Construcción de portafolios en fondos de inversión considerando momentos estadísticos superiores

Este estudio busca crear portafolios con activos ETF, aplicando un enfoque cuantitativo que incluye momentos estadísticos de orden superior, más allá de la normalidad de la utilidad esperada. El objetivo es optimizar la utilidad y destacar los tres portafolios principales. Al evaluar portafolios con...

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Autores:
Ossa González , Genjis A.
Tipo de recurso:
Article of journal
Fecha de publicación:
2024
Institución:
Universidad Externado de Colombia
Repositorio:
Biblioteca Digital Universidad Externado de Colombia
Idioma:
spa
OAI Identifier:
oai:bdigital.uexternado.edu.co:001/25298
Acceso en línea:
https://bdigital.uexternado.edu.co/handle/001/25298
https://doi.org/10.18601/17941113.n26.02
Palabra clave:
Return;
asymmetry;
kurtosis;
portfolio optimization
retorno;
asimetría;
curtosis;
optimización de portafolios
Rights
openAccess
License
Genjis A. Ossa González - 2024
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network_acronym_str uexternad2
network_name_str Biblioteca Digital Universidad Externado de Colombia
repository_id_str
dc.title.spa.fl_str_mv Construcción de portafolios en fondos de inversión considerando momentos estadísticos superiores
dc.title.translated.eng.fl_str_mv Construction of Portfolios Considering Higher Moments for Investment Funds
title Construcción de portafolios en fondos de inversión considerando momentos estadísticos superiores
spellingShingle Construcción de portafolios en fondos de inversión considerando momentos estadísticos superiores
Return;
asymmetry;
kurtosis;
portfolio optimization
retorno;
asimetría;
curtosis;
optimización de portafolios
title_short Construcción de portafolios en fondos de inversión considerando momentos estadísticos superiores
title_full Construcción de portafolios en fondos de inversión considerando momentos estadísticos superiores
title_fullStr Construcción de portafolios en fondos de inversión considerando momentos estadísticos superiores
title_full_unstemmed Construcción de portafolios en fondos de inversión considerando momentos estadísticos superiores
title_sort Construcción de portafolios en fondos de inversión considerando momentos estadísticos superiores
dc.creator.fl_str_mv Ossa González , Genjis A.
dc.contributor.author.spa.fl_str_mv Ossa González , Genjis A.
dc.subject.eng.fl_str_mv Return;
asymmetry;
kurtosis;
portfolio optimization
topic Return;
asymmetry;
kurtosis;
portfolio optimization
retorno;
asimetría;
curtosis;
optimización de portafolios
dc.subject.spa.fl_str_mv retorno;
asimetría;
curtosis;
optimización de portafolios
description Este estudio busca crear portafolios con activos ETF, aplicando un enfoque cuantitativo que incluye momentos estadísticos de orden superior, más allá de la normalidad de la utilidad esperada. El objetivo es optimizar la utilidad y destacar los tres portafolios principales. Al evaluar portafolios con ETF como LABU, PSQ, FXI, SPY e IWM, se notó una reducción en rendimientos al aplicar momentos superiores. El portafolio 2, bajo la hipótesis de normalidad, sobresa­lió por su alta media de rendimiento y baja volatilidad, a pesar de una curtosis elevada. Sin embargo, la inclusión de momentos superiores indicó un aumento del riesgo, lo que hizo que ningún portafolio fuera óptimo para inversión.
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-12-05T12:44:37Z
2025-04-09T17:21:25Z
dc.date.available.none.fl_str_mv 2024-12-05T12:44:37Z
2025-04-09T17:21:25Z
dc.date.issued.none.fl_str_mv 2024-12-05
dc.type.spa.fl_str_mv Artículo de revista
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dc.relation.citationedition.spa.fl_str_mv Núm. 26 , Año 2024 : Enero-Junio
dc.relation.citationendpage.none.fl_str_mv 28
dc.relation.citationissue.spa.fl_str_mv 26
dc.relation.citationstartpage.none.fl_str_mv 7
dc.relation.ispartofjournal.spa.fl_str_mv ODEON
dc.relation.references.spa.fl_str_mv Arditti, D. (1967). Risk and the required return on equity. The Journal of Finance, 22(1), 19-36. https://doi.org/10.2307/2977297
Aksaraylı, M., y Pala, O. (2018). A polynomial goal programming model for portfolio optimization based on entropy and higher moments. Expert Systems with Applications, 94, 185-192. https://doi.org/10.1016/j.eswa.2017.10.056
BlackRock (2023). iShares Russell 2000 ETF. https://www.blackrock.com/cl/produc-tos/239710/ishares-russell-2000-etf
Bergh, G., y Rensburg, P. (2008). Hedge funds and higher moment portfolio selection. Journal of Derivatives & Hedge Funds, 14, 102-126. https://doi.org/10.1057/ jdhf.2008.14
Brito, R. P., Sebastião, H. y Godinho, P. (2019). Portfolio management with higher moments: The cardinality impact. International Transactions in Operational Research, 26(6), 2531-2560. https://doi.org/10.1111/itor.12404
Charupat, N. y Miu, P. (2013). The pricing efficiency of leveraged exchange-traded funds: evidence from the USmarkets. Journal of Financial Research, 36(2), 253- 278. https://doi.org/10.1111/j.1475-6803.2013.12010.x
Dahlquist, M., Farago, A., y Tédongap, R. (2017). Asymmetries and portfolio choice. The Review of Financial Studies, 30(2), 667-702. https://doi.org/10.1093/rfs/hhw091
Fama, E. F. (1965). The behavior of stock-market prices. The journal of Business, 38(1), 34-105.
Harvey, C. R., Liechty, J. C., Liechty, M. W. y Mueller, P. (2010). Portfolio selection with higher moments. Quantitative Finance, 10, 469-485. http://dx.doi.org/10.1080/14697681003756877
Harvey, C. R. y Siddique, A. (1999). Autoregressive conditional skewness. Journal of fi-nancial and quantitative analysis, 34(4), 465-487. https://doi.org/10.2307/2676230
Gong, X., Yu, C., Min, L. y Ge, Z. (2021). Regret theory-based fuzzy multi-objective portfolio selection model involving deacross-efficiency and higher moments. Applied Soft Computing, 100, 106958. https://doi.org/10.1016/j.asoc.2020.106958
Guiso, L. y Paiella, M. (2001). Risk Aversion, Wealth and Background Risk. Micro-economic Theory Journal. https://doi.org/10.2139/ssrn.262958.
Jean, W. H. (1971). The extension of portfolio analysis to three or more parameters. Journal of financial and Quantitative Analysis, 6(1), 505-515. https://doi. org/10.2307/2330125
Jondeau, E., y Rockinger, M. (2006). Optimal portfolio allocation under higher moments. European Financial Management, 12(1), 29-55. https://doi.org/10.1111/j.1354-7798.2006.00309.x
Konno, H., Hiroshi, S. e Hiroaki, Y. (1993). A mean-absolute deviation-skewness portfolio optimization model. Annals of Operations Research, 45(1), 205-220.
Lai, T. Y. (1991). Portfolio selection with skewness: A multiple-objective approach. Review of Quantitative Finance and Accounting, 1, 293-305. https://doi.org/10.1007/BF02408382
Levy, H., y Arditti, F. D. (1975). Valuation, leverage and the cost of capital in the case of depreciable assets: A reply. The Journal of Finance, 30(1), 221-223. https://doi.org/10.2307/2978446
Levy, H. y Markowitz, H. M. (1979). Approximating expected utility by a function of mean and variance. The American Economic Review, 69(3), 308-317. https://www.jstor.org/stable/1807366
Mandelbrot, B. (1963). New methods in statistical economics. Journal of Political Economy, 71(5), 421-440. https://doi.org/10.1086/258792
Markowitz, H. (1952). Portfolio Selection. Journal of Finance, American Finance Association, 7(1), 77-91. https://doi.org/10.2307/2975974
Molina, M. (2022). Paso a paso. Prueba de la t de Student para muestras independientes. Revista electrónica AnestesiaR, 14(8), 1-5. https://doi.org/10.30445/rear.v14i8.1060
Pierro, M. D. y Mosevich, J. (2011). Effects of skewness and kurtosis on portfolio rankings. Quantitative Finance, 11(10), 1449-1453. https://doi.org/10.1080/1469 7688.2010.495723
Peiro, A. (1999). Skewness in financial returns. Journal of Banking & Finance, 23(6), 847-862. https://doi.org/10.1016/S0378-4266(98)00119-8
Premaratne, G. y Bera, A. K. (2000). Modeling asymmetry and excess kurtosis in stock return data. Illinois Research & Reference Working Paper No. 00-123. http://dx.doi.org/10.2139/ssrn.259009
Vilella, F. (2020). Rebrotes del Covid-19 mantendrán en auge a sectores ya beneficiados. Revista Uruguaya de Economía y Finanzas Personales, Portfolio, 102(8), 29-32.
Saranya, K. y Prasanna, P. K. (2014). Portfolio selection and optimization with higher moments: Evidence from the Indian stock market. Asia-Pacific Financial Markets, 21, 133-149. https://doi.org/10.1007/s10690-014-9180-0
Salinas, S. M., Maldonado, D. A. y Díaz, L. G. (2010). Estimación del riesgo en un portafolio de activos. Apuntes del CENES, 29(50), 117-150.
Sweta, K. (2023). Top-Ranked ETFS to Buy on Small-Cap Comeback. Yahoo Finance.
Steyn, J. P. y Theart, L. (2021). The pricing of skewness: Evidence from the Johannesburg Stock Exchange. Investment Analysts Journal, 50(2), 133-144. https://doi.org/10.1080/10293523.2021.1898744
Thiele, S. (2020). Modeling the conditional distribution of financial returns with asymmetric tails. Journal of Applied Econometrics, 35(1), 46-60. https://doi. org/10.1002/jae.2730tyva(2023). Qué es el etfspy. https://tyba.com.co/blog/spy/
Xu, Z., Li, X. y Chevapatrakul, T. (2019). Return asymmetry and the cross sección of stock returns. Social Science Research Network. http://dx.doi.org/10.2139/ ssrn.2850842
Zhu, F., Luo, X. y Jin, X. (2019). Predicting the volatility of the iShares China Large- Cap ETF: What is the role of the SSE 50 ETF? Pacific-Basin Finance Journal, 57, 101192. https://doi.org/10.1016/j.pacfin.2019.101192
dc.rights.spa.fl_str_mv Genjis A. Ossa González - 2024
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spelling Ossa González , Genjis A.2024-12-05T12:44:37Z2025-04-09T17:21:25Z2024-12-05T12:44:37Z2025-04-09T17:21:25Z2024-12-05Este estudio busca crear portafolios con activos ETF, aplicando un enfoque cuantitativo que incluye momentos estadísticos de orden superior, más allá de la normalidad de la utilidad esperada. El objetivo es optimizar la utilidad y destacar los tres portafolios principales. Al evaluar portafolios con ETF como LABU, PSQ, FXI, SPY e IWM, se notó una reducción en rendimientos al aplicar momentos superiores. El portafolio 2, bajo la hipótesis de normalidad, sobresa­lió por su alta media de rendimiento y baja volatilidad, a pesar de una curtosis elevada. Sin embargo, la inclusión de momentos superiores indicó un aumento del riesgo, lo que hizo que ningún portafolio fuera óptimo para inversión.This study aims to create portfolios with ETF assets, using a quantitative approach that extends beyond expected utility’s normality to include higher-order statistical moments. The goal is to optimize the utility and highlight the top three portfolios. When analyzing portfolios featuring ETFS such as LABU, PSQ, FXI, spy, and IWM, a decrease in returns was observed upon incorporat­ing higher moments. Portfolio 2 stood out under the assumption of normality for its higher average return and lower volatility, despite a significantly higher kurtosis. However, factoring in higher-order moments indicated an increased risk, rendering none of the portfolios optimal for investment.application/pdf10.18601/17941113.n26.022346-21401794-1113https://bdigital.uexternado.edu.co/handle/001/25298https://doi.org/10.18601/17941113.n26.02spaUniversidad Externado de Colombiahttps://revistas.uexternado.edu.co/index.php/odeon/article/download/10067/17163Núm. 26 , Año 2024 : Enero-Junio28267ODEONArditti, D. (1967). Risk and the required return on equity. The Journal of Finance, 22(1), 19-36. https://doi.org/10.2307/2977297Aksaraylı, M., y Pala, O. (2018). A polynomial goal programming model for portfolio optimization based on entropy and higher moments. Expert Systems with Applications, 94, 185-192. https://doi.org/10.1016/j.eswa.2017.10.056BlackRock (2023). iShares Russell 2000 ETF. https://www.blackrock.com/cl/produc-tos/239710/ishares-russell-2000-etfBergh, G., y Rensburg, P. (2008). Hedge funds and higher moment portfolio selection. Journal of Derivatives & Hedge Funds, 14, 102-126. https://doi.org/10.1057/ jdhf.2008.14Brito, R. P., Sebastião, H. y Godinho, P. (2019). Portfolio management with higher moments: The cardinality impact. International Transactions in Operational Research, 26(6), 2531-2560. https://doi.org/10.1111/itor.12404Charupat, N. y Miu, P. (2013). The pricing efficiency of leveraged exchange-traded funds: evidence from the USmarkets. Journal of Financial Research, 36(2), 253- 278. https://doi.org/10.1111/j.1475-6803.2013.12010.xDahlquist, M., Farago, A., y Tédongap, R. (2017). Asymmetries and portfolio choice. The Review of Financial Studies, 30(2), 667-702. https://doi.org/10.1093/rfs/hhw091Fama, E. F. (1965). The behavior of stock-market prices. The journal of Business, 38(1), 34-105.Harvey, C. R., Liechty, J. C., Liechty, M. W. y Mueller, P. (2010). Portfolio selection with higher moments. Quantitative Finance, 10, 469-485. http://dx.doi.org/10.1080/14697681003756877Harvey, C. R. y Siddique, A. (1999). Autoregressive conditional skewness. Journal of fi-nancial and quantitative analysis, 34(4), 465-487. https://doi.org/10.2307/2676230Gong, X., Yu, C., Min, L. y Ge, Z. (2021). Regret theory-based fuzzy multi-objective portfolio selection model involving deacross-efficiency and higher moments. Applied Soft Computing, 100, 106958. https://doi.org/10.1016/j.asoc.2020.106958Guiso, L. y Paiella, M. (2001). Risk Aversion, Wealth and Background Risk. Micro-economic Theory Journal. https://doi.org/10.2139/ssrn.262958.Jean, W. H. (1971). The extension of portfolio analysis to three or more parameters. Journal of financial and Quantitative Analysis, 6(1), 505-515. https://doi. org/10.2307/2330125Jondeau, E., y Rockinger, M. (2006). Optimal portfolio allocation under higher moments. European Financial Management, 12(1), 29-55. https://doi.org/10.1111/j.1354-7798.2006.00309.xKonno, H., Hiroshi, S. e Hiroaki, Y. (1993). A mean-absolute deviation-skewness portfolio optimization model. Annals of Operations Research, 45(1), 205-220.Lai, T. Y. (1991). Portfolio selection with skewness: A multiple-objective approach. Review of Quantitative Finance and Accounting, 1, 293-305. https://doi.org/10.1007/BF02408382Levy, H., y Arditti, F. D. (1975). Valuation, leverage and the cost of capital in the case of depreciable assets: A reply. The Journal of Finance, 30(1), 221-223. https://doi.org/10.2307/2978446Levy, H. y Markowitz, H. M. (1979). Approximating expected utility by a function of mean and variance. The American Economic Review, 69(3), 308-317. https://www.jstor.org/stable/1807366Mandelbrot, B. (1963). New methods in statistical economics. Journal of Political Economy, 71(5), 421-440. https://doi.org/10.1086/258792Markowitz, H. (1952). Portfolio Selection. Journal of Finance, American Finance Association, 7(1), 77-91. https://doi.org/10.2307/2975974Molina, M. (2022). Paso a paso. Prueba de la t de Student para muestras independientes. Revista electrónica AnestesiaR, 14(8), 1-5. https://doi.org/10.30445/rear.v14i8.1060Pierro, M. D. y Mosevich, J. (2011). Effects of skewness and kurtosis on portfolio rankings. Quantitative Finance, 11(10), 1449-1453. https://doi.org/10.1080/1469 7688.2010.495723Peiro, A. (1999). Skewness in financial returns. Journal of Banking & Finance, 23(6), 847-862. https://doi.org/10.1016/S0378-4266(98)00119-8Premaratne, G. y Bera, A. K. (2000). Modeling asymmetry and excess kurtosis in stock return data. Illinois Research & Reference Working Paper No. 00-123. http://dx.doi.org/10.2139/ssrn.259009Vilella, F. (2020). Rebrotes del Covid-19 mantendrán en auge a sectores ya beneficiados. Revista Uruguaya de Economía y Finanzas Personales, Portfolio, 102(8), 29-32.Saranya, K. y Prasanna, P. K. (2014). Portfolio selection and optimization with higher moments: Evidence from the Indian stock market. Asia-Pacific Financial Markets, 21, 133-149. https://doi.org/10.1007/s10690-014-9180-0Salinas, S. M., Maldonado, D. A. y Díaz, L. G. (2010). Estimación del riesgo en un portafolio de activos. Apuntes del CENES, 29(50), 117-150.Sweta, K. (2023). Top-Ranked ETFS to Buy on Small-Cap Comeback. Yahoo Finance.Steyn, J. P. y Theart, L. (2021). The pricing of skewness: Evidence from the Johannesburg Stock Exchange. Investment Analysts Journal, 50(2), 133-144. https://doi.org/10.1080/10293523.2021.1898744Thiele, S. (2020). Modeling the conditional distribution of financial returns with asymmetric tails. Journal of Applied Econometrics, 35(1), 46-60. https://doi. org/10.1002/jae.2730tyva(2023). Qué es el etfspy. https://tyba.com.co/blog/spy/Xu, Z., Li, X. y Chevapatrakul, T. (2019). Return asymmetry and the cross sección of stock returns. Social Science Research Network. http://dx.doi.org/10.2139/ ssrn.2850842Zhu, F., Luo, X. y Jin, X. (2019). Predicting the volatility of the iShares China Large- Cap ETF: What is the role of the SSE 50 ETF? Pacific-Basin Finance Journal, 57, 101192. https://doi.org/10.1016/j.pacfin.2019.101192Genjis A. Ossa González - 2024info: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/10067Return;asymmetry;kurtosis;portfolio optimizationretorno;asimetría;curtosis;optimización de portafoliosConstrucción de portafolios en fondos de inversión considerando momentos estadísticos superioresConstruction of Portfolios Considering Higher Moments for Investment FundsArtículo de revistahttp://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/publishedVersionPublicationOREORE.xmltext/xml2566https://bdigital.uexternado.edu.co/bitstreams/723e616d-c2ad-4aca-b55e-3e74229f88c0/download46d13b109f76e2e01843472bfc5f65c4MD51001/25298oai:bdigital.uexternado.edu.co:001/252982025-04-09 12:21:25.367http://creativecommons.org/licenses/by-nc-sa/4.0Genjis A. Ossa González - 2024https://bdigital.uexternado.edu.coUniversidad Externado de Colombiametabiblioteca@metabiblioteca.org

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