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 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.
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Genjis A. Ossa González - 2024
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Construcción de portafolios en fondos de inversión considerando momentos estadísticos superiores Konno, H., Hiroshi, S. e Hiroaki, Y. (1993). A mean-absolute deviation-skewness portfolio optimization model. Annals of Operations Research, 45(1), 205-220. 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 Markowitz, H. (1952). Portfolio Selection. Journal of Finance, American Finance Association, 7(1), 77-91. https://doi.org/10.2307/2975974 Mandelbrot, B. (1963). New methods in statistical economics. Journal of Political Economy, 71(5), 421-440. https://doi.org/10.1086/258792 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 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 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 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 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 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 Guiso, L. y Paiella, M. (2001). Risk Aversion, Wealth and Background Risk. Micro-economic Theory Journal. https://doi.org/10.2139/ssrn.262958. 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 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 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 Fama, E. F. (1965). The behavior of stock-market prices. The journal of Business, 38(1), 34-105. 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 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 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 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 http://purl.org/coar/resource_type/c_6501 Text http://purl.org/coar/access_right/c_abf2 info:eu-repo/semantics/openAccess http://purl.org/coar/version/c_970fb48d4fbd8a85 info:eu-repo/semantics/publishedVersion http://purl.org/redcol/resource_type/ARTREF info:eu-repo/semantics/article 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. 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 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 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/ 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 Sweta, K. (2023). Top-Ranked ETFS to Buy on Small-Cap Comeback. Yahoo Finance. 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. 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 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 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 BlackRock (2023). iShares Russell 2000 ETF. https://www.blackrock.com/cl/produc-tos/239710/ishares-russell-2000-etf ODEON 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. Ossa González , Genjis A. retorno; asimetría; curtosis; optimización de portafolios 26 Núm. 26 , Año 2024 : Enero-Junio Artículo de revista application/pdf text/html Universidad Externado de Colombia Publication Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0. https://revistas.uexternado.edu.co/index.php/odeon/article/view/10067 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 Arditti, D. (1967). Risk and the required return on equity. The Journal of Finance, 22(1), 19-36. https://doi.org/10.2307/2977297 Español http://creativecommons.org/licenses/by-nc-sa/4.0 Genjis A. Ossa González - 2024 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. Construction of Portfolios Considering Higher Moments for Investment Funds Journal article Return; asymmetry; kurtosis; portfolio optimization 28 1794-1113 https://revistas.uexternado.edu.co/index.php/odeon/article/download/10067/18119 https://revistas.uexternado.edu.co/index.php/odeon/article/download/10067/18118 7 2346-2140 https://doi.org/10.18601/17941113.n26.02 2024-12-05T12:44:37Z 2024-12-05 10.18601/17941113.n26.02 2024-12-05T12:44:37Z |
institution |
UNIVERSIDAD EXTERNADO DE COLOMBIA |
thumbnail |
https://nuevo.metarevistas.org/UNIVERSIDADEXTERNADODECOLOMBIA/logo.png |
country_str |
Colombia |
collection |
Revista ODEON |
title |
Construcción de portafolios en fondos de inversión considerando momentos estadísticos superiores |
spellingShingle |
Construcción de portafolios en fondos de inversión considerando momentos estadísticos superiores Ossa González , Genjis A. retorno; asimetría; curtosis; optimización de portafolios Return; asymmetry; kurtosis; portfolio optimization |
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 |
title_eng |
Construction of Portfolios Considering Higher Moments for Investment Funds |
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.
|
description_eng |
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.
|
author |
Ossa González , Genjis A. |
author_facet |
Ossa González , Genjis A. |
topicspa_str_mv |
retorno; asimetría; curtosis; optimización de portafolios |
topic |
retorno; asimetría; curtosis; optimización de portafolios Return; asymmetry; kurtosis; portfolio optimization |
topic_facet |
retorno; asimetría; curtosis; optimización de portafolios Return; asymmetry; kurtosis; portfolio optimization |
citationissue |
26 |
citationedition |
Núm. 26 , Año 2024 : Enero-Junio |
publisher |
Universidad Externado de Colombia |
ispartofjournal |
ODEON |
source |
https://revistas.uexternado.edu.co/index.php/odeon/article/view/10067 |
language |
Español |
format |
Article |
rights |
http://purl.org/coar/access_right/c_abf2 info:eu-repo/semantics/openAccess Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0. http://creativecommons.org/licenses/by-nc-sa/4.0 Genjis A. Ossa González - 2024 |
references |
Konno, H., Hiroshi, S. e Hiroaki, Y. (1993). A mean-absolute deviation-skewness portfolio optimization model. Annals of Operations Research, 45(1), 205-220. 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 Markowitz, H. (1952). Portfolio Selection. Journal of Finance, American Finance Association, 7(1), 77-91. https://doi.org/10.2307/2975974 Mandelbrot, B. (1963). New methods in statistical economics. Journal of Political Economy, 71(5), 421-440. https://doi.org/10.1086/258792 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 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 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 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 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 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 Guiso, L. y Paiella, M. (2001). Risk Aversion, Wealth and Background Risk. Micro-economic Theory Journal. https://doi.org/10.2139/ssrn.262958. 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 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 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 Fama, E. F. (1965). The behavior of stock-market prices. The journal of Business, 38(1), 34-105. 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 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 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 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 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. 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 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 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/ 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 Sweta, K. (2023). Top-Ranked ETFS to Buy on Small-Cap Comeback. Yahoo Finance. 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. 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 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 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 BlackRock (2023). iShares Russell 2000 ETF. https://www.blackrock.com/cl/produc-tos/239710/ishares-russell-2000-etf 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 Arditti, D. (1967). Risk and the required return on equity. The Journal of Finance, 22(1), 19-36. https://doi.org/10.2307/2977297 |
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