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pro vyhledávání: '"Moreno Pirachican, Wilson Fernando"'
Publikováno v:
Aggarwal C.(2018), Neural Networks and Deep Learning. Springer. Yorktown Heights, New York.
Bellman R., (1957),Dynamic Programming. Princeton University Press. Princeton, New Jersey.
Björk T. (2009), Arbitrage Theory in Continuos Time 3th. Ed. OXFORD UNIVERSITY PRESS. Stockholm School of Economics.
Desai R., Lele T., Viens F, (2003), A Monte Carlo method for portfolio optimization under partially observed stochastic volatility. IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, Proceedings (CIFEr). pág. 257-263.
Detemple J., García R., Rindisbacher M. (2003), A Monte Carlo Method for Optimal Portfolios. The Journal of Finance Vol. 58 pág. 401-446. Wiley for the American Finance Association.
Freitas F., De Souza A., De Almeida A.(2009), Prediction-based portfolio optimization model using neural networks. Neurocomputación, Volumen 72, números 10–12, pág. 2155-2170.
Gerón A, (2019), Hands On Machine Learning with Scikit-Learn, Keras, TensorFlow, O'Reilly Media Inc, Canada.
Giordano F., Fox W., Horton S.,(2014), A First Course in Mathematical Modeling, CENGAGE Learning.
Hirsa A. (2013) Computational methods in Finance. CRC Press Taylor \& Francis Group.
Kafash B., (2019), Approximating the Solution of Stochastic Optimal Control Problems and the Merton's Portfolio Selection Model. Springer: Computational Economics 54:763–782.
Kingma D,Lei J., (2014), Adam: A Method for Stochastic Optimization, published as a conference paper at the 3rd International Conference for Learning Representations, San Diego, 2015.
Korn R., Korn E, (2010), Option Pricing and Portfolio Optimization. Amercan Mathematical Society. Graduate Studies in Mathematics Vol.31. Stelzenberg, Germany.
Korn R., Korn E,. Kroisandt G., (2010), Monte Carlo methods and models in finance and insurance. CRC Press Taylor \& Francis Group.
Krawczyk J. (2001). A Markovian approximated solution to a portfolio management problem. Information Technology and Management.
Kushner H, Dupois P.(2001). Numerical Methods for Stochastic Control Problems in Continuous Time. New York. Springer.
Merton R., (1971), Optimum Consumption and Portfolio Rules in a Continuous-Time Model. J. Econom. Theory 3, 373-413.
Moolayil J., (2019), Learn Keras for Deep Neural Networks. Vancouver, Canadá. APRESS.
Pham H (2009), Continuous-time Stochastic Control and Optimization with Financial Applications. New York. Springer
Pham H (2010), Stochastic control and applications in finance. University of Paris Diderot, LPMA, Paris.
Ramirez H. (2017), Numerical methods in finance course 2017. Universidad del Rosario, Bogotá.
Steiner M., Wittkemper H., Portfolio optimization with a neural network implementation of the coherent market hypothesis, European Journal of Operational Research, Vol. 100, issue 1, pág. 27-40.
Yin G., Jin H., \& Jin, Z (2009), Numerical methods for portfolio selection with bounded constraints. Journal of Computational and Applied Mathematics, 233(2), 564-581.
Repositorio EdocUR-U. Rosario
Universidad del Rosario
instacron:Universidad del Rosario
Bellman R., (1957),Dynamic Programming. Princeton University Press. Princeton, New Jersey.
Björk T. (2009), Arbitrage Theory in Continuos Time 3th. Ed. OXFORD UNIVERSITY PRESS. Stockholm School of Economics.
Desai R., Lele T., Viens F, (2003), A Monte Carlo method for portfolio optimization under partially observed stochastic volatility. IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, Proceedings (CIFEr). pág. 257-263.
Detemple J., García R., Rindisbacher M. (2003), A Monte Carlo Method for Optimal Portfolios. The Journal of Finance Vol. 58 pág. 401-446. Wiley for the American Finance Association.
Freitas F., De Souza A., De Almeida A.(2009), Prediction-based portfolio optimization model using neural networks. Neurocomputación, Volumen 72, números 10–12, pág. 2155-2170.
Gerón A, (2019), Hands On Machine Learning with Scikit-Learn, Keras, TensorFlow, O'Reilly Media Inc, Canada.
Giordano F., Fox W., Horton S.,(2014), A First Course in Mathematical Modeling, CENGAGE Learning.
Hirsa A. (2013) Computational methods in Finance. CRC Press Taylor \& Francis Group.
Kafash B., (2019), Approximating the Solution of Stochastic Optimal Control Problems and the Merton's Portfolio Selection Model. Springer: Computational Economics 54:763–782.
Kingma D,Lei J., (2014), Adam: A Method for Stochastic Optimization, published as a conference paper at the 3rd International Conference for Learning Representations, San Diego, 2015.
Korn R., Korn E, (2010), Option Pricing and Portfolio Optimization. Amercan Mathematical Society. Graduate Studies in Mathematics Vol.31. Stelzenberg, Germany.
Korn R., Korn E,. Kroisandt G., (2010), Monte Carlo methods and models in finance and insurance. CRC Press Taylor \& Francis Group.
Krawczyk J. (2001). A Markovian approximated solution to a portfolio management problem. Information Technology and Management.
Kushner H, Dupois P.(2001). Numerical Methods for Stochastic Control Problems in Continuous Time. New York. Springer.
Merton R., (1971), Optimum Consumption and Portfolio Rules in a Continuous-Time Model. J. Econom. Theory 3, 373-413.
Moolayil J., (2019), Learn Keras for Deep Neural Networks. Vancouver, Canadá. APRESS.
Pham H (2009), Continuous-time Stochastic Control and Optimization with Financial Applications. New York. Springer
Pham H (2010), Stochastic control and applications in finance. University of Paris Diderot, LPMA, Paris.
Ramirez H. (2017), Numerical methods in finance course 2017. Universidad del Rosario, Bogotá.
Steiner M., Wittkemper H., Portfolio optimization with a neural network implementation of the coherent market hypothesis, European Journal of Operational Research, Vol. 100, issue 1, pág. 27-40.
Yin G., Jin H., \& Jin, Z (2009), Numerical methods for portfolio selection with bounded constraints. Journal of Computational and Applied Mathematics, 233(2), 564-581.
Repositorio EdocUR-U. Rosario
Universidad del Rosario
instacron:Universidad del Rosario
Supongamos que un agente con riqueza positiva desea invertir una proporción en un activo de riesgo y el resto en un bono. El problema consiste en escoger el porcentaje de riqueza óptimo que maximíce su utilidad al final del periodo de inversión.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4240e42a448cd9e229426e6ca33c7e3e
