Construcción de portafolios en fondos de inversión considerando momentos estadísticos superiores

Construction of Portfolios Considering Higher Moments for Investment Funds

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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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Arditti, D. (1967). Risk and the required return on equity. The Journal of Finance, 22(1), 19-36. https://doi.org/10.2307/2977297 DOI: https://doi.org/10.1111/j.1540-6261.1967.tb01651.x

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 DOI: 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 DOI: 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 DOI: 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 DOI: 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 DOI: 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. DOI: https://doi.org/10.1086/294743

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 DOI: https://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 DOI: 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 DOI: 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. DOI: 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 DOI: 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 DOI: 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. DOI: https://doi.org/10.1007/BF02282050

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 DOI: 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 DOI: https://doi.org/10.1111/j.1540-6261.1975.tb03175.x

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 DOI: 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 DOI: https://doi.org/10.1111/j.1540-6261.1952.tb01525.x

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 DOI: 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 DOI: https://doi.org/10.1080/14697688.2010.495723

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 DOI: 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 DOI: https://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 DOI: 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 DOI: 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/ DOI: https://doi.org/10.1002/jae.2730

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 DOI: https://doi.org/10.1016/j.pacfin.2019.101192

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