Estimating expected returns with forecast combinations

Ilustraciones

Autores:: Richter, Robert

Tipo de recurso:

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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repository_id_str
dc.title.eng.fl_str_mv	Estimating expected returns with forecast combinations
dc.title.translated.spa.fl_str_mv	Estimación de los rendimientos esperados con combinaciones de previsiones
title	Estimating expected returns with forecast combinations
spellingShingle	Estimating expected returns with forecast combinations 330 - Economía Financial Forecasting and Simulation Predicción y simulación financiera Financial Forecasting and Simulation Predicción y simulación financiera C53 Forecasting Models; Simulation Methods Economic forecasting Pronóstico de la economía Forecasting techniques Técnicas de predicción Shrinkage Decision tress Expert aggregation Mean-variance Generalized random forest Automatic arima Portfolio optimisation Exponential smoothing Media-varianza Árboles de decision Arima automatizado Agregación de expertos Optimización de portafolios
title_short	Estimating expected returns with forecast combinations
title_full	Estimating expected returns with forecast combinations
title_fullStr	Estimating expected returns with forecast combinations
title_full_unstemmed	Estimating expected returns with forecast combinations
title_sort	Estimating expected returns with forecast combinations
dc.creator.fl_str_mv	Richter, Robert
dc.contributor.advisor.none.fl_str_mv	Gómez Portilla, Karoll
dc.contributor.author.none.fl_str_mv	Richter, Robert
dc.contributor.researchgroup.spa.fl_str_mv	Grupo Interdisciplinario en Teoría e Investigación Aplicada en Ciencias Económicas
dc.subject.ddc.spa.fl_str_mv	330 - Economía
topic	330 - Economía Financial Forecasting and Simulation Predicción y simulación financiera Financial Forecasting and Simulation Predicción y simulación financiera C53 Forecasting Models; Simulation Methods Economic forecasting Pronóstico de la economía Forecasting techniques Técnicas de predicción Shrinkage Decision tress Expert aggregation Mean-variance Generalized random forest Automatic arima Portfolio optimisation Exponential smoothing Media-varianza Árboles de decision Arima automatizado Agregación de expertos Optimización de portafolios
dc.subject.other.eng.fl_str_mv	Financial Forecasting and Simulation
dc.subject.other.spa.fl_str_mv	Predicción y simulación financiera
dc.subject.ecm.eng.fl_str_mv	Financial Forecasting and Simulation
dc.subject.ecm.spa.fl_str_mv	Predicción y simulación financiera
dc.subject.jel.none.fl_str_mv	C53 Forecasting Models; Simulation Methods
dc.subject.lemb.none.fl_str_mv	Economic forecasting Pronóstico de la economía Forecasting techniques Técnicas de predicción
dc.subject.proposal.eng.fl_str_mv	Shrinkage Decision tress Expert aggregation Mean-variance Generalized random forest Automatic arima Portfolio optimisation Exponential smoothing
dc.subject.proposal.spa.fl_str_mv	Media-varianza Árboles de decision Arima automatizado Agregación de expertos Optimización de portafolios
description	Ilustraciones
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-09-03T22:41:41Z
dc.date.available.none.fl_str_mv	2021-09-03T22:41:41Z
dc.date.issued.none.fl_str_mv	2021-09-03
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/80095
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/80095 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.references.none.fl_str_mv	Amit, Y. & Geman, D. (1997). Shape quantization and recognition with randomized trees. 0899-7667, 9 (7), 1545–1588. https://doi.org/10.1162/neco.1997.9.7.1545 Anderson, B. D. O. (2012). Optimal filtering. Dover Publications. Aoki, M. & Havenner, A. (1991). State space modeling of multiple time series. Econometric Reviews, 10 (1), 1–59. https://doi.org/10.1080/07474939108800194 Arlot, S. & Genuer, R. (2014). Analysis of purely random forests bias. https://arxiv.org/pdf/1407.3939 Athey, S., Tibshirani, J. & Wager, S. (2019). Generalized random forests. 0090-5364, 47 (2), 1148–1178. https://doi.org/10.1214/18-AOS1709 Ban, G.-Y., El Karoui, N. & Lim, A. E. B. (2018). Machine learning and portfolio optimiz ation, (64), 1136–1154. Biau, G. (2012). Analysis of a random forests model, (13), 1063–1095. Biau, G., Devroye, L. & Lugosi, G. (2008). Consistency of random forests and other averaging classifiers, (9), 2015–2033. Blum, A. & Mansour, Y. (2007). From external to internal regret. Journal of Machine Learning Research, 8 (47), 1307–1324. Box, G. E. P. & Jenkins, G. M. (1970). Time series analysis: Forecasting and control. Holden Day. Breiman, L. (1984). Classification and regression trees [Breiman, Leo, (author.)]. [Routledge]. Breiman, L. (1996). Bagging predictors [PII: BF00058655]. 08856125, 24 (2), 123–140. https://doi.org/10.1007/BF00058655 Breiman, L. (2001). Random forests [PII: 354300]. 08856125, 45 (1), 5–32. https://doi.org/10.1023/A:1010933404324 Brockwell, P. J. & Davis, R. A. (2006). Time series: Theory and methods (2nd ed., correc ted.). New York, Springer. Buhlmann, P. & Yu, B. (2002). Analyzing bagging [PII: aos30n4r01]. 0090-5364, 30 (4), 927–961. https://doi.org/10.1214/aos/1031689014 Cesa-Bianchi, N. & Lugosi, G. (2006). Prediction, learning, and games. Cambridge University Press. Cesa-Bianchi, N. & Lugosi, G. (2003). Potential-based algorithms in on-line prediction and game theory [PII: 5120299]. 08856125, 51 (3), 239–261. https://doi.org/10.1023/A:1022901500417 Amit, Y. & Geman, D. (1997). Shape quantization and recognition with randomized trees. 0899-7667, 9 (7), 1545–1588. https://doi.org/10.1162/neco.1997.9.7.1545 Anderson, B. D. O. (2012). Optimal filtering. Dover Publications. Aoki, M. & Havenner, A. (1991). State space modeling of multiple time series. Econometric Reviews, 10 (1), 1–59. https://doi.org/10.1080/07474939108800194 Arlot, S. & Genuer, R. (2014). Analysis of purely random forests bias. https://arxiv.org/pdf/1407.3939 Athey, S., Tibshirani, J. & Wager, S. (2019). Generalized random forests. 0090-5364, 47 (2), 1148–1178. https://doi.org/10.1214/18-AOS1709 Ban, G.-Y., El Karoui, N. & Lim, A. E. B. (2018). Machine learning and portfolio optimiz ation, (64), 1136–1154. Biau, G. (2012). Analysis of a random forests model, (13), 1063–1095. Biau, G., Devroye, L. & Lugosi, G. (2008). Consistency of random forests and other averaging classifiers, (9), 2015–2033. Blum, A. & Mansour, Y. (2007). From external to internal regret. Journal of Machine Learning Research, 8 (47), 1307–1324. Box, G. E. P. & Jenkins, G. M. (1970). Time series analysis: Forecasting and control. Holden Day. Breiman, L. (1984). Classification and regression trees [Breiman, Leo, (author.)]. [Routledge]. Breiman, L. (1996). Bagging predictors [PII: BF00058655]. 08856125, 24 (2), 123–140. https://doi.org/10.1007/BF00058655 Breiman, L. (2001). Random forests [PII: 354300]. 08856125, 45 (1), 5–32. https://doi.org/10.1023/A:1010933404324 Brockwell, P. J. & Davis, R. A. (2006). Time series: Theory and methods (2nd ed., correc ted.). New York, Springer. Buhlmann, P. & Yu, B. (2002). Analyzing bagging [PII: aos30n4r01]. 0090-5364, 30 (4), 927–961. https://doi.org/10.1214/aos/1031689014 Cesa-Bianchi, N. & Lugosi, G. (2006). Prediction, learning, and games. Cambridge University Press. Cesa-Bianchi, N. & Lugosi, G. (2003). Potential-based algorithms in on-line prediction and game theory [PII: 5120299]. 08856125, 51 (3), 239–261. https://doi.org/10.1023/A:1022901500413 Hyndman, R. J., Koehler, A. B., Ord, J. K. & Snyder, R. D. (2008). Forecasting with exponential smoothing: The state space approach. Berlin, Heidelberg, Springer. https://doi.org/10.1007/978-3-540-71918-2 Jagannathan, R. & Ma, T. (2003). Risk reduction in large portfolios: Why imposing the wrong constraints helps, 58 (4), 1651–1683. https://doi.org/10.1111/1540-6261.00580 Koenker, R. (2005). Quantile regression [Koenker, Roger (VerfasserIn)]. Cambridge, Cam bridge University Press. https://doi.org/10.1017/CBO9780511754098 Koenker, R. & Bassett, G. (1978). Regression quantiles [Econometrica, 46(1), 33]. Econo metrica, 46 (1), 33. https://doi.org/10.2307/1913643 Kwiatkowski, D., Phillips, P. C., Schmidt, P. & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root [PII: 030440769290104Y]. Journal of Econometrics, 54 (1-3), 159–178. https://doi.org/10.1016/0304-4076(92)90104-y Landau, S. & Chis Ster, I. (2010). Cluster analysis: Overview, 72–83. https://doi.org/10.1016/B978-0-08-044894-7.01315-4 Ledoit, O. & Wolf, M. (2004). Honey, i shrunk the sample covariance matrix, (4), 110–119. https://doi.org/10.3905/jpm.2004.110 Lin, Y. & Jeon, Y. (2006). Random forests and adaptive nearest neighbors. 0162-1459, 101 (474), 578–590. https://doi.org/10.1198/016214505000001230 Lintner, J. (1965). The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets. 00346535, 47 (1), 13. https://doi.org/10.2307/1924119 Littlestone, N. & Warmuth, M. K. (1994). The weighted majority algorithm [PII: S0890540184710091]. Information and Computation, 108 (2), 212–261. https://doi.org/10.1006/inco.1994.1009 Lopez de Prado, M. (2016). Building diversified portfolios that outperform out of sample. The Journal of Portfolio Management, 42 (4), 59–69. https://doi.org/10.3905/jpm.2016.42.4.059 Makridakis, S. & Hibon, M. (2000). The m3-competition: Results, conclusions and implica tions [PII: S0169207000000571]. International Journal of Forecasting, 16 (4), 451–476. https://doi.org/10.1016/S0169-2070(00)00057-1 Markowitz, H. M. (1952). Portfolio selection, (Vol. 7, No. 1), 77–91. McAndrew, T., Wattanachit, N., Gibson, G. C. & Reich, N. G. (2019). Aggregating predic tions from experts: A scoping review of statistical methods, experiments, and applica tions [https://github.com/tomcm39/AggregatingExpertElicitedDataForPrediction v0.2: updated funding info]. https://arxiv.org/pdf/1912.11409 Meinshausen, N. (2006). Quantile regression forests. Journal of Machine Learning Research, 7 (Jun), 983–999. Mentch, L. & Hooker, G. (2016). Quantifying uncertainty in random forests via confidence intervals and hypothesis tests, (17), 1–41. Merton, R. C. (1980). On estimating the expected return on the market: An exploratory investigation, (8), 323–361 Mossin, J. (1966). Equilibrium in a capital asset market, (34), 768–783. Newey, W. K. (1994). The asymptotic variance of semiparametric estimators, 62 (6), 1349. https://doi.org/10.2307/2951752 Schmidhuber, J. (2014). Deep learning in neural networks: An overview, (61), 85–117. Scornet, E., Biau, G. & Vert, J.-P. (2015). Consistency of random forests. 0090-5364, 43 (4), 1716–1741. https://doi.org/10.1214/15-AOS1321 Sharpe, W. F. (1964). Capital asset prices: a theory of market equilibirium under conditions of risk, (19), 425–442. Sharpe, W. F. (1970). Portolio theory and capital markets. McGraw-Hill. Staniswalis, J. G. (1989). The kernel estimate of a regression function in likelihood-based models. 0162-1459, 84 (405), 276. https://doi.org/10.2307/2289874 Stone, C. J. (1977). Consistent nonparametric regression, (5), 595–620. Tibshirani, R. & Hastie, T. (1987). Local likelihood estimation. 0162-1459, 82 (398), 559–567. https://doi.org/10.1080/01621459.1987.10478466 Timmermann, A. (2006). Chapter 4 forecast combinations. In G. Elliott, C. Granger Vovk, V. (1998). A game of prediction with expert advice [PII: S0022000097915567]. Journal of Computer and System Sciences, 56 (2), 153–173. https://doi.org/10.1006/jcss. 1997.1556 Wager, S. & Athey, S. (2018). Estimation and inference of heterogeneous treatment effects using random forests. 0162-1459, 113 (523), 1228–1242. https://doi.org/10.1080/01621459.2017.1319839 Wager, S. & Walther, G. (2015). Adaptive concentration of regression trees, with application to random forests. https://arxiv.org/pdf/1503.06388 Zeileis, A., Hothorn, T. & Hornik, K. (2008). Model-based recursive partitioning. 1061-8600, 17 (2), 492–514. https://doi.org/10.1198/106186008X31933
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dc.rights.license.spa.fl_str_mv	Atribución-NoComercial-CompartirIgual 4.0 Internacional
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
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dc.format.extent.spa.fl_str_mv	xii, 48 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ciencias Económicas - Maestría en Administración
dc.publisher.department.spa.fl_str_mv	Escuela de Administración y Contaduría Pública
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ciencias Económicas
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
bitstream.url.fl_str_mv	https://repositorio.unal.edu.co/bitstream/unal/80095/3/license.txt https://repositorio.unal.edu.co/bitstream/unal/80095/4/Tesis_RobertRichter.pdf https://repositorio.unal.edu.co/bitstream/unal/80095/5/Tesis_RobertRichter.pdf.jpg
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spelling	Atribución-NoComercial-CompartirIgual 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Gómez Portilla, Karoll7d00bb94aa1a311b5728decda6dfed59600Richter, Robert45cf2c53b815ab02f879a4bba0877ebfGrupo Interdisciplinario en Teoría e Investigación Aplicada en Ciencias Económicas2021-09-03T22:41:41Z2021-09-03T22:41:41Z2021-09-03https://repositorio.unal.edu.co/handle/unal/80095Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/IlustracionesThis thesis proposes to apply forecasts produced by expert aggregation as novel predictor of expected returns to 2 different portfolio strategies: 1) mean-variance as proposed by (Markowitz, 1952) and 2) shrinkage of the covariance matrix S as in (Ledoit, 2004). Experts were built by generating forecasts with quantile regression as in generalized random forests and automatised versions of exponential smoothing and ARIMA. This study evaluates the predictive performance of two forecast combination algorithms 1) ML-Prod and 2) ML-Poly using a simulation study, before applying the superior method to a portfolio scenario. After evaluating prediction accuracy, the superior ML-Poly algorithm was chosen to forecast expected returns and showed promising out-of-sample results for the considered portfolios, returning superior values for the selected performance parameter and only marginal inferior results in terms of turnover ratio. Using the simulation study, the results of the portfolios were also validated.Esta tesis propone aplicar los pronósticos generados por la agregación de expertos como un novedoso predictor de los rendimientos esperados a 2 estrategias de portafolio diferentes: 1) Mean-Variance como propone (Markowitz, 1952) y 2) contracción de la matriz de covarianza S como en (Ledoit, 2004). Los expertos se construyeron generando pronósticos con Quantile Regression de Generalized Random Forests y versiones automatizadas de Exponential Smoothing y ARIMA. Este estudio evalúa la precisión de los pronósticos de dos algoritmos de agregación de expertos 1) ML-Prod y 2) ML-Poly mediante un estudio de simulación, antes de aplicar el método superior a un portafolio diversificado. Después de evaluar la precisión de los pronósticos, se eligió el algoritmo superior ML-Poly para pronosticar los rendimientos esperados y mostró resultados prometedores fuera de la muestra para los portafolios considerados, devolviendo valores superiores para los parámetros de rendimiento seleccionados y resultados inferiores marginales en términos de ratio de rotación. Mediante el estudio de simulación, también se validaron los resultados de los portafolios. (Texto tomado de la fuente).Mención MeritoriaTesis de grado presentada como requisito parcial para optar al título de: Magister en Administración de Negocios (Universidad Europea de Viadrina)MaestríaMagister en AdministraciónEstudio EmpiricoSeminario de Investigación IIxii, 48 páginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ciencias Económicas - Maestría en AdministraciónEscuela de Administración y Contaduría PúblicaFacultad de Ciencias EconómicasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá330 - EconomíaFinancial Forecasting and SimulationPredicción y simulación financieraFinancial Forecasting and SimulationPredicción y simulación financieraC53 Forecasting Models; Simulation MethodsEconomic forecastingPronóstico de la economíaForecasting techniquesTécnicas de predicciónShrinkageDecision tressExpert aggregationMean-varianceGeneralized random forestAutomatic arimaPortfolio optimisationExponential smoothingMedia-varianzaÁrboles de decisionArima automatizadoAgregación de expertosOptimización de portafoliosEstimating expected returns with forecast combinationsEstimación de los rendimientos esperados con combinaciones de previsionesTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAmit, Y. & Geman, D. (1997). Shape quantization and recognition with randomized trees. 0899-7667, 9 (7), 1545–1588. https://doi.org/10.1162/neco.1997.9.7.1545Anderson, B. D. O. (2012). Optimal filtering. Dover Publications.Aoki, M. & Havenner, A. (1991). State space modeling of multiple time series. Econometric Reviews, 10 (1), 1–59. https://doi.org/10.1080/07474939108800194Arlot, S. & Genuer, R. (2014). Analysis of purely random forests bias. https://arxiv.org/pdf/1407.3939Athey, S., Tibshirani, J. & Wager, S. (2019). Generalized random forests. 0090-5364, 47 (2), 1148–1178. https://doi.org/10.1214/18-AOS1709Ban, G.-Y., El Karoui, N. & Lim, A. E. B. (2018). Machine learning and portfolio optimiz ation, (64), 1136–1154.Biau, G. (2012). Analysis of a random forests model, (13), 1063–1095.Biau, G., Devroye, L. & Lugosi, G. (2008). Consistency of random forests and other averaging classifiers, (9), 2015–2033.Blum, A. & Mansour, Y. (2007). From external to internal regret. Journal of Machine Learning Research, 8 (47), 1307–1324.Box, G. E. P. & Jenkins, G. M. (1970). Time series analysis: Forecasting and control. Holden Day.Breiman, L. (1984). Classification and regression trees [Breiman, Leo, (author.)]. [Routledge].Breiman, L. (1996). Bagging predictors [PII: BF00058655]. 08856125, 24 (2), 123–140. https://doi.org/10.1007/BF00058655Breiman, L. (2001). Random forests [PII: 354300]. 08856125, 45 (1), 5–32. https://doi.org/10.1023/A:1010933404324Brockwell, P. J. & Davis, R. A. (2006). Time series: Theory and methods (2nd ed., correc ted.). New York, Springer.Buhlmann, P. & Yu, B. (2002). Analyzing bagging [PII: aos30n4r01]. 0090-5364, 30 (4), 927–961. https://doi.org/10.1214/aos/1031689014Cesa-Bianchi, N. & Lugosi, G. (2006). Prediction, learning, and games. Cambridge University Press.Cesa-Bianchi, N. & Lugosi, G. (2003). Potential-based algorithms in on-line prediction and game theory [PII: 5120299]. 08856125, 51 (3), 239–261. https://doi.org/10.1023/A:1022901500417Amit, Y. & Geman, D. (1997). Shape quantization and recognition with randomized trees. 0899-7667, 9 (7), 1545–1588. https://doi.org/10.1162/neco.1997.9.7.1545Anderson, B. D. O. (2012). Optimal filtering. Dover Publications.Aoki, M. & Havenner, A. (1991). State space modeling of multiple time series. Econometric Reviews, 10 (1), 1–59. https://doi.org/10.1080/07474939108800194Arlot, S. & Genuer, R. (2014). Analysis of purely random forests bias. https://arxiv.org/pdf/1407.3939Athey, S., Tibshirani, J. & Wager, S. (2019). Generalized random forests. 0090-5364, 47 (2), 1148–1178. https://doi.org/10.1214/18-AOS1709Ban, G.-Y., El Karoui, N. & Lim, A. E. B. (2018). Machine learning and portfolio optimiz ation, (64), 1136–1154.Biau, G. (2012). Analysis of a random forests model, (13), 1063–1095.Biau, G., Devroye, L. & Lugosi, G. (2008). Consistency of random forests and other averaging classifiers, (9), 2015–2033.Blum, A. & Mansour, Y. (2007). From external to internal regret. Journal of Machine Learning Research, 8 (47), 1307–1324.Box, G. E. P. & Jenkins, G. M. (1970). Time series analysis: Forecasting and control. Holden Day.Breiman, L. (1984). Classification and regression trees [Breiman, Leo, (author.)]. [Routledge].Breiman, L. (1996). Bagging predictors [PII: BF00058655]. 08856125, 24 (2), 123–140. https://doi.org/10.1007/BF00058655Breiman, L. (2001). Random forests [PII: 354300]. 08856125, 45 (1), 5–32. https://doi.org/10.1023/A:1010933404324Brockwell, P. J. & Davis, R. A. (2006). Time series: Theory and methods (2nd ed., correc ted.). New York, Springer.Buhlmann, P. & Yu, B. (2002). Analyzing bagging [PII: aos30n4r01]. 0090-5364, 30 (4), 927–961. https://doi.org/10.1214/aos/1031689014Cesa-Bianchi, N. & Lugosi, G. (2006). Prediction, learning, and games. Cambridge University Press.Cesa-Bianchi, N. & Lugosi, G. (2003). Potential-based algorithms in on-line prediction and game theory [PII: 5120299]. 08856125, 51 (3), 239–261. https://doi.org/10.1023/A:1022901500413Hyndman, R. J., Koehler, A. B., Ord, J. K. & Snyder, R. D. (2008). Forecasting with exponential smoothing: The state space approach. Berlin, Heidelberg, Springer. https://doi.org/10.1007/978-3-540-71918-2Jagannathan, R. & Ma, T. (2003). Risk reduction in large portfolios: Why imposing the wrong constraints helps, 58 (4), 1651–1683. https://doi.org/10.1111/1540-6261.00580Koenker, R. (2005). Quantile regression [Koenker, Roger (VerfasserIn)]. Cambridge, Cam bridge University Press. https://doi.org/10.1017/CBO9780511754098Koenker, R. & Bassett, G. (1978). Regression quantiles [Econometrica, 46(1), 33]. Econo metrica, 46 (1), 33. https://doi.org/10.2307/1913643Kwiatkowski, D., Phillips, P. C., Schmidt, P. & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root [PII: 030440769290104Y]. 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Model-based recursive partitioning. 1061-8600, 17 (2), 492–514. https://doi.org/10.1198/106186008X31933Público generalLICENSElicense.txtlicense.txttext/plain; charset=utf-83964https://repositorio.unal.edu.co/bitstream/unal/80095/3/license.txtcccfe52f796b7c63423298c2d3365fc6MD53ORIGINALTesis_RobertRichter.pdfTesis_RobertRichter.pdfTesis de Maestría en Administraciónapplication/pdf926881https://repositorio.unal.edu.co/bitstream/unal/80095/4/Tesis_RobertRichter.pdfcd18fb1d754ce55edfffe488aa515f38MD54THUMBNAILTesis_RobertRichter.pdf.jpgTesis_RobertRichter.pdf.jpgGenerated Thumbnailimage/jpeg5051https://repositorio.unal.edu.co/bitstream/unal/80095/5/Tesis_RobertRichter.pdf.jpg9ca64f98c4271b8094c8d95e497702f7MD55unal/80095oai:repositorio.unal.edu.co:unal/800952024-07-28 23:59:06.228Repositorio Institucional Universidad Nacional de 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Estimating expected returns with forecast combinations

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