Valor en Riesgo y simulación: una revisión sistemática

El valor en riesgo es la medida de mercado utilizada por las instituciones financieras y adoptada por el Comité de Basilea para calcular y gestionar el riesgo, lo que la convierte en una medida necesaria para el sector financiero. En este artículo se realiza un estudio bibliométrico del Valor en Rie...

Full description

Autores:
Pineda Guerrero, Mauren Silene
Agudelo Aguirre, Alberto Antonio
Rojas Medina, Ricardo Alfredo
Duque Hurtado, Pedro Luis
Tipo de recurso:
Article of journal
Fecha de publicación:
2021
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
spa
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/11923
Acceso en línea:
https://doi.org/10.17981/econcuc.43.1.2022.Econ.3
Palabra clave:
Value at Risk
VaR
Bibliometric
Risk
Scientific Mapping
Riesgo
Valor en riesgo
VaR
Bibliometría
Mapeo científico
Rights
openAccess
License
ECONÓMICAS CUC - 2021
id RCUC2_08ee08ab19026ad5bac73222f25264e0
oai_identifier_str oai:repositorio.cuc.edu.co:11323/11923
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Valor en Riesgo y simulación: una revisión sistemática
dc.title.translated.eng.fl_str_mv Value at Risk and simulation: a systematic review
title Valor en Riesgo y simulación: una revisión sistemática
spellingShingle Valor en Riesgo y simulación: una revisión sistemática
Value at Risk
VaR
Bibliometric
Risk
Scientific Mapping
Riesgo
Valor en riesgo
VaR
Bibliometría
Mapeo científico
title_short Valor en Riesgo y simulación: una revisión sistemática
title_full Valor en Riesgo y simulación: una revisión sistemática
title_fullStr Valor en Riesgo y simulación: una revisión sistemática
title_full_unstemmed Valor en Riesgo y simulación: una revisión sistemática
title_sort Valor en Riesgo y simulación: una revisión sistemática
dc.creator.fl_str_mv Pineda Guerrero, Mauren Silene
Agudelo Aguirre, Alberto Antonio
Rojas Medina, Ricardo Alfredo
Duque Hurtado, Pedro Luis
dc.contributor.author.spa.fl_str_mv Pineda Guerrero, Mauren Silene
Agudelo Aguirre, Alberto Antonio
Rojas Medina, Ricardo Alfredo
Duque Hurtado, Pedro Luis
dc.subject.eng.fl_str_mv Value at Risk
VaR
Bibliometric
Risk
Scientific Mapping
topic Value at Risk
VaR
Bibliometric
Risk
Scientific Mapping
Riesgo
Valor en riesgo
VaR
Bibliometría
Mapeo científico
dc.subject.spa.fl_str_mv Riesgo
Valor en riesgo
VaR
Bibliometría
Mapeo científico
description El valor en riesgo es la medida de mercado utilizada por las instituciones financieras y adoptada por el Comité de Basilea para calcular y gestionar el riesgo, lo que la convierte en una medida necesaria para el sector financiero. En este artículo se realiza un estudio bibliométrico del Valor en Riesgo (VaR) y su cálculo mediante procesos de simulación. Para ello se revisan las investigaciones publicadas en los últimos 20 años en las bases de datos Scopus y Web of Science, recopilando los documentos más relevantes para su análisis. Posteriormente se presenta la justificación del tema y se elabora la red social utilizando la analogía del árbol, en la que cada uno de los documentos más importantes se clasifican como raíz, tronco u hoja. Finalmente, se identifican las perspectivas de investigación del tema mediante un análisis de co-citaciones. Se concluye que las mujeres tienen un alto grado de participación en cargos gerenciales, sin embargo, se nota una diferencia significativa de 3.492.556 pesos en los salarios de los dos sexos, donde los hombres son quienes obtiene mayores ingresos.
publishDate 2021
dc.date.issued.none.fl_str_mv 2021-01-01
dc.date.accessioned.none.fl_str_mv 2022-01-01 00:00:00
dc.date.available.none.fl_str_mv 2022-01-01 00:00:00
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.local.eng.fl_str_mv Journal article
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/ART
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.coarversion.spa.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
format http://purl.org/coar/resource_type/c_6501
status_str publishedVersion
dc.identifier.issn.none.fl_str_mv 0120-3932
dc.identifier.url.none.fl_str_mv https://doi.org/10.17981/econcuc.43.1.2022.Econ.3
dc.identifier.doi.none.fl_str_mv 10.17981/econcuc.43.1.2022.Econ.3
dc.identifier.eissn.none.fl_str_mv 2382-3860
identifier_str_mv 0120-3932
10.17981/econcuc.43.1.2022.Econ.3
2382-3860
url https://doi.org/10.17981/econcuc.43.1.2022.Econ.3
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.ispartofjournal.spa.fl_str_mv Económicas CUC
dc.relation.references.spa.fl_str_mv Andersonn, F., Mausser, H., Rosen, D. & Uryasev, S. (2001). Credit risk optimization with Conditional Value-at-Risk criterion. Mathemastical Programming, 89(2), 273–291. https://doi.org/10.1007/PL00011399
Andriosopoulos, K. & Nomikos, N. (2015). Risk management in the energy markets and Value-at-Risk modelling: a hybrid approach. The European Journal of Finance, 21(7), 548–574. https://doi.org/10.1080/1351847X.2013.862173
Aria, M, y Cuccurullo, C. (2017). Bibliometrix: una herramienta R para el análisis exhaustivo de la cartografía científica. Journal of Informetrics, 11(4), 959–975. Disponible en https://doi.org/10.1016/j.joi.2017.08.007
Artzner, P., Delbaen, F., Eber, J. & Heath, D. (1999). Coherent Measures of Risk. Mathematical Finance, 9(3), 203–228. https://doi.org/10.1111/1467-9965.00068
Asimit, V., Peng, L., Wang, R. y Yu, A. (2019). An efficient approach to quantile capital allocation and sensitivity analysis. Mathematical Finance, 29(4), 1131–1156. https://doi.org/10.1111/mafi.12211
Babazadeh, H. & Esfahanipour, A. (2019). A novel multi period mean-VaR portfolio optimization model considering practical constraints and transaction cost. Journal of Computational and Applied Mathematics, 361(1), 313–342. https://doi.org/10.1016/j.cam.2018.10.039
Bastian, M., Heymann, S. & Jacomy, M. (May. 2009). Gephi: An Open Source Software for Exploring and Manipulating Networks. Presentation at Third International Conference on Weblogs and Social Media. ICWSM, San José, CA, USA. https://doi.org/10.13140/2.1.1341.1520
BIS. (2011). El Nuevo Acuerdo de Capital de Basilea. [Documento Consultivo]. Basilea: BIS. Recuperado de https://www.bis.org/publ/bcbsca03_s.pdf
Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1 Broadie, M., Du, Y. & Moallemi, C. C. (2011). Efficient Risk Estimation via Nested Sequential Simulation. Management Science, 57(6), 1172–1194. https://doi.org/10.1287/mnsc.1110.1330
Buitrago, S., Duque, P. y Robledo, S. (2020). Branding Corporativo: una revisión bibliográfica. Económicas CUC, 41(1), 143–162. https://doi.org/10.17981/econcuc.41.1.2020.Org.1 Chen, Q., Gerlach, R. & Lu, Z. (2012). Bayesian Value-at-Risk and expected shortfall forecasting via the asymmetric Laplace distribution. Computational Statistics and Data Analysis, 56(11), 3498–3516. https://doi.org/10.1016/j.csda.2010.06.018
Colletaz, G., Hurlin, C. & Pérignon, C. (2013). The Risk Map: A new tool for validating risk models. Journal of Banking & Finance, 37(10), 3843–3854. https://doi.org/10.1016/j.jbankfin.2013.06.006
Dahlgreen, R., Ching, C. & Lawarree, J. (2003). Risk Assessment in Energy Trading. IEEE Transactions on Power Systems, 18(2), 503–511. https://doi.org/10.1109/TPWRS.2003.810685
Du, Z. & Escanciano, J. C. (2017). Backtesting Expected Shortfall: Accounting for Tail Risk. Management Science, 63(4), 940–958. https://doi.org/10.1287/mnsc.2015.2342
Duffie, D. & Pan, J. (1997). An Overview of Value at Risk. The Journal of Derivatives. 4(3), 7–49. https://doi.org/10.3905/JOD.1997.407971
Duque, P. y Cervantes-Cervantes, L.-S. (2019). Responsabilidad Social Universitaria: una revisión sistemática y análisis bibliométrico. Estudios Gerenciales, 35(153), 451–464. https://doi.org/10.18046/j.estger.2019.153.3389
Duque, P. y Duque, E. (2020). Marketing digital y comercio electrónico: un análisis bibliométrico. En: M. Redondo, A. Barrera y C. Duque (eds.), Nuevos modelos de negocio (pp. 73–96). Pereira: Unilibre. Disponible en https://hdl.handle.net/10901/18463
Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987–1007. https://doi.org/10.2307/1912773
Engle, R. F. & Manganelli, S. (2004). CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles. Journal of Business & Economic Statistics, 22,367–381. https://doi.org/10.1198/073500104000000370
Escanciano, J. C. & Pei, P. (2012). Pitfalls in backtesting Historical Simulation VaR models. Journal of Banking & Finance, 36(8), 2233–2244. https://doi.org/10.1016/j.jbankfin.2012.04.004
Gaglianone, W., Renato, L., Linton, O. & Smith, D. (2011). Evaluating Value-at-Risk Models via Quantile Regression. Journal of Business & Economic Statistics, 29(1), 150–160. https://doi.org/10.1198/jbes.2010.07318 Gaio, L., Pimenta, T., Guasti, F., Passos, I. & Oliveira, N. (2018). Value-at-risk performance in emerging and developed countries. International Journal of Managerial Finance, 14(5), 591–612. https://doi.org/10.1108/IJMF-10-2017-0244
Gephi. (versión 0.9.2). Gephi. [Visualization and exploration software for graphs and networks]. Compiègne: UTC. Available: https://gephi.org
Gencay, R. & Selcuk, F. (2004). Extreme value theory and Value-at-Risk: Relative performance in emerging markets. International Journal of Forecasting, 20(2), 287–303. https://doi.org/10.1016/j.ijforecast.2003.09.005
Gençay, R., Selçuk, F. & Ulugülyagci, A. (2003). High volatility, thick tails and extreme value theory in value-at-risk estimation. Insurance. Mathematics and Economics, 33(2), 337–356. https://doi.org/10.1016/J.INSMATHECO.2003.07.004
Glasserman, P. & Li, J. (2005). Importance Sampling for Portfolio Credit Risk. Management Science, 51(11), 1643–1656. https://doi.org/10.1287/mnsc.1050.0415
Glasserman, P., Heilderberg, P. & Shahabuddin, P. (2002). Portfolio value-at-risk with heavy-tailed risk factors. Mathematical Finance, 12(3), 239–269. https://doi.org/10.1111/1467-9965.00141
Glosten, L. R., Jagannathan, R. & Runkle, D. E. (1993). On the Relation between the Expected Value and the Volatility of the Nominal Excess. The Journal of Finance, 48(5), 1779–1801. https://doi.org/10.2307/2329067
Gordy, M. B. & Juneja, S. (2010). Nested Simulation in Portfolio Risk Measurement. Management Science, 56(10), 1833–1848. https://doi.org/10.1287/mnsc.1100.1213
Hong, L. J. (2009). Estimating Quantile Sensitivities. Operations Research, 57(1), 118–130. https://doi.org/10.1287/opre.1080.0531
Huang, D., Zhub, S., Fabozzi, F. & Fukushima, M. (2008). Portfolio selection with uncertain exit time: A robust CVaR approach. Journal of Economic Dynamics & Control, 32(2), 594–623. https://doi.org/10.1016/j.jedc.2007.03.003
Jabr, R. A. (2005). Robust Self-Scheduling Under Price Uncertainty Using Conditional Value-at-Risk. IEEE Transactions on Power Systems, 20(4), 1852–1858. https://doi.org/10.1109/TPWRS.2005.856952
Kennedy, J. & Elberhart, R. (Nov. 1995). Particle Swarm Optimization. Presented at Third International Conference on Neural Networks, ICNN, Perth, WA, Australia. https://doi.org/10.1109/ICNN.1995.488968
Kupiec, P. H. (1995). Techniques for Verifying the Accuracy of Risk Measurement Models. The Journal of Derivatives, 3(2), 73–84. https://doi.org/10.3905/JOD.1995.407942
Kuester, K., Mittnik, S. & Paoella, M. (2005). Value-at-Risk Prediction: A Comparison of Alternative Strategies. Journal of Financial Econometrics, 4(1), 53–89. https://doi.org/10.1093/jjfinec/nbj002
Liu, B. & Liu, Y.-K. (2002). Expected Value of Fuzzy Variable and Fuzzy Expected Value Models. IEEE Transactions on Fuzzy Systems, 10(4), 445–450. https://doi.org/10.1109/TFUZZ.2002.800692
Lwin, K. T., Qu, R. & MacCarthy, B. L. (2017). Mean-VaR Portfolio Optimization: A Nonparametric Approach. European Journal of Operational Research, 260(2), 751–766. https://doi.org/10.1016/j.ejor.2017.01.005
Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77–91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.x
Marimoutou, V., Raggad, B. & Trabelsi, A. (2009). Extreme Value Theory and Value at Risk: Application to oil market. Energy Economics, 31(4), 519–530. https://doi.org/10.1016/j.eneco.2009.02.005
McNeil, A. J. & Frey, R. (2000). Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach. Journal of Empirical Finance, 7(3-4), 271–300. https://doi.org/10.1016/S0927-5398(00)00012-8
Moazeni, S., Coleman, T. F. & Li, Y. (2013). Smoothing and parametric rules for stochastic mean-CVaR optimal execution strategy. Annals of Operations Research, 237, 99–120. https://doi.org/10.1007/s10479-013-1391-7
Muller, F. M. & Brutti, M. (2017). Numerical comparison of multivariate models to forecasting risk measures. Risk Management, 20(1), 29–50. https://doi. org/10.1057/s41283-017-0026-8
Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347–370. https://doi.org/10.2307/2938260
Novales, A., & Garcia-Jorcano, L. (2018). Backtesting extreme value theory models of expected shortfall. Quantitative Finance, 19(5), 799–825. https://doi.org/10.1080/14697688.2018.1535182
Paolella, M. (2017). The Univariate Collapsing Method for Portfolio Optimization. Econometrics, 5(2), 1–33. https://doi.org/10.3390/econometrics5020018
Perignon, C. & Smith, D. (2010). The level and quality of Value-at-Risk disclosure by commercial Banks. Journal of Banking & Finance, 34(2), 362–377. https://doi.org/10.1016/J.JBANKFIN.2009.08.009
Pritsker, M. (2006). The hidden dangers of historical simulation. Journal of Banking & Finance, 30(2), 561–582. https://doi.org/10.1016/j.jbankfin.2005.04.013
R. (version 3). Statistical computing and graphics. [Software environment]. Vienna: R Core Team. Available: https://www.r-project.org/
Robledo, S., Osorio, G. y López, C. (2014). Networking en pequeña empresa: una revisión bibliográfica utilizando la teoria de grafos. Revista Vínculos, 11(2), 117–134. https://revistas.udistrital.edu.co/index.php/vinculos/article/view/9664
Rockafellar, R. T. & Uryasev, S. (2000). Optimization of conditional value-at-risk. Risk.net, 2(3), 21–41. https://doi.org/10.21314/JOR.2000.038
Scheller, F. & Auer, B. (2018). How does the choice of Value-at-Risk estimator influence asset allocation decisions? Quantitative Finance, 18(12), 2005–2022. https://doi.org/10.1080/14697688.2018.1459806
Sci2 Tool. (versión 1.3). Science of Science. [Software]. Bloomington: CNS. Available: https://sci2.cns.iu.edu/user/index.php
Vijverberg, C.-P. C., Vijverberg, W. P. M. & Taşpınar, S. (2015). Linking Tukey’s Legacy to Financial Risk Measurement. Computational Statistics and Data Analysis, 100, 595–615. https://doi.org/10.1016/j.csda.2015.08.018
Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge: CUP. https://doi.org/10.1017/CBO9780511815478
Wang, B., Wang, S. & Watadata, J. (2011). Fuzzy-Portfolio-Selection Models With Value-at-Risk. IEEE Transactions on Fuzzy Systems, 19(4), 758–769. https:// doi.org/10.1109/TFUZZ.2011.2144599
Wang, C., Chen, Q. & Gerlach, R. (2018). Bayesian realized-GARCH models for financial tail risk forecasting incorporating the two-sided Weibull distribution. Quantitative Finance, 19(6), 1017–1042. https://doi.org/10.1080/14697688.2018.1540880
Xu, L. (2014). Model-free inference for tail risk measures. Econometric Theory, 34(3), 1–32. https://doi.org/10.1017/S0266466614000802
Yao, H., Li, Z. & Lai, Y. (2013). Mean–CVaR portfolio selection: A nonparametric estimation framework. Computers & Operations Research, 40(4), 1014–1044. https://doi.org/10.1016/J.COR.2012.11.007
Zaichao, Du. & Escanciano, J. C. (2016). Backtesting Expected Shortfall: Accounting for Tail Risk. Management Science, 63(4), 940–958. https://doi.org/10.1287/mnsc.2015.2342
Zhang, L., Luo, M. & Boncella, R. (2017). Product information diffusion in a social network. Electronic Commerce Research, 20(1), 3–19. https://doi.org/10.1007/s10660-018-9316-9
Zhang, Q. & Wang, X. (2009). Hedge Contract Characterization and Risk-Constrained Electricity Procurement. IEEE Transactions on Power Systems, 24(3), 1547–1548. https://doi.org/10.1109/TPWRS.2009.2021233
Ziggel, D., Berens, T., Weib, G. & Wied, D. (2014). A new set of improved Valueat-Risk back tests. Journal of Banking & Finance, 48(C), 29–41. https://doi.org/10.1016/J.JBANKFIN.2014.07.005
Zuluaga, M., Robledo, S., Osorio, G. A., Yathe, L., González, D. & Taborda, G. (2016). Metabolómica y Pesticidas: Revisión sistemática de literatura usando teoría de grafos para el análisis de referencias. Nova, 14(25), 121–128. https://doi.org/10.22490/24629448.1735
dc.relation.citationendpage.none.fl_str_mv 82
dc.relation.citationstartpage.none.fl_str_mv 57
dc.relation.citationissue.spa.fl_str_mv 1
dc.relation.citationvolume.spa.fl_str_mv 43
dc.relation.bitstream.none.fl_str_mv https://revistascientificas.cuc.edu.co/economicascuc/article/download/3093/3691
https://revistascientificas.cuc.edu.co/economicascuc/article/download/3093/4022
https://revistascientificas.cuc.edu.co/economicascuc/article/download/3093/4023
https://revistascientificas.cuc.edu.co/economicascuc/article/download/3093/4024
dc.relation.citationedition.spa.fl_str_mv Núm. 1 , Año 2022
dc.rights.spa.fl_str_mv ECONÓMICAS CUC - 2021
dc.rights.uri.spa.fl_str_mv https://creativecommons.org/licenses/by-nc-nd/4.0
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.coar.spa.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv ECONÓMICAS CUC - 2021
https://creativecommons.org/licenses/by-nc-nd/4.0
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.mimetype.spa.fl_str_mv application/pdf
text/html
application/xml
application/epub+zip
dc.publisher.spa.fl_str_mv Universidad de la Costa
dc.source.spa.fl_str_mv https://revistascientificas.cuc.edu.co/economicascuc/article/view/3093
institution Corporación Universidad de la Costa
bitstream.url.fl_str_mv https://repositorio.cuc.edu.co/bitstreams/e9d28f01-f7b6-48a0-aa5a-0b98e54f7c28/download
bitstream.checksum.fl_str_mv 3a1414d4f5dabf45bbea84991f99c79f
bitstream.checksumAlgorithm.fl_str_mv MD5
repository.name.fl_str_mv Repositorio de la Universidad de la Costa CUC
repository.mail.fl_str_mv repdigital@cuc.edu.co
_version_ 1828166832534585344
spelling Pineda Guerrero, Mauren SileneAgudelo Aguirre, Alberto AntonioRojas Medina, Ricardo AlfredoDuque Hurtado, Pedro Luis2022-01-01 00:00:002022-01-01 00:00:002021-01-010120-3932https://doi.org/10.17981/econcuc.43.1.2022.Econ.310.17981/econcuc.43.1.2022.Econ.32382-3860El valor en riesgo es la medida de mercado utilizada por las instituciones financieras y adoptada por el Comité de Basilea para calcular y gestionar el riesgo, lo que la convierte en una medida necesaria para el sector financiero. En este artículo se realiza un estudio bibliométrico del Valor en Riesgo (VaR) y su cálculo mediante procesos de simulación. Para ello se revisan las investigaciones publicadas en los últimos 20 años en las bases de datos Scopus y Web of Science, recopilando los documentos más relevantes para su análisis. Posteriormente se presenta la justificación del tema y se elabora la red social utilizando la analogía del árbol, en la que cada uno de los documentos más importantes se clasifican como raíz, tronco u hoja. Finalmente, se identifican las perspectivas de investigación del tema mediante un análisis de co-citaciones. Se concluye que las mujeres tienen un alto grado de participación en cargos gerenciales, sin embargo, se nota una diferencia significativa de 3.492.556 pesos en los salarios de los dos sexos, donde los hombres son quienes obtiene mayores ingresos.Value at Risk is the market measure used by financial institutions and adopted by the Basel Committee to calculate and manage risk, making it a necessary measure for the financial sector. In this article, a bibliometric study of Value at Risk (VaR) is carried out and its calculation using simulation processes. For this purpose, a review was made of the research published over the last 20 years in the Scopus and Web of Science databases, compiling the most relevant documents for analysis. Subsequently, the justification of the topic is presented, and the social network is elaborated using the tree analogy, in which each of the most important documents is classified as root, stem, or leaf. Finally, the research perspectives of the topic are identified through a cocitations analysis. It is concluded that women have a high degree of participation in managerial positions, however, a significant difference of 3,492,556 pesos is noted in the salaries of the two sexes, where men are the ones who obtain the highest income.application/pdftext/htmlapplication/xmlapplication/epub+zipspaUniversidad de la CostaECONÓMICAS CUC - 2021https://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.http://purl.org/coar/access_right/c_abf2https://revistascientificas.cuc.edu.co/economicascuc/article/view/3093Value at RiskVaRBibliometricRiskScientific MappingRiesgoValor en riesgoVaRBibliometríaMapeo científicoValor en Riesgo y simulación: una revisión sistemáticaValue at Risk and simulation: a systematic reviewArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articleJournal articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Económicas CUCAndersonn, F., Mausser, H., Rosen, D. & Uryasev, S. (2001). Credit risk optimization with Conditional Value-at-Risk criterion. Mathemastical Programming, 89(2), 273–291. https://doi.org/10.1007/PL00011399Andriosopoulos, K. & Nomikos, N. (2015). Risk management in the energy markets and Value-at-Risk modelling: a hybrid approach. The European Journal of Finance, 21(7), 548–574. https://doi.org/10.1080/1351847X.2013.862173Aria, M, y Cuccurullo, C. (2017). Bibliometrix: una herramienta R para el análisis exhaustivo de la cartografía científica. Journal of Informetrics, 11(4), 959–975. Disponible en https://doi.org/10.1016/j.joi.2017.08.007Artzner, P., Delbaen, F., Eber, J. & Heath, D. (1999). Coherent Measures of Risk. Mathematical Finance, 9(3), 203–228. https://doi.org/10.1111/1467-9965.00068Asimit, V., Peng, L., Wang, R. y Yu, A. (2019). An efficient approach to quantile capital allocation and sensitivity analysis. Mathematical Finance, 29(4), 1131–1156. https://doi.org/10.1111/mafi.12211Babazadeh, H. & Esfahanipour, A. (2019). A novel multi period mean-VaR portfolio optimization model considering practical constraints and transaction cost. Journal of Computational and Applied Mathematics, 361(1), 313–342. https://doi.org/10.1016/j.cam.2018.10.039Bastian, M., Heymann, S. & Jacomy, M. (May. 2009). Gephi: An Open Source Software for Exploring and Manipulating Networks. Presentation at Third International Conference on Weblogs and Social Media. ICWSM, San José, CA, USA. https://doi.org/10.13140/2.1.1341.1520BIS. (2011). El Nuevo Acuerdo de Capital de Basilea. [Documento Consultivo]. Basilea: BIS. Recuperado de https://www.bis.org/publ/bcbsca03_s.pdfBollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327. https://doi.org/10.1016/0304-4076(86)90063-1 Broadie, M., Du, Y. & Moallemi, C. C. (2011). Efficient Risk Estimation via Nested Sequential Simulation. Management Science, 57(6), 1172–1194. https://doi.org/10.1287/mnsc.1110.1330Buitrago, S., Duque, P. y Robledo, S. (2020). Branding Corporativo: una revisión bibliográfica. Económicas CUC, 41(1), 143–162. https://doi.org/10.17981/econcuc.41.1.2020.Org.1 Chen, Q., Gerlach, R. & Lu, Z. (2012). Bayesian Value-at-Risk and expected shortfall forecasting via the asymmetric Laplace distribution. Computational Statistics and Data Analysis, 56(11), 3498–3516. https://doi.org/10.1016/j.csda.2010.06.018Colletaz, G., Hurlin, C. & Pérignon, C. (2013). The Risk Map: A new tool for validating risk models. Journal of Banking & Finance, 37(10), 3843–3854. https://doi.org/10.1016/j.jbankfin.2013.06.006Dahlgreen, R., Ching, C. & Lawarree, J. (2003). Risk Assessment in Energy Trading. IEEE Transactions on Power Systems, 18(2), 503–511. https://doi.org/10.1109/TPWRS.2003.810685Du, Z. & Escanciano, J. C. (2017). Backtesting Expected Shortfall: Accounting for Tail Risk. Management Science, 63(4), 940–958. https://doi.org/10.1287/mnsc.2015.2342Duffie, D. & Pan, J. (1997). An Overview of Value at Risk. The Journal of Derivatives. 4(3), 7–49. https://doi.org/10.3905/JOD.1997.407971Duque, P. y Cervantes-Cervantes, L.-S. (2019). Responsabilidad Social Universitaria: una revisión sistemática y análisis bibliométrico. Estudios Gerenciales, 35(153), 451–464. https://doi.org/10.18046/j.estger.2019.153.3389Duque, P. y Duque, E. (2020). Marketing digital y comercio electrónico: un análisis bibliométrico. En: M. Redondo, A. Barrera y C. Duque (eds.), Nuevos modelos de negocio (pp. 73–96). Pereira: Unilibre. Disponible en https://hdl.handle.net/10901/18463Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987–1007. https://doi.org/10.2307/1912773Engle, R. F. & Manganelli, S. (2004). CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles. Journal of Business & Economic Statistics, 22,367–381. https://doi.org/10.1198/073500104000000370Escanciano, J. C. & Pei, P. (2012). Pitfalls in backtesting Historical Simulation VaR models. Journal of Banking & Finance, 36(8), 2233–2244. https://doi.org/10.1016/j.jbankfin.2012.04.004Gaglianone, W., Renato, L., Linton, O. & Smith, D. (2011). Evaluating Value-at-Risk Models via Quantile Regression. Journal of Business & Economic Statistics, 29(1), 150–160. https://doi.org/10.1198/jbes.2010.07318 Gaio, L., Pimenta, T., Guasti, F., Passos, I. & Oliveira, N. (2018). Value-at-risk performance in emerging and developed countries. International Journal of Managerial Finance, 14(5), 591–612. https://doi.org/10.1108/IJMF-10-2017-0244Gephi. (versión 0.9.2). Gephi. [Visualization and exploration software for graphs and networks]. Compiègne: UTC. Available: https://gephi.orgGencay, R. & Selcuk, F. (2004). Extreme value theory and Value-at-Risk: Relative performance in emerging markets. International Journal of Forecasting, 20(2), 287–303. https://doi.org/10.1016/j.ijforecast.2003.09.005Gençay, R., Selçuk, F. & Ulugülyagci, A. (2003). High volatility, thick tails and extreme value theory in value-at-risk estimation. Insurance. Mathematics and Economics, 33(2), 337–356. https://doi.org/10.1016/J.INSMATHECO.2003.07.004Glasserman, P. & Li, J. (2005). Importance Sampling for Portfolio Credit Risk. Management Science, 51(11), 1643–1656. https://doi.org/10.1287/mnsc.1050.0415Glasserman, P., Heilderberg, P. & Shahabuddin, P. (2002). Portfolio value-at-risk with heavy-tailed risk factors. Mathematical Finance, 12(3), 239–269. https://doi.org/10.1111/1467-9965.00141Glosten, L. R., Jagannathan, R. & Runkle, D. E. (1993). On the Relation between the Expected Value and the Volatility of the Nominal Excess. The Journal of Finance, 48(5), 1779–1801. https://doi.org/10.2307/2329067Gordy, M. B. & Juneja, S. (2010). Nested Simulation in Portfolio Risk Measurement. Management Science, 56(10), 1833–1848. https://doi.org/10.1287/mnsc.1100.1213Hong, L. J. (2009). Estimating Quantile Sensitivities. Operations Research, 57(1), 118–130. https://doi.org/10.1287/opre.1080.0531Huang, D., Zhub, S., Fabozzi, F. & Fukushima, M. (2008). Portfolio selection with uncertain exit time: A robust CVaR approach. Journal of Economic Dynamics & Control, 32(2), 594–623. https://doi.org/10.1016/j.jedc.2007.03.003Jabr, R. A. (2005). Robust Self-Scheduling Under Price Uncertainty Using Conditional Value-at-Risk. IEEE Transactions on Power Systems, 20(4), 1852–1858. https://doi.org/10.1109/TPWRS.2005.856952Kennedy, J. & Elberhart, R. (Nov. 1995). Particle Swarm Optimization. Presented at Third International Conference on Neural Networks, ICNN, Perth, WA, Australia. https://doi.org/10.1109/ICNN.1995.488968Kupiec, P. H. (1995). Techniques for Verifying the Accuracy of Risk Measurement Models. The Journal of Derivatives, 3(2), 73–84. https://doi.org/10.3905/JOD.1995.407942Kuester, K., Mittnik, S. & Paoella, M. (2005). Value-at-Risk Prediction: A Comparison of Alternative Strategies. Journal of Financial Econometrics, 4(1), 53–89. https://doi.org/10.1093/jjfinec/nbj002Liu, B. & Liu, Y.-K. (2002). Expected Value of Fuzzy Variable and Fuzzy Expected Value Models. IEEE Transactions on Fuzzy Systems, 10(4), 445–450. https://doi.org/10.1109/TFUZZ.2002.800692Lwin, K. T., Qu, R. & MacCarthy, B. L. (2017). Mean-VaR Portfolio Optimization: A Nonparametric Approach. European Journal of Operational Research, 260(2), 751–766. https://doi.org/10.1016/j.ejor.2017.01.005Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77–91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.xMarimoutou, V., Raggad, B. & Trabelsi, A. (2009). Extreme Value Theory and Value at Risk: Application to oil market. Energy Economics, 31(4), 519–530. https://doi.org/10.1016/j.eneco.2009.02.005McNeil, A. J. & Frey, R. (2000). Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach. Journal of Empirical Finance, 7(3-4), 271–300. https://doi.org/10.1016/S0927-5398(00)00012-8Moazeni, S., Coleman, T. F. & Li, Y. (2013). Smoothing and parametric rules for stochastic mean-CVaR optimal execution strategy. Annals of Operations Research, 237, 99–120. https://doi.org/10.1007/s10479-013-1391-7Muller, F. M. & Brutti, M. (2017). Numerical comparison of multivariate models to forecasting risk measures. Risk Management, 20(1), 29–50. https://doi. org/10.1057/s41283-017-0026-8Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347–370. https://doi.org/10.2307/2938260Novales, A., & Garcia-Jorcano, L. (2018). Backtesting extreme value theory models of expected shortfall. Quantitative Finance, 19(5), 799–825. https://doi.org/10.1080/14697688.2018.1535182Paolella, M. (2017). The Univariate Collapsing Method for Portfolio Optimization. Econometrics, 5(2), 1–33. https://doi.org/10.3390/econometrics5020018Perignon, C. & Smith, D. (2010). The level and quality of Value-at-Risk disclosure by commercial Banks. Journal of Banking & Finance, 34(2), 362–377. https://doi.org/10.1016/J.JBANKFIN.2009.08.009Pritsker, M. (2006). The hidden dangers of historical simulation. Journal of Banking & Finance, 30(2), 561–582. https://doi.org/10.1016/j.jbankfin.2005.04.013R. (version 3). Statistical computing and graphics. [Software environment]. Vienna: R Core Team. Available: https://www.r-project.org/Robledo, S., Osorio, G. y López, C. (2014). Networking en pequeña empresa: una revisión bibliográfica utilizando la teoria de grafos. Revista Vínculos, 11(2), 117–134. https://revistas.udistrital.edu.co/index.php/vinculos/article/view/9664Rockafellar, R. T. & Uryasev, S. (2000). Optimization of conditional value-at-risk. Risk.net, 2(3), 21–41. https://doi.org/10.21314/JOR.2000.038Scheller, F. & Auer, B. (2018). How does the choice of Value-at-Risk estimator influence asset allocation decisions? Quantitative Finance, 18(12), 2005–2022. https://doi.org/10.1080/14697688.2018.1459806Sci2 Tool. (versión 1.3). Science of Science. [Software]. Bloomington: CNS. Available: https://sci2.cns.iu.edu/user/index.phpVijverberg, C.-P. C., Vijverberg, W. P. M. & Taşpınar, S. (2015). Linking Tukey’s Legacy to Financial Risk Measurement. Computational Statistics and Data Analysis, 100, 595–615. https://doi.org/10.1016/j.csda.2015.08.018Wasserman, S. & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge: CUP. https://doi.org/10.1017/CBO9780511815478Wang, B., Wang, S. & Watadata, J. (2011). Fuzzy-Portfolio-Selection Models With Value-at-Risk. IEEE Transactions on Fuzzy Systems, 19(4), 758–769. https:// doi.org/10.1109/TFUZZ.2011.2144599Wang, C., Chen, Q. & Gerlach, R. (2018). Bayesian realized-GARCH models for financial tail risk forecasting incorporating the two-sided Weibull distribution. Quantitative Finance, 19(6), 1017–1042. https://doi.org/10.1080/14697688.2018.1540880Xu, L. (2014). Model-free inference for tail risk measures. Econometric Theory, 34(3), 1–32. https://doi.org/10.1017/S0266466614000802Yao, H., Li, Z. & Lai, Y. (2013). Mean–CVaR portfolio selection: A nonparametric estimation framework. Computers & Operations Research, 40(4), 1014–1044. https://doi.org/10.1016/J.COR.2012.11.007Zaichao, Du. & Escanciano, J. C. (2016). Backtesting Expected Shortfall: Accounting for Tail Risk. Management Science, 63(4), 940–958. https://doi.org/10.1287/mnsc.2015.2342Zhang, L., Luo, M. & Boncella, R. (2017). Product information diffusion in a social network. Electronic Commerce Research, 20(1), 3–19. https://doi.org/10.1007/s10660-018-9316-9Zhang, Q. & Wang, X. (2009). Hedge Contract Characterization and Risk-Constrained Electricity Procurement. IEEE Transactions on Power Systems, 24(3), 1547–1548. https://doi.org/10.1109/TPWRS.2009.2021233Ziggel, D., Berens, T., Weib, G. & Wied, D. (2014). A new set of improved Valueat-Risk back tests. Journal of Banking & Finance, 48(C), 29–41. https://doi.org/10.1016/J.JBANKFIN.2014.07.005Zuluaga, M., Robledo, S., Osorio, G. A., Yathe, L., González, D. & Taborda, G. (2016). Metabolómica y Pesticidas: Revisión sistemática de literatura usando teoría de grafos para el análisis de referencias. Nova, 14(25), 121–128. https://doi.org/10.22490/24629448.17358257143https://revistascientificas.cuc.edu.co/economicascuc/article/download/3093/3691https://revistascientificas.cuc.edu.co/economicascuc/article/download/3093/4022https://revistascientificas.cuc.edu.co/economicascuc/article/download/3093/4023https://revistascientificas.cuc.edu.co/economicascuc/article/download/3093/4024Núm. 1 , Año 2022OREORE.xmltext/xml2692https://repositorio.cuc.edu.co/bitstreams/e9d28f01-f7b6-48a0-aa5a-0b98e54f7c28/download3a1414d4f5dabf45bbea84991f99c79fMD5111323/11923oai:repositorio.cuc.edu.co:11323/119232024-11-18 11:28:15.823https://creativecommons.org/licenses/by-nc-nd/4.0ECONÓMICAS CUC - 2021metadata.onlyhttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.co

Publicaciones similares