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Journal articles on the topic 'Value at risk'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Meraklı, Merve, and Simge Küçükyavuz. "Vector-valued multivariate conditional value-at-risk." Operations Research Letters 46, no. 3 (May 2018): 300–305. http://dx.doi.org/10.1016/j.orl.2018.02.006.

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12

Stefaniak, Radosław. "REVIEW OF VALUE AT RISK ESTIMATION METHODS." PRACE NAUKOWE UNIWERSYTETU EKONOMICZNEGO WE WROCŁAWIU, no. 519 (2018): 173–83. http://dx.doi.org/10.15611/pn.2018.519.14.

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13

Stambaugh, Fred. "Risk and value at risk." European Management Journal 14, no. 6 (December 1996): 612–21. http://dx.doi.org/10.1016/s0263-2373(96)00057-6.

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14

Kountur, Ronny. "The likelihood value of residual risk estimation in the management of enterprise risk." Investment Management and Financial Innovations 15, no. 3 (July 13, 2018): 49–55. http://dx.doi.org/10.21511/imfi.15(3).2018.04.

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A model for estimating the likelihood value of residual risk (Y) is introduced. The model consists of three independent variables: the likelihood value of risk before risk treatment (X1), the quality of risk treatment (X2), and the appropriateness of risk treatment (X3). An experimental research design with a multiple linear regression analysis was used in the estimation. All independent variables, the likelihood value of risk before treatment, the quality of risk treatment, and the appropriateness of risk treatment, can be significantly used to estimate the likelihood value of residual risk. Since no model of estimating residual risk of likelihood had been introduced yet, the findings of this study provide significant contribution to firms or organizations that need to assess the likelihood value of residual risks.

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15

Byczkowska, Katarzyna. "Katz Frailty Syndrom has no Predictive Value in Low-Risk Patients Undergoing Transcatheter Aortic Valve Implantation." Clinical Cardiology and Cardiovascular Interventions 04, no. 16 (October 12, 2021): 01–08. http://dx.doi.org/10.31579/2641-0419/227.

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Background: Aortic stenosis is a disease of the elderly people, with multiple comorbidities and often with the frailty syndrome. Therefore, we decided that frailty as a clinical factor requires precise characterization as it is a valuable supplement to the risk stratification in transcatheter aortic Valve implantation (TAVI). Objective: The aim of our study was to evaluate the prognostic value of the Katz frailty scale in patients undergoing TAVI in relation to the risk of mortality assessed with the STS scale. Material and methods: The study included 105 patients with severe aortic stenosis (AS) treated with TAVI at the Department of Invasive Cardiology, Central Clinical Hospital of the Ministry of Interior. In our group, the Katz frailty syndrome confirmed in all patients, and 48% in the advanced stage. Results: Statistical analysis showed a significant difference between survival and Katz frailty score before TAVI. Analysis using Cox's model confirmed a significant prognostic value for the Katz frailty syndrome before TAVI. Patients with moderate to severe frailty on the Katz score (values ≤ 4) had a 13,68 times higher risk of death per year compared to the group with Katz frailty syndrome ≥ 5. Multivariate regression analysis indicated that Katz frailty score and STS score were prognostically significant factors of cardiovascular death in patients undergoing TAVI. Conclusion: The Katz frailty score had a significant prognostic value in the high- and intermediate risk patients. Katz frailty score and STS risk score significantly correlated with the risk of death from cardiovascular causes in frailty patients undergoing TAVI.

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16

Marshall, Chris, and Michael Siegel. "Value at Risk." Journal of Derivatives 4, no. 3 (February 28, 1997): 91–111. http://dx.doi.org/10.3905/jod.1997.407975.

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17

Sackley, William H. "Value at Risk." CFA Digest 30, no. 4 (November 2000): 90–91. http://dx.doi.org/10.2469/dig.v30.n4.788.

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18

Chakraborty, Biplab. "Value at Risk." Management Accountant Journal 57, no. 7 (July 1, 2022): 81. http://dx.doi.org/10.33516/maj.v57i7.81-84p.

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19

Linsmeier, Thomas J., and Neil D. Pearson. "Value at Risk." Financial Analysts Journal 56, no. 2 (March 2000): 47–67. http://dx.doi.org/10.2469/faj.v56.n2.2343.

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20

Lechner, Lindsay A., and Timothy C. Ovaert. "Value‐at‐risk." Journal of Risk Finance 11, no. 5 (November 9, 2010): 464–80. http://dx.doi.org/10.1108/15265941011092059.

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21

MacMinn, Richard D. "Value and risk." Journal of Banking & Finance 26, no. 2-3 (March 2002): 297–301. http://dx.doi.org/10.1016/s0378-4266(01)00223-0.

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22

Seiter, Mischa, and Sven Eckert. "Value at Risk." Controlling 16, no. 7 (2004): 425–26. http://dx.doi.org/10.15358/0935-0381-2004-7-425.

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23

Sarin, Rakesh K., and Martin Weber. "Risk-value models." European Journal of Operational Research 70, no. 2 (October 1993): 135–49. http://dx.doi.org/10.1016/0377-2217(93)90033-j.

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24

Crespi, Giovanni Paolo, and Elisa Mastrogiacomo. "Qualitative robustness of set-valued value-at-risk." Mathematical Methods of Operations Research 91, no. 1 (February 2020): 25–54. http://dx.doi.org/10.1007/s00186-020-00707-9.

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25

Mangiero, Susan M. "Value at Risk and Derivatives Risk." CFA Digest 28, no. 2 (May 1998): 59–61. http://dx.doi.org/10.2469/dig.v28.n2.273.

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26

Pilbeam, Keith, and Rehan Noronha. "Risk budgeting and Value-at-Risk." International Journal of Monetary Economics and Finance 1, no. 2 (2008): 149. http://dx.doi.org/10.1504/ijmef.2008.019219.

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27

Strnad, Petr. "Market liquidity risk and its incorporation into value at risk." Acta Oeconomica Pragensia 17, no. 2 (April 1, 2009): 21–37. http://dx.doi.org/10.18267/j.aop.11.

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28

Miles, Ralph F. "Risk‐Adjusted Mission Value: Trading Off Mission Risk for Mission Value." Risk Analysis 24, no. 2 (April 2004): 415–24. http://dx.doi.org/10.1111/j.0272-4332.2004.00443.x.

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29

Peng, Jin. "Credibilistic Value and Average Value at Risk in Fuzzy Risk Analysis." Fuzzy Information and Engineering 3, no. 1 (March 2011): 69–79. http://dx.doi.org/10.1007/s12543-011-0067-8.

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30

Lim, Chee Yeow, and Patricia Mui-Siang Tan. "Value relevance of value-at-risk disclosure." Review of Quantitative Finance and Accounting 29, no. 4 (August 13, 2007): 353–70. http://dx.doi.org/10.1007/s11156-007-0038-7.

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31

Romeike, Frank. "Buchbesprechung: Corporate Risk Management— Cash Flow at Risk und Value at Risk von Peter Hager." RISKNEWS 1, no. 3 (June 2004): 71–72. http://dx.doi.org/10.1002/risk.200490065.

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32

Belles-Sampera, Jaume, Montserrat Guillén, and Miguel Santolino. "Beyond Value-at-Risk: GlueVaR Distortion Risk Measures." Risk Analysis 34, no. 1 (June 11, 2013): 121–34. http://dx.doi.org/10.1111/risa.12080.

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33

Kim, Gyuyeon, and Taewook Lee. "Bootstrap Value-at-Risk estimation based on CCC-GARCH models." Journal of the Korean Data And Information Science Sociaty 29, no. 3 (May 31, 2018): 747–67. http://dx.doi.org/10.7465/jkdi.2018.29.3.747.

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34

Mozes, Haim A. "The Risk in Value." Journal of Investing 29, no. 3 (February 9, 2020): 6–17. http://dx.doi.org/10.3905/joi.2020.1.119.

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35

Gaytán Cortés, Juan. "Value at Risk (VaR)." Mercados Y Negocios, no. 45 (January 1, 2022): 95–106. http://dx.doi.org/10.32870/myn.vi45.7665.g6726.

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The technique (VaR) is a statistical measure of the risk. It is associated with financial risks related to the high volatility in prices, interest rates, or exchange rates. It is used massively by entities because of the necessity to measure risk in constantly traded portfolios.

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36

Gaytán Cortés, Juan. "Value at Risk (VaR)." Mercados Y Negocios, no. 45 (January 1, 2022): 95–106. http://dx.doi.org/10.32870/myn.vi45.7665.

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Abstract:

The technique (VaR) is a statistical measure of the risk. It is associated with financial risks related to the high volatility in prices, interest rates, or exchange rates. It is used massively by entities because of the necessity to measure risk in constantly traded portfolios.

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37

Gaytán Cortés, Juan. "Value at Risk (VaR)." Mercados Y Negocios, no. 45 (January 1, 2022): 95–106. http://dx.doi.org/10.32870/myn.vi45.7665.g6732.

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Abstract:

The technique (VaR) is a statistical measure of the risk. It is associated with financial risks related to the high volatility in prices, interest rates, or exchange rates. It is used massively by entities because of the necessity to measure risk in constantly traded portfolios.

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38

MITSUSHITA, Kenta, and Shin MURAKOSHI. "Educational value in risk:." Taiikugaku kenkyu (Japan Journal of Physical Education, Health and Sport Sciences) 65 (2020): 19–33. http://dx.doi.org/10.5432/jjpehss.19048.

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39

Horsch, Andreas, and Bedia Jüttner. "Value-at-Risk-Varianten." WiSt - Wirtschaftswissenschaftliches Studium 47, no. 4 (2018): 48–50. http://dx.doi.org/10.15358/0340-1650-2018-4-48.

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40

Bernstein, Sheri, and Marni Gittleman. "The Value of Risk." Journal of Museum Education 35, no. 1 (April 2010): 43–58. http://dx.doi.org/10.1080/10598650.2010.11510649.

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41

Kihlbom, Ulrik. "Genetic risk and value." Journal of Risk Research 21, no. 2 (July 1, 2016): 222–35. http://dx.doi.org/10.1080/13669877.2016.1200653.

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42

Essert, Henry. "Risk and Enterprise Value." Geneva Papers on Risk and Insurance - Issues and Practice 27, no. 3 (July 2002): 435–43. http://dx.doi.org/10.1111/1468-0440.00183.

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43

ARRHENIUS, GUSTAF, and WLODEK RABINOWICZ. "VALUE AND UNACCEPTABLE RISK." Economics and Philosophy 21, no. 2 (October 2005): 177–97. http://dx.doi.org/10.1017/s0266267105000556.

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Consider a transitive value ordering of outcomes and lotteries on outcomes, which satisfies substitutivity of equivalents and obeys “continuity for easy cases,” i.e., allows compensating risks of small losses by chances of small improvements. Temkin (2001) has argued that such an ordering must also – rather counter-intuitively – allow chances of small improvements to compensate risks of huge losses. In this paper, we show that Temkin's argument is flawed but that a better proof is possible. However, it is more difficult to determine what conclusions should be drawn from this result. Contrary to what Temkin suggests, substitutivity of equivalents is a notoriously controversial principle. But even in the absence of substitutivity, the counter-intuitive conclusion is derivable from a strengthened version of continuity for easy cases. The best move, therefore, might be to question the latter principle, even in its original simple version: as we argue, continuity for easy cases gives rise to a sorites.

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44

Dyer, James S., and Jianmin Jia. "Relative risk—value models." European Journal of Operational Research 103, no. 1 (November 1997): 170–85. http://dx.doi.org/10.1016/s0377-2217(96)00254-8.

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45

Bridges, Glenys. "Radiographs: value v risk." Dental Nursing 16, no. 10 (October 2, 2020): 508–9. http://dx.doi.org/10.12968/denn.2020.16.10.508.

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46

MICHETTI, MELANIA. "VALUE AT RISK (VaR)." BANKPEDIA REVIEW 3, no. 2 (December 2013): 19–24. http://dx.doi.org/10.14612/michetti_2_2013.

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47

Wirch, Julia Lynn. "Raising Value at Risk." North American Actuarial Journal 3, no. 2 (April 1999): 106–15. http://dx.doi.org/10.1080/10920277.1999.10595804.

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48

Shen, Leo, and Robert J. Elliott. "How to value risk." Expert Systems with Applications 39, no. 5 (April 2012): 6111–15. http://dx.doi.org/10.1016/j.eswa.2011.11.006.

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49

Wu Xiaoqin. "Research on Portfolio Risk Value Adjustment Based on Value-at Risk Method." International Journal of Digital Content Technology and its Applications 7, no. 4 (February 28, 2013): 58–66. http://dx.doi.org/10.4156/jdcta.vol7.issue4.8.

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50

Chun, So Yeon, Alexander Shapiro, and Stan Uryasev. "Conditional Value-at-Risk and Average Value-at-Risk: Estimation and Asymptotics." Operations Research 60, no. 4 (August 2012): 739–56. http://dx.doi.org/10.1287/opre.1120.1072.

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