Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Ramirez, Hugo E."'
This paper studies the optimal liquidation of stocks in the presence of temporary and permanent price impacts, and we focus in the case of cryptocurrencies. We start by presenting analytical solutions to the problem with linear temporary impact, and
Externí odkaz:
http://arxiv.org/abs/2303.10043
Autor:
Ramirez, Hugo E., Serrano, Rafael
We study investment and insurance demand decisions for an agent in a theoretical continuous-time expected utility maximization model that combines risky assets with an (exogenous) insurable background risk. This risk takes the form of a jump-diffusio
Externí odkaz:
http://arxiv.org/abs/2303.04236
Autor:
Ramírez, Hugo E., Serrano, Rafael
Publikováno v:
In Applied Mathematics and Computation 15 January 2025 485
Autor:
RAMIREZ, HUGO E.1 (AUTHOR) hugoedu.ramirez@urosario.edu.co, DUCK, PETER2 (AUTHOR) duck@maths.manchester.ac.uk, JOHNSON, PAUL V.2 (AUTHOR) pjohnson2@maths.manchester.ac.uk, HOWELL, SYDNEY3 (AUTHOR) sydney.howell@manchester.ac.uk
Publikováno v:
International Journal of Theoretical & Applied Finance. Sep2019, Vol. 22 Issue 6, pN.PAG-N.PAG. 31p. 1 Chart, 9 Graphs.
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
Publikováno v:
Andersen, Torben G y col. (2001). «The distribution of realized stock return volatility». En: Journal of financial economics 61.1, págs. 43-76
Banco de la República (s.f.). Tasa Representativa del Mercado. https://www.banrep. gov.co/es/estadisticas/trm. Accedido 08-11-2019.
Brownlee, Jason (2017). Multivariate Time Series Forecasting with LSTMs in Keras. https://machinelearningmastery.com/multivariate-time-seriesforecasting-lstms-keras/. Accedido 02-07-2019.
Bucci, Andrea (2019). Realized Volatility Forecasting with Neural Networks. MPRA Paper. url: https://ideas.repec.org/p/pra/mprapa/95443.html.
Chollet, François y col. (2015). Keras. https://keras.io.
Corredor Velandia, César Augusto y Stefano Vega Mazzeo (2012). «Análisis de corto plazo del contagio de variables y noticias financieras en estados unidos y Colombia». En: Revista de Economía del Caribe 9.
Engle, Robert F y Andrew J Patton (2007). «What good is a volatility model?» En: Forecasting volatility in the financial markets. Elsevier, págs. 47-63
Fischer, Thomas y Christopher Krauss (2018). «Deep learning with long short-term memory networks for financial market predictions». En: European Journal of Operational Research 270.2, págs. 654-669.
Géron, Aurélien (2017). Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems. O’Reilly Media, Inc.
Kim, Ha Young y Chang Hyun Won (2018). «Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models». En: Expert Systems with Applications 103, págs. 25-37.
Kingma, Diederik P. y Jimmy Ba (2014). Adam: A Method for Stochastic Optimization. arXiv: 1412.6980[cs.LG].
Kristjanpoller, Werner y Marcel C Minutolo (2016). «Forecasting volatility of oil price using an artificial neural network-GARCH model». En: Expert Systems with Applications 65, págs. 233-241.
Nelson, David MQ, Adriano CM Pereira y Renato A de Oliveira (2017). «Stock market’s price movement prediction with LSTM neural networks». En: 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, págs. 1419-1426.
Olah, Christopher (2015). Understanding LSTM networks. https://colah.github. io/posts/2015-08-Understanding-LSTMs/. Accedido 06-11-2019.
Parra Barrios, Alberto (2019). «Impacto de las decisiones de política monetaria de la FED en indicadores de la economía colombiana durante el periodo 2007-2015». En: Revista Finanzas y Política Económica, Vol. 11, no. 1 (ene.–jun.) p. 149-182.
Petneházi, Gábor y József Gáll (2019). «Exploring the predictability of range-based volatility estimators using recurrent neural networks». En: Intelligent Systems in Accounting, Finance and Management.
Sheppard, Kevin (2019). Financial Econometrics Notes. https://www.kevinsheppard.com/teaching/mfe/notes/.
Siami-Namini, Sima, Neda Tavakoli y Akbar Siami Namin (2018). «A Comparison of ARIMA and LSTM in Forecasting Time Series». En: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, págs. 1394-1401.
Srivastava, Nitish y col. (2014). «Dropout: a simple way to prevent neural networks from overfitting». En: The journal of machine learning research 15.1, págs. 1929-1958.
Suárez, Álvaro Andrés Cámaro y col. (2006). «Una aproximación empírica en la relación entre las tasas de interés de los TES TF y el tipo de cambio en Colombia». En: INNOVAR. Revista de Ciencias Administrativas y Sociales 16.27, págs. 47-55.
Repositorio EdocUR-U. Rosario
Universidad del Rosario
instacron:Universidad del Rosario
Banco de la República (s.f.). Tasa Representativa del Mercado. https://www.banrep. gov.co/es/estadisticas/trm. Accedido 08-11-2019.
Brownlee, Jason (2017). Multivariate Time Series Forecasting with LSTMs in Keras. https://machinelearningmastery.com/multivariate-time-seriesforecasting-lstms-keras/. Accedido 02-07-2019.
Bucci, Andrea (2019). Realized Volatility Forecasting with Neural Networks. MPRA Paper. url: https://ideas.repec.org/p/pra/mprapa/95443.html.
Chollet, François y col. (2015). Keras. https://keras.io.
Corredor Velandia, César Augusto y Stefano Vega Mazzeo (2012). «Análisis de corto plazo del contagio de variables y noticias financieras en estados unidos y Colombia». En: Revista de Economía del Caribe 9.
Engle, Robert F y Andrew J Patton (2007). «What good is a volatility model?» En: Forecasting volatility in the financial markets. Elsevier, págs. 47-63
Fischer, Thomas y Christopher Krauss (2018). «Deep learning with long short-term memory networks for financial market predictions». En: European Journal of Operational Research 270.2, págs. 654-669.
Géron, Aurélien (2017). Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems. O’Reilly Media, Inc.
Kim, Ha Young y Chang Hyun Won (2018). «Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models». En: Expert Systems with Applications 103, págs. 25-37.
Kingma, Diederik P. y Jimmy Ba (2014). Adam: A Method for Stochastic Optimization. arXiv: 1412.6980[cs.LG].
Kristjanpoller, Werner y Marcel C Minutolo (2016). «Forecasting volatility of oil price using an artificial neural network-GARCH model». En: Expert Systems with Applications 65, págs. 233-241.
Nelson, David MQ, Adriano CM Pereira y Renato A de Oliveira (2017). «Stock market’s price movement prediction with LSTM neural networks». En: 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, págs. 1419-1426.
Olah, Christopher (2015). Understanding LSTM networks. https://colah.github. io/posts/2015-08-Understanding-LSTMs/. Accedido 06-11-2019.
Parra Barrios, Alberto (2019). «Impacto de las decisiones de política monetaria de la FED en indicadores de la economía colombiana durante el periodo 2007-2015». En: Revista Finanzas y Política Económica, Vol. 11, no. 1 (ene.–jun.) p. 149-182.
Petneházi, Gábor y József Gáll (2019). «Exploring the predictability of range-based volatility estimators using recurrent neural networks». En: Intelligent Systems in Accounting, Finance and Management.
Sheppard, Kevin (2019). Financial Econometrics Notes. https://www.kevinsheppard.com/teaching/mfe/notes/.
Siami-Namini, Sima, Neda Tavakoli y Akbar Siami Namin (2018). «A Comparison of ARIMA and LSTM in Forecasting Time Series». En: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, págs. 1394-1401.
Srivastava, Nitish y col. (2014). «Dropout: a simple way to prevent neural networks from overfitting». En: The journal of machine learning research 15.1, págs. 1929-1958.
Suárez, Álvaro Andrés Cámaro y col. (2006). «Una aproximación empírica en la relación entre las tasas de interés de los TES TF y el tipo de cambio en Colombia». En: INNOVAR. Revista de Ciencias Administrativas y Sociales 16.27, págs. 47-55.
Repositorio EdocUR-U. Rosario
Universidad del Rosario
instacron:Universidad del Rosario
En este trabajo se propone un modelo híbrido LSTM-GARCH para el pronóstico de la volatilidad de la tasa representativa del mercado (TRM). Este modelo es una red neuronal recurrente LSTM, en la cual se incluyen como variables explicativas los coefic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::976b1da2fb28931541950d285517084c
https://repository.urosario.edu.co/handle/10336/20708
https://repository.urosario.edu.co/handle/10336/20708
Publikováno v:
I. J. Cox, J.C. and S. Ross. A theory of the term structure of interest rates. Econometrica, 53(2):385-408., 1985.
G. Cybenko. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems, 1989.
D. N. Eric Chin and S. Ólafsson. Problems and solutions in mathematical fi nance (volume 1: Stochastic calculus). John Wiley & Sons, Ltd, 2014.
J. Gatheral. The volatility surface: A practitioner's guide. John Wiley & Sons, Ltd, 2006.
P. Gupta. Topics in laplace and fourier transforms. Laxmi Publications Pvt.Ltd., 2019.
S. L. Heston. A closed-form solution for options with stochastic volatility with applications to bond and currency options. Review of Financial Studies, 6:327-43., 1993.
K. Hout and S.Foulon. ADI finite difference schemes for option pricing in the heston model with correlation. International Journal of Numerical Analysis and Modeling, 7(2):303-320., 2010.
T. Kluge. Pricing derivatives in stochastic volatility models using the fi nite difference method. Web page, 2002.
A. L. Lewis. A simple option formula for general jump-diffusion and other exponential lévy processes. Envision Financial Systems and OptionCity.net, 2001.
N. G. Ramazan Gencay and D. Kukolj. Option pricing with modular neural networks. JEL No. C45; G12, 2008.
F. D. Rouah. The heston model and its extensions in matlab and c#. John Wiley & Sons, Inc., Hoboken, New Jersey, 2013.
C. W. O. Shuaiqiang Liu and S. M. Bohte. Pricing options and computing implied volatilities using neural networks. Risks, 7(1) (2019), 2019.
Repositorio EdocUR-U. Rosario
Universidad del Rosario
instacron:Universidad del Rosario
G. Cybenko. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems, 1989.
D. N. Eric Chin and S. Ólafsson. Problems and solutions in mathematical fi nance (volume 1: Stochastic calculus). John Wiley & Sons, Ltd, 2014.
J. Gatheral. The volatility surface: A practitioner's guide. John Wiley & Sons, Ltd, 2006.
P. Gupta. Topics in laplace and fourier transforms. Laxmi Publications Pvt.Ltd., 2019.
S. L. Heston. A closed-form solution for options with stochastic volatility with applications to bond and currency options. Review of Financial Studies, 6:327-43., 1993.
K. Hout and S.Foulon. ADI finite difference schemes for option pricing in the heston model with correlation. International Journal of Numerical Analysis and Modeling, 7(2):303-320., 2010.
T. Kluge. Pricing derivatives in stochastic volatility models using the fi nite difference method. Web page, 2002.
A. L. Lewis. A simple option formula for general jump-diffusion and other exponential lévy processes. Envision Financial Systems and OptionCity.net, 2001.
N. G. Ramazan Gencay and D. Kukolj. Option pricing with modular neural networks. JEL No. C45; G12, 2008.
F. D. Rouah. The heston model and its extensions in matlab and c#. John Wiley & Sons, Inc., Hoboken, New Jersey, 2013.
C. W. O. Shuaiqiang Liu and S. M. Bohte. Pricing options and computing implied volatilities using neural networks. Risks, 7(1) (2019), 2019.
Repositorio EdocUR-U. Rosario
Universidad del Rosario
instacron:Universidad del Rosario
En esta tesis, implementamos el aprendizaje profundo para la fijación de precios de opciones. Se propone un enfoque basado en datos, a través de una red neuronal artificial (ANN), para calcular el precio de las opciones de compra europeas con el mo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::21c1d205352ec73bc1e5f60cdce9669a
https://repository.urosario.edu.co/handle/10336/20712
https://repository.urosario.edu.co/handle/10336/20712
Autor:
Castañeda, Daniel Santiago
Publikováno v:
Schumacher, R. P., & Chen, H. (2009). Textual analysis of stock market prediction using breaking financial news: The AZFin text system. ACM Transactions on Information Systems (TOIS), 27(2), 12
Schumacher, R. P., Zhang, Y., & Huang, C. N. (2009, October). Sentiment analysis of financial news articles. In 20th Annual Conference of International Information Management Association.
Huang, W., Nakamori, Y., & Wang, S. Y. (2005). Forecasting stock market movement direction with support vector machine. Computers & Operations Research, 32(10), 2513-2522.
Theodoridis, S., & Mavroforakis, M. (2007). Reduced convex hulls: a geometric approach to support vector machines [lecture notes]. IEEE Signal Processing Magazine, 24(3), 119-122.
Kara, Y., Boyacioglu, M. A., & Baykan, Ö. K. (2011). Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Expert systems with Applications, 38(5), 5311-5319
Gala García, Y. (2013). Algoritmos SVM para problemas sobre big data (Master's thesis).
Martín Guareño, J. J. (2016). Support vector regression: propiedades y aplicaciones.
Cocianu, C. L., & Grigoryan, H. (2016). MACHINE LEARNING TECHNIQUES FOR STOCK MARKET PREDICTION. A CASE STUDY OF OMV PETROM. Economic Computation & Economic Cybernetics Studies & Research, 50(3).
Pyo, S., Lee, J., Cha, M., & Jang, H. (2017). Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets. PloS one, 12(11), e0188107.
Xia, X. L. (2018). Training sparse least squares support vector machines by the QR decomposition. Neural Networks, 106, 175-184
Repositorio EdocUR-U. Rosario
Universidad del Rosario
instacron:Universidad del Rosario
Schumacher, R. P., Zhang, Y., & Huang, C. N. (2009, October). Sentiment analysis of financial news articles. In 20th Annual Conference of International Information Management Association.
Huang, W., Nakamori, Y., & Wang, S. Y. (2005). Forecasting stock market movement direction with support vector machine. Computers & Operations Research, 32(10), 2513-2522.
Theodoridis, S., & Mavroforakis, M. (2007). Reduced convex hulls: a geometric approach to support vector machines [lecture notes]. IEEE Signal Processing Magazine, 24(3), 119-122.
Kara, Y., Boyacioglu, M. A., & Baykan, Ö. K. (2011). Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Expert systems with Applications, 38(5), 5311-5319
Gala García, Y. (2013). Algoritmos SVM para problemas sobre big data (Master's thesis).
Martín Guareño, J. J. (2016). Support vector regression: propiedades y aplicaciones.
Cocianu, C. L., & Grigoryan, H. (2016). MACHINE LEARNING TECHNIQUES FOR STOCK MARKET PREDICTION. A CASE STUDY OF OMV PETROM. Economic Computation & Economic Cybernetics Studies & Research, 50(3).
Pyo, S., Lee, J., Cha, M., & Jang, H. (2017). Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets. PloS one, 12(11), e0188107.
Xia, X. L. (2018). Training sparse least squares support vector machines by the QR decomposition. Neural Networks, 106, 175-184
Repositorio EdocUR-U. Rosario
Universidad del Rosario
instacron:Universidad del Rosario
Durante las últimas décadas y gracias al desarrollo de la tecnología en los informatica, los algoritmos de machine learning han evolucionado lo que ha permitido en la mayoría de los casos obtener mejores resultados que los algoritmos tradicionale
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::99db5afe2d454b9f37ea54e5e04fd77f
http://repository.urosario.edu.co/handle/10336/20114
http://repository.urosario.edu.co/handle/10336/20114
Publikováno v:
Alfonsi, A. Fruth, A. and Schied, A. (2010). Optimal execution strategies in limit order books with general shape functions. Quantitative Finance, Taylor
Almgren, R., Chriss, N. (2000). Optimal execution of portfolio transactions. J. Risk 3, 5-39
Barger, W. & Lorig, M. (2018). Optimal Liquidation Under Stochastic Price Impact. International Journal of Theoretical and Applied Finance
Barles G. and Souganidis, P.E. (1991). Convergence of approximation schemes for fully nonlinear second order equations. Asymptotic Analysis, 4(3):271-283.
Bellman, R. & Dreyfus, S. (1962). Applied dynamic programming. A report kprepared for United States Air Force project RAND.
Bershova, N. & Rakhlin, D. (2013). The Non-Linear Market Impact of Large Trades: Evidence from Buy-Side Order Flow. Quantitative Finance. Vol. 13, No. 11, 1759-1778.
Cartea, A., Jaimungal, S. & Penalva, J. (2015). Algorithmic and high-frequency trading. Cambridge University Press.
Cartea, A. & Jaimungal S. (2016)(A). A closed-form execution strategy to target volume weighted average price. SIAM Journal on Financial Mathematics 7(1), 760-785.
Cartea, A. & Jaimungal S. (2016)(B). Incorporating order-flow into optimal execution. Mathematics and Financial Economics 10(3), 339-364.
Gu´eant, O. (2014). Permanent market impact can be nonlinear. Preprint Available online at https://arxiv.org/pdf/1305.0413.pdf
Gatheral, J. (2010). No-dynamic-arbitrage and market impact. Quantitative Finance,10(7):749-759.
Huberman G., Stanzl W. (2004). Price manipulation and quasi-arbitrage. Econometrica, 72(4):1247-1275
Pham, H. (2009) Continuous-time Stochastic Control and Optimization with Financial Applications. Springer.
Subramanian, A. (2008). Optimal Liquidation by a Large Investor. SIAM Journal of Applied Mathematics. 68. 1168-1201.
Tóth, B., Eisler, Z., Bouchaud, J.P. (2016). The square-root impact law also holds for option markets. Wilmott 2016(85), 70-73
Repositorio EdocUR-U. Rosario
Universidad del Rosario
instacron:Universidad del Rosario
Almgren, R., Chriss, N. (2000). Optimal execution of portfolio transactions. J. Risk 3, 5-39
Barger, W. & Lorig, M. (2018). Optimal Liquidation Under Stochastic Price Impact. International Journal of Theoretical and Applied Finance
Barles G. and Souganidis, P.E. (1991). Convergence of approximation schemes for fully nonlinear second order equations. Asymptotic Analysis, 4(3):271-283.
Bellman, R. & Dreyfus, S. (1962). Applied dynamic programming. A report kprepared for United States Air Force project RAND.
Bershova, N. & Rakhlin, D. (2013). The Non-Linear Market Impact of Large Trades: Evidence from Buy-Side Order Flow. Quantitative Finance. Vol. 13, No. 11, 1759-1778.
Cartea, A., Jaimungal, S. & Penalva, J. (2015). Algorithmic and high-frequency trading. Cambridge University Press.
Cartea, A. & Jaimungal S. (2016)(A). A closed-form execution strategy to target volume weighted average price. SIAM Journal on Financial Mathematics 7(1), 760-785.
Cartea, A. & Jaimungal S. (2016)(B). Incorporating order-flow into optimal execution. Mathematics and Financial Economics 10(3), 339-364.
Gu´eant, O. (2014). Permanent market impact can be nonlinear. Preprint Available online at https://arxiv.org/pdf/1305.0413.pdf
Gatheral, J. (2010). No-dynamic-arbitrage and market impact. Quantitative Finance,10(7):749-759.
Huberman G., Stanzl W. (2004). Price manipulation and quasi-arbitrage. Econometrica, 72(4):1247-1275
Pham, H. (2009) Continuous-time Stochastic Control and Optimization with Financial Applications. Springer.
Subramanian, A. (2008). Optimal Liquidation by a Large Investor. SIAM Journal of Applied Mathematics. 68. 1168-1201.
Tóth, B., Eisler, Z., Bouchaud, J.P. (2016). The square-root impact law also holds for option markets. Wilmott 2016(85), 70-73
Repositorio EdocUR-U. Rosario
Universidad del Rosario
instacron:Universidad del Rosario
This study addresses a basic model to solve a problem of liquidation of shares, which does not take into consideration the round trip trade, a fundamental concept for establishing the condition of linearity of the permanent impact, and excluded from
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5293b25808e4a91077dc53a8c7b60657
http://repository.urosario.edu.co/handle/10336/20022
http://repository.urosario.edu.co/handle/10336/20022
Autor:
Romero Ramirez, Juan Felipe
Publikováno v:
Repositorio EdocUR-U. Rosario
Universidad del Rosario
instacron:Universidad del Rosario
Universidad del Rosario
instacron:Universidad del Rosario
Esta tesis implementa una estrategia de trading de alta frecuencia, conocida como pairs trading, sobre los activos de un portafolio haciendo uso de diversos algoritmos de machine learning. Se hace énfasis en el uso de los Hidden Markov Models para m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::14a4aeb3bd61faf3e0217392f1d3be97
http://repository.urosario.edu.co/handle/10336/19892
http://repository.urosario.edu.co/handle/10336/19892