Optimización robusta de portafolios: conjuntos de incertidumbre y contrapartes robustas

Los modelos de optimización robusta (OR) han permitido superar las limitaciones del modelo media-varianza (MV), que comprende el enfoque tradicional para la selección de portafolios óptimos de inversión, al incorporar la incertidumbre de los parámetros del modelo (retornos esperados y covarianzas)....

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Autores:: Zapata Quimbayo, Carlos Andrés

Tipo de recurso:: Article of journal

Fecha de publicación:: 2022

Institución:: Universidad Externado de Colombia

Repositorio:: Biblioteca Digital Universidad Externado de Colombia

Idioma:: spa

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dc.title.spa.fl_str_mv	Optimización robusta de portafolios: conjuntos de incertidumbre y contrapartes robustas
dc.title.translated.eng.fl_str_mv	Robust Portfolio Optimization: Uncertainty Sets and Robust Counterparts
title	Optimización robusta de portafolios: conjuntos de incertidumbre y contrapartes robustas
spellingShingle	Optimización robusta de portafolios: conjuntos de incertidumbre y contrapartes robustas optimal portfolio; robust optimization; uncertainty sets portafolio óptimo; optimización robusta; conjuntos de incertidumbre
title_short	Optimización robusta de portafolios: conjuntos de incertidumbre y contrapartes robustas
title_full	Optimización robusta de portafolios: conjuntos de incertidumbre y contrapartes robustas
title_fullStr	Optimización robusta de portafolios: conjuntos de incertidumbre y contrapartes robustas
title_full_unstemmed	Optimización robusta de portafolios: conjuntos de incertidumbre y contrapartes robustas
title_sort	Optimización robusta de portafolios: conjuntos de incertidumbre y contrapartes robustas
dc.creator.fl_str_mv	Zapata Quimbayo, Carlos Andrés
dc.contributor.author.none.fl_str_mv	Zapata Quimbayo, Carlos Andrés
dc.subject.eng.fl_str_mv	optimal portfolio; robust optimization; uncertainty sets
topic	optimal portfolio; robust optimization; uncertainty sets portafolio óptimo; optimización robusta; conjuntos de incertidumbre
dc.subject.spa.fl_str_mv	portafolio óptimo; optimización robusta; conjuntos de incertidumbre
description	Los modelos de optimización robusta (OR) han permitido superar las limitaciones del modelo media-varianza (MV), que comprende el enfoque tradicional para la selección de portafolios óptimos de inversión, al incorporar la incertidumbre de los parámetros del modelo (retornos esperados y covarianzas). En este trabajo se presentan los desarrollos de la OR en la teoría de portafolio mediante el enfoque del peor de los casos, a partir del cual se incorporan las formulaciones robustas para el modelo MV, teniendo en cuenta los trabajos de Markowitz y Sharpe. A partir de estas formulaciones, se lleva a cabo una sencilla aplicación en la que se resaltan las ventajas y bondades de las contrapartes robustas frente al modelo MV original. Al final, se presenta una breve discusión de formulaciones adicionales en materia de conjuntos de incertidumbre y otras medidas de desempeño.
publishDate	2022
dc.date.accessioned.none.fl_str_mv	2022-06-07 09:28:03 2022-09-08T13:42:45Z
dc.date.available.none.fl_str_mv	2022-06-07 09:28:03 2022-09-08T13:42:45Z
dc.date.issued.none.fl_str_mv	2022-06-07
dc.type.spa.fl_str_mv	Artículo de revista
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dc.identifier.doi.none.fl_str_mv	10.18601/17941113.n20.04
dc.identifier.eissn.none.fl_str_mv	2346-2140
dc.identifier.issn.none.fl_str_mv	1794-1113
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dc.relation.citationedition.spa.fl_str_mv	Núm. 20 , Año 2021 : Enero-Junio
dc.relation.citationendpage.none.fl_str_mv	121
dc.relation.citationissue.spa.fl_str_mv	20
dc.relation.citationstartpage.none.fl_str_mv	93
dc.relation.ispartofjournal.spa.fl_str_mv	Odeon
dc.relation.references.spa.fl_str_mv	Bandi, C. y Bertsimas, D. (2012). Tractable stochastic analysis in high dimensions via robust optimization. Mathematical programming, 134(1), 23-70. Ben-Tal, A. y Nemirovski, A. (1998). Robust convex optimization. Mathematics of Operations Research, 23(4), 769-805. Bertsimas, D., Darnell, C. y Soucy, R. (1999). Portfolio construction through mixedinteger programming at Grantham, Mayo, Van Otterloo and Company. Interfaces, 29(1), 49-66. Bertsimas, D. y Brown, D. (2009). Constructing uncertainty sets for robust linear optimization. Operations Research, 57(6), 1483-1495. Bertsimas, D., Brown, D. y Caramanis, C. (2011). Theory and applications of robust optimization. SIAM Review, 53(3), 464-501. Best, M. y Grauer, R. (1991). On the sensitivity of mean variance efficient portfolios to changes in asset Means. The Review of Financial Studies, 4(2), 314-342. Black, F. y Litterman, R. (1991). Global Asset Allocation with Equities, Bonds, and Currencies. Goldman, Sachs & Co Fixed Income Research, 1-44. Black, F. y Litterman, R. (1992). Global portfolio optimization. Financial Analysts Journal, 48(5), 28-43. Blog, B., Hoek, G., Kan, A. y Timmer, G. (1983). The optimal selection of small portfolios. Management Science, 29(7), 792-798. Chopra, V. y Ziemba, W. (1993). The effects of errors in means, variances, and covariances on optimal portfolio choice. Journal of Portfolio Management, 19(2), 6-11. Choueifaty, Y. y Coignard, Y. (2008). Toward maximum diversification. Journal of Portfolio Management, 35(1), 40-51. El Ghaoui, L., Oustry, F. y Lebret, H. (1998). Robust solutions to uncertain semidefinite programs. SIAM Journal on Optimization, 9(1), 33-52. El Ghaoui, L., Oks, M. y Oustry, F. (2003). Worst-case value-at-risk and robust portfolio optimization: A conic programming approach. Operations Research, 51(4), 543-556. Elton, E., Gruber, M. y Padberg, M. (1976). Simple criteria for optimal portfolio selection. The Journal of Finance, 31(5), 1341-1357. Fabozzi, F., Huang, D. y Zhou, G. (2010). Robust portfolios: Contributions from operations research and finance. Annals of Operations Research, 176(1), 191-220. Fabozzi, F., Kolm, P., Pachamanova, D. A. y Focardi, S. (2007). Robust portfolio optimization and management. John Wiley & Sons. Francis, J. y Kim, D. (2013). Modern Portfolio Theory: Foundations, Analysis, and New Developments. John Wiley & Sons. Garlappi, L., Uppal, R. y Wang, T. (2007). Portfolio selection with parameter and model uncertainty: A multi-prior approach. Review of Financial Studies, 20(1), 41-81. Georgantas, A., Doumpos, M. y Zopounidis, C. (2021). Robust optimization approaches for portfolio selection: a comparative analysis. Annals of Operations Research, 1-17. Goldfarb, D. e Iyengar, G. (2003). Robust portfolio selection problems. Mathematics of Operations Research, 28(1), 1-38. He, G. y Litterman, R. (1999). The intuition behind Black-Litterman model portfolios. Technical report, Goldman Sachs–Investment Management Research, 1-18. Huang, D., Fabozzi, F. y Fukushima, M. (2007). Robust portfolio selection with uncertain exit time using worst-case VaR strategy. Operations Research Letters, 35, 627-635. Idzorek, T. (2007). A step-by-step guide to the Black-Litterman model: Incorporating user-specified confidence levels (pp. 17-38). En S. Satchell (Ed.). Forecasting expected returns in the financial markets. Academic Press. James, W. y Stein, C. (1961). Estimation with quadratic loss. Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, 1, 361-380. Kapsos, M., Christofides, N. y Rustem, B. (2014). Worst-case robust Omega ratio. European Journal of Operational Research, 234(2), 499-507. Kara, G., Ozmen, A. y Weber, G. (2019). Stability advances in robust portfolio optimization under parallelepiped uncertainty. Central European Journal of Operations Research, 27(1), 241-261. Keating, C. y Shadwick, W. (2002). A universal performance measure. Journal of Performance Measurement, 6(3), 59-84. Kim, J., Kim, W. y Fabozzi, F. (2013). Recent developments in robust portfolios with a worst-case approach. Journal of Optimization Theory and Applications, 161(1), 103-121. Kim, J., Kim, W., Kwon, D. y Fabozzi, F. (2018). Robust equity portfolio performance. Annals of Operations Research, 266(1-2), 293-312. Kolm, P., Tütüncü, R. y Fabozzi, F. (2014). 60 years of portfolio optimization: Practical challenges and current trends. European Journal of Operational Research, 234(2), 356-371. Ledoit, O. y Wolf, M. (2003). Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. Journal of Empirical Finance, 10(5), 603-621. Lintner, J. (1965). Security prices, risk, and maximal gains from diversification. The Journal of Finance, 20(4), 587-615. Lobo, M. y Boyd, S. (2000). Portfolio optimization with linear and fixed transaction costs and bounds on risk. Annals of Operations Research, 152(1), 341-365. Lu, Z. (2011b). Robust portfolio selection based on a joint ellipsoidal uncertainty set. Optimization Methods & Software, 26, 89-104. Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91. Markowitz, H. (1959). Portfolio selection: efficient diversification of investments. Wiley. Meucci, A. (2008). Fully flexible views: Theory and practice. Risk, 21(10), 97-102. Meucci, A. (2009). Enhancing the Black-Litterman and related approaches: Views and stress-test on risk factors. Journal of Asset Management, 10, 89-96. Meucci, A. (2011). Robust Bayesian Allocation. https://ssrn.com/abstract=681553, 1-18. Michaud, R. (1989). The Markowitz optimization enigma: Is optimization optimal? Financial Analysts Journal, 45(1), 31-42. Mossin, J. (1966). Equilibrium in a capital asset market. Econometrica, 34(4), 768-783. Pachamanova, D. y Fabozzi, F. (2012). Equity Portfolio Selection Models in Practice. Encyclopedia of Financial Models, 1, 61-87. Rockefellar, R. y Uryasev, S. (2000). Optimization of conditional value-at-risk. Journal of Risk, 3(1), 21-41. Romero, C. (2010). La Teoría Moderna de Portafolio: un ensayo sobre sus formulaciones originales y sus repercusiones contemporáneas. ODEON, 5, 103-118. Schöttle, K., Werner, R. y Zagst, R. (2010). Comparison and robustification of Bayes and Black-Litterman models. Mathematical Methods of Operations Research, 71(3), 453-475. Sharma, A., Utz, S. y Mehra, A. (2017). Omega-CVaR portfolio optimization and its worst-case analysis. OR Spectrum, 39(2), 505-539. Sharpe, W. (1964). Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk. The Journal of Finance, 19(1), 425-42. Sortino, F. y Price, L. (1994). Performance measurement in a downside risk framework. Journal of Investing, 3(3), 59-64. Treynor, J. (1965) How to rate management of investment funds. Harvard Business Review, 43, 63-75. Tütüncü, R. y Koenig, M. (2004). Robust asset allocation. Annals of Operations Research, 132(1-4), 157-187. Xidonas, P., Steuer, R. y Hassapis, C. (2020). Robust portfolio optimization: A categorized bibliographic review. Annals of Operations Research, 292(1), 533-552. Yin, C., Perchet, R. y Soupé, F. (2021). A practical guide to robust portfolio optimization. Quantitative Finance, 21(6), 911-928. Zhu, S. y Fukushima, M. (2009). Worst-case conditional value-at-risk with application to robust portfolio management. Operations Research, 57(5), 1155-1168. Zymler, S., Kuhn, D. y Rustem, B. (2013). Worst-case value at risk of nonlinear portfolios. Management Science, 59(1), 172-188.
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spelling	Zapata Quimbayo, Carlos Andrésvirtual::422-12022-06-07 09:28:032022-09-08T13:42:45Z2022-06-07 09:28:032022-09-08T13:42:45Z2022-06-07Los modelos de optimización robusta (OR) han permitido superar las limitaciones del modelo media-varianza (MV), que comprende el enfoque tradicional para la selección de portafolios óptimos de inversión, al incorporar la incertidumbre de los parámetros del modelo (retornos esperados y covarianzas). En este trabajo se presentan los desarrollos de la OR en la teoría de portafolio mediante el enfoque del peor de los casos, a partir del cual se incorporan las formulaciones robustas para el modelo MV, teniendo en cuenta los trabajos de Markowitz y Sharpe. A partir de estas formulaciones, se lleva a cabo una sencilla aplicación en la que se resaltan las ventajas y bondades de las contrapartes robustas frente al modelo MV original. Al final, se presenta una breve discusión de formulaciones adicionales en materia de conjuntos de incertidumbre y otras medidas de desempeño.Robust optimization (or) models have made it possible to overcome the limitations of the mean-variance (mv) model, which involves the traditional approach for the optimal portfolio selection, by incorporating the uncertainty of the model parameters (expected returns and covariances). In this paper, the or advances in portfolio theory are presented using the worst-case approach, from which the robust formulations for the mv model are incorporated, considering the Markowitz and Sharpe works. From these formulations, a straightforward application is implemented where the advantages and benefits of the robust counterparts are highlighted compared to the original MV model. At the end, a brief discussion of additional formulations regarding uncertainty sets and other performance measures is presented.application/pdf10.18601/17941113.n20.042346-21401794-1113https://bdigital.uexternado.edu.co/handle/001/7928https://doi.org/10.18601/17941113.n20.04spaFacultad de Finanzas, Gobierno y Relaciones Internacionaleshttps://revistas.uexternado.edu.co/index.php/odeon/article/download/7837/11404Núm. 20 , Año 2021 : Enero-Junio1212093OdeonBandi, C. y Bertsimas, D. (2012). Tractable stochastic analysis in high dimensions via robust optimization. Mathematical programming, 134(1), 23-70.Ben-Tal, A. y Nemirovski, A. (1998). Robust convex optimization. Mathematics of Operations Research, 23(4), 769-805.Bertsimas, D., Darnell, C. y Soucy, R. (1999). Portfolio construction through mixedinteger programming at Grantham, Mayo, Van Otterloo and Company. Interfaces, 29(1), 49-66.Bertsimas, D. y Brown, D. (2009). Constructing uncertainty sets for robust linear optimization. Operations Research, 57(6), 1483-1495.Bertsimas, D., Brown, D. y Caramanis, C. (2011). Theory and applications of robust optimization. SIAM Review, 53(3), 464-501.Best, M. y Grauer, R. (1991). On the sensitivity of mean variance efficient portfolios to changes in asset Means. The Review of Financial Studies, 4(2), 314-342.Black, F. y Litterman, R. (1991). Global Asset Allocation with Equities, Bonds, and Currencies. Goldman, Sachs & Co Fixed Income Research, 1-44.Black, F. y Litterman, R. (1992). Global portfolio optimization. Financial Analysts Journal, 48(5), 28-43.Blog, B., Hoek, G., Kan, A. y Timmer, G. (1983). The optimal selection of small portfolios. Management Science, 29(7), 792-798.Chopra, V. y Ziemba, W. (1993). The effects of errors in means, variances, and covariances on optimal portfolio choice. Journal of Portfolio Management, 19(2), 6-11.Choueifaty, Y. y Coignard, Y. (2008). Toward maximum diversification. Journal of Portfolio Management, 35(1), 40-51.El Ghaoui, L., Oustry, F. y Lebret, H. (1998). Robust solutions to uncertain semidefinite programs. SIAM Journal on Optimization, 9(1), 33-52.El Ghaoui, L., Oks, M. y Oustry, F. (2003). Worst-case value-at-risk and robust portfolio optimization: A conic programming approach. Operations Research, 51(4), 543-556.Elton, E., Gruber, M. y Padberg, M. (1976). Simple criteria for optimal portfolio selection. The Journal of Finance, 31(5), 1341-1357.Fabozzi, F., Huang, D. y Zhou, G. (2010). Robust portfolios: Contributions from operations research and finance. Annals of Operations Research, 176(1), 191-220.Fabozzi, F., Kolm, P., Pachamanova, D. A. y Focardi, S. (2007). Robust portfolio optimization and management. John Wiley & Sons.Francis, J. y Kim, D. (2013). Modern Portfolio Theory: Foundations, Analysis, and New Developments. John Wiley & Sons.Garlappi, L., Uppal, R. y Wang, T. (2007). Portfolio selection with parameter and model uncertainty: A multi-prior approach. Review of Financial Studies, 20(1), 41-81.Georgantas, A., Doumpos, M. y Zopounidis, C. (2021). Robust optimization approaches for portfolio selection: a comparative analysis. Annals of Operations Research, 1-17.Goldfarb, D. e Iyengar, G. (2003). Robust portfolio selection problems. Mathematics of Operations Research, 28(1), 1-38.He, G. y Litterman, R. (1999). The intuition behind Black-Litterman model portfolios. Technical report, Goldman Sachs–Investment Management Research, 1-18.Huang, D., Fabozzi, F. y Fukushima, M. (2007). Robust portfolio selection with uncertain exit time using worst-case VaR strategy. Operations Research Letters, 35, 627-635.Idzorek, T. (2007). A step-by-step guide to the Black-Litterman model: Incorporating user-specified confidence levels (pp. 17-38). En S. Satchell (Ed.). Forecasting expected returns in the financial markets. Academic Press.James, W. y Stein, C. (1961). Estimation with quadratic loss. Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, 1, 361-380.Kapsos, M., Christofides, N. y Rustem, B. (2014). Worst-case robust Omega ratio. European Journal of Operational Research, 234(2), 499-507.Kara, G., Ozmen, A. y Weber, G. (2019). Stability advances in robust portfolio optimization under parallelepiped uncertainty. Central European Journal of Operations Research, 27(1), 241-261.Keating, C. y Shadwick, W. (2002). A universal performance measure. Journal of Performance Measurement, 6(3), 59-84.Kim, J., Kim, W. y Fabozzi, F. (2013). Recent developments in robust portfolios with a worst-case approach. Journal of Optimization Theory and Applications, 161(1), 103-121.Kim, J., Kim, W., Kwon, D. y Fabozzi, F. (2018). Robust equity portfolio performance. Annals of Operations Research, 266(1-2), 293-312.Kolm, P., Tütüncü, R. y Fabozzi, F. (2014). 60 years of portfolio optimization: Practical challenges and current trends. European Journal of Operational Research, 234(2), 356-371.Ledoit, O. y Wolf, M. (2003). Improved estimation of the covariance matrix of stock returns with an application to portfolio selection. Journal of Empirical Finance, 10(5), 603-621.Lintner, J. (1965). Security prices, risk, and maximal gains from diversification. The Journal of Finance, 20(4), 587-615.Lobo, M. y Boyd, S. (2000). Portfolio optimization with linear and fixed transaction costs and bounds on risk. Annals of Operations Research, 152(1), 341-365.Lu, Z. (2011b). Robust portfolio selection based on a joint ellipsoidal uncertainty set. Optimization Methods & Software, 26, 89-104.Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.Markowitz, H. (1959). Portfolio selection: efficient diversification of investments. Wiley.Meucci, A. (2008). Fully flexible views: Theory and practice. Risk, 21(10), 97-102.Meucci, A. (2009). Enhancing the Black-Litterman and related approaches: Views and stress-test on risk factors. Journal of Asset Management, 10, 89-96.Meucci, A. (2011). Robust Bayesian Allocation. https://ssrn.com/abstract=681553, 1-18.Michaud, R. (1989). The Markowitz optimization enigma: Is optimization optimal? Financial Analysts Journal, 45(1), 31-42.Mossin, J. (1966). Equilibrium in a capital asset market. Econometrica, 34(4), 768-783.Pachamanova, D. y Fabozzi, F. (2012). Equity Portfolio Selection Models in Practice. Encyclopedia of Financial Models, 1, 61-87.Rockefellar, R. y Uryasev, S. (2000). Optimization of conditional value-at-risk. Journal of Risk, 3(1), 21-41.Romero, C. (2010). La Teoría Moderna de Portafolio: un ensayo sobre sus formulaciones originales y sus repercusiones contemporáneas. ODEON, 5, 103-118.Schöttle, K., Werner, R. y Zagst, R. (2010). Comparison and robustification of Bayes and Black-Litterman models. Mathematical Methods of Operations Research, 71(3), 453-475.Sharma, A., Utz, S. y Mehra, A. (2017). Omega-CVaR portfolio optimization and its worst-case analysis. OR Spectrum, 39(2), 505-539.Sharpe, W. (1964). Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk. The Journal of Finance, 19(1), 425-42.Sortino, F. y Price, L. (1994). Performance measurement in a downside risk framework. Journal of Investing, 3(3), 59-64.Treynor, J. (1965) How to rate management of investment funds. Harvard Business Review, 43, 63-75.Tütüncü, R. y Koenig, M. (2004). Robust asset allocation. Annals of Operations Research, 132(1-4), 157-187.Xidonas, P., Steuer, R. y Hassapis, C. (2020). Robust portfolio optimization: A categorized bibliographic review. Annals of Operations Research, 292(1), 533-552.Yin, C., Perchet, R. y Soupé, F. (2021). A practical guide to robust portfolio optimization. Quantitative Finance, 21(6), 911-928.Zhu, S. y Fukushima, M. (2009). Worst-case conditional value-at-risk with application to robust portfolio management. Operations Research, 57(5), 1155-1168.Zymler, S., Kuhn, D. y Rustem, B. (2013). Worst-case value at risk of nonlinear portfolios. Management Science, 59(1), 172-188.Carlos Andrés Zapata Quimbayo - 2022info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.http://creativecommons.org/licenses/by-nc-sa/4.0https://revistas.uexternado.edu.co/index.php/odeon/article/view/7837optimal portfolio;robust optimization;uncertainty setsportafolio óptimo;optimización robusta;conjuntos de incertidumbreOptimización robusta de portafolios: conjuntos de incertidumbre y contrapartes robustasRobust Portfolio Optimization: Uncertainty Sets and Robust CounterpartsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Textinfo:eu-repo/semantics/articleJournal articlehttp://purl.org/redcol/resource_type/ARTREFinfo:eu-repo/semantics/publishedVersionPublicationZapata Quimbayovirtual::422-1Carlos Andrésvirtual::422-1https://scholar.google.com/citations?user=HRLzkWMAAAAJ&hl=esvirtual::422-10000-0003-3337-0182virtual::422-145febcec-2de6-48ab-8e77-0efc1a2af467virtual::422-145febcec-2de6-48ab-8e77-0efc1a2af467virtual::422-1OREORE.xmltext/xml2578https://bdigital.uexternado.edu.co/bitstreams/9038d021-1c7b-41f5-ac2b-e144ab388e89/download649ab6e04d5628c42aaa93a4eba069d3MD51001/7928oai:bdigital.uexternado.edu.co:001/79282022-10-10 10:07:09.682http://creativecommons.org/licenses/by-nc-sa/4.0Carlos Andrés Zapata Quimbayo - 2022https://bdigital.uexternado.edu.coUniversidad Externado de Colombiametabiblioteca@metabiblioteca.org

Optimización robusta de portafolios: conjuntos de incertidumbre y contrapartes robustas

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