk." In 2012 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr). IEEE, 2012. http://dx.doi.org/10.1109/cifer.2012.6327832.

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Gianfreda, Angelica, and Giacomo Scandolo. "ENERGY RISK MANAGEMENT BY VALUE-AT-RISK." In 2nd International Scientific Conference - Economics and Management: How to Cope With Disrupted Times. Association of Economists and Managers of the Balkans, Belgrade, Serbia; Faculty of Management Koper, Slovenia; Doba Business School - Maribor, Slovenia; Integrated Business Faculty - Skopje, Macedonia; Faculty of Management - Zajecar, Serbia, 2018. http://dx.doi.org/10.31410/eman.2018.280.

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"Value at Risk Estimation using Extreme Value Theory." In 19th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2011. http://dx.doi.org/10.36334/modsim.2011.d6.singh.

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Peng, Jin. "Measuring Fuzzy Risk by Credibilistic Value at Risk." In 2008 3rd International Conference on Innovative Computing Information and Control. IEEE, 2008. http://dx.doi.org/10.1109/icicic.2008.351.

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Zhong, J., and F. F. Wu. "Operating reserve value at risk." In 2006 IEEE Power Engineering Society General Meeting. IEEE, 2006. http://dx.doi.org/10.1109/pes.2006.1708859.

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Sanghvi, Anuj Dilip, and Ryan Cryar. "Cybersecurity Value-at-Risk Framework." In 2023 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2023. http://dx.doi.org/10.1109/pesgm52003.2023.10252996.

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Hepworth, Adam J., Michael P. Atkinson, and Roberto Szechtman. "A sequential elimination approach to value-at-risk and conditional value-at-risk selection." In 2017 Winter Simulation Conference (WSC). IEEE, 2017. http://dx.doi.org/10.1109/wsc.2017.8247963.

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Reports on the topic "Value at risk"

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Ketterer, Juan Antonio, and Agustina Calatayud. Integrated Value Chain Risk Management. Inter-American Development Bank, January 2016. http://dx.doi.org/10.18235/0010631.

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A widespread view in the private sector is that the lack of access to finance significantly limits the entry into and the performance of value chains. Access to finance is expensive, scarce, and short term in countries in Latin America and the Caribbean, and it hampers firms' investment and the financial management required to gain entry and remain as participants in a value chain. The lack of access to finance is a consequence of a series of market failures that form the basis for public policy intervention. The region's development banks and specialized agencies have thus designed programs to ease access to value chains and improve their performance. This paper suggests that the public sector could have a more effective role in enhancing value chain access and performance by embracing an integrated risk management approach to value chains. This approach will assist thepublic sector identify the various threats to which value chains are exposed, estimate the probability of occurrence and severity of such risks, and ensure risk prevention and mitigation through the use of a cost-effective combination of financial and nonfinancial instruments.
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Santos, Tano, and Pietro Veronesi. Cash-Flow Risk, Discount Risk, and the Value Premium. Cambridge, MA: National Bureau of Economic Research, December 2005. http://dx.doi.org/10.3386/w11816.

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Simpson, D. E. The societal impact value of risk. Office of Scientific and Technical Information (OSTI), April 1995. http://dx.doi.org/10.2172/80993.

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Adrian, Tobias, and Hyun Song Shin. Procyclical Leverage and Value-at-Risk. Cambridge, MA: National Bureau of Economic Research, April 2013. http://dx.doi.org/10.3386/w18943.

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Shin, Hyun-Han, and Rene Stulz. Firm Value, Risk, and Growth Opportunities. Cambridge, MA: National Bureau of Economic Research, July 2000. http://dx.doi.org/10.3386/w7808.

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Sanghvi, Anuj, Ryan Cryar, Jordan Smart, Nate Evans, Amanda Joyce, and Stephanie Jenkins. Hydropower Cybersecurity Value-at-Risk Framework. Office of Scientific and Technical Information (OSTI), February 2023. http://dx.doi.org/10.2172/1924011.

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Johnson, Jennifer. Intimate Partner Violence Risk Assessment: The Additive Value of Victim Reported Risk. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.7416.

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Engle, Robert, and Simone Manganelli. CAViaR: Conditional Value at Risk by Quantile Regression. Cambridge, MA: National Bureau of Economic Research, September 1999. http://dx.doi.org/10.3386/w7341.

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9

Borbinha, José. D4.4 Report on Risk, Benefit, Impact and Value. Collaboration to Clarify the Costs of Curation, November 2014. http://dx.doi.org/10.7207/4c-4.4.

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10

Bauer, Daniel, Darius Lakdawalla, and Julian Reif. Mortality Risk, Insurance, and the Value of Life. Cambridge, MA: National Bureau of Economic Research, September 2018. http://dx.doi.org/10.3386/w25055.

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