Literatura académica sobre el tema "Stochastic Volatility"
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Artículos de revistas sobre el tema "Stochastic Volatility"
Blanco, Belen. "Capturing the volatility smile: parametric volatility models versus stochastic volatility models". Public and Municipal Finance 5, n.º 4 (26 de diciembre de 2016): 15–22. http://dx.doi.org/10.21511/pmf.05(4).2016.02.
Texto completoSABANIS, SOTIRIOS. "STOCHASTIC VOLATILITY". International Journal of Theoretical and Applied Finance 05, n.º 05 (agosto de 2002): 515–30. http://dx.doi.org/10.1142/s021902490200150x.
Texto completoAlghalith, Moawia, Christos Floros y Konstantinos Gkillas. "Estimating Stochastic Volatility under the Assumption of Stochastic Volatility of Volatility". Risks 8, n.º 2 (11 de abril de 2020): 35. http://dx.doi.org/10.3390/risks8020035.
Texto completoVeraart, Almut E. D. y Luitgard A. M. Veraart. "Stochastic volatility and stochastic leverage". Annals of Finance 8, n.º 2-3 (21 de mayo de 2010): 205–33. http://dx.doi.org/10.1007/s10436-010-0157-3.
Texto completoGuyon, Julien. "Stochastic Volatility Modeling". Quantitative Finance 17, n.º 6 (18 de abril de 2017): 825–28. http://dx.doi.org/10.1080/14697688.2017.1309181.
Texto completoBandi, Federico M. y Roberto Renò. "NONPARAMETRIC STOCHASTIC VOLATILITY". Econometric Theory 34, n.º 6 (3 de julio de 2018): 1207–55. http://dx.doi.org/10.1017/s0266466617000457.
Texto completoCapobianco, E. "Stochastic Volatility Systems". International Journal of Modelling and Simulation 17, n.º 2 (enero de 1997): 137–42. http://dx.doi.org/10.1080/02286203.1997.11760322.
Texto completoIlinski, Kirill y Oleg Soloviev. "Stochastic volatility membrane". Wilmott 2004, n.º 3 (mayo de 2004): 74–81. http://dx.doi.org/10.1002/wilm.42820040317.
Texto completoMahatma, Yudi y Ibnu Hadi. "Stochastic Volatility Estimation of Stock Prices using the Ensemble Kalman Filter". InPrime: Indonesian Journal of Pure and Applied Mathematics 3, n.º 2 (10 de noviembre de 2021): 136–43. http://dx.doi.org/10.15408/inprime.v3i2.20256.
Texto completoSun, Ya, Meiyi Wang y Hua Xie. "Volatility analysis of the flight block time based on the stochastic volatility model". Journal of Physics: Conference Series 2489, n.º 1 (1 de mayo de 2023): 012002. http://dx.doi.org/10.1088/1742-6596/2489/1/012002.
Texto completoTesis sobre el tema "Stochastic Volatility"
Andersson, Kristina. "Stochastic Volatility". Thesis, Uppsala University, Department of Mathematics, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-121722.
Texto completoGaliotos, Vassilis. "Stochastic Volatility and the Volatility Smile". Thesis, Uppsala University, Department of Mathematics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-120151.
Texto completoLe, Truc. "Stochastic volatility models". Monash University, School of Mathematical Sciences, 2005. http://arrow.monash.edu.au/hdl/1959.1/5181.
Texto completoZeytun, Serkan. "Stochastic Volatility, A New Approach For Vasicek Model With Stochastic Volatility". Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/3/12606561/index.pdf.
Texto completoCap, Thi Diu. "Implied volatility with HJM–type Stochastic Volatility model". Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-54938.
Texto completoJacquier, Antoine. "Implied volatility asymptotics under affine stochastic volatility models". Thesis, Imperial College London, 2010. http://hdl.handle.net/10044/1/6142.
Texto completoOzkan, Pelin. "Analysis Of Stochastic And Non-stochastic Volatility Models". Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/3/12605421/index.pdf.
Texto completoVavruška, Marek. "Realised stochastic volatility in practice". Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-165381.
Texto completoHrbek, Filip. "Metody předvídání volatility". Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-264689.
Texto completoLopes, Moreira de Veiga Maria Helena. "Modelling and forecasting stochastic volatility". Doctoral thesis, Universitat Autònoma de Barcelona, 2004. http://hdl.handle.net/10803/4046.
Texto completoEn mi primer capítulo, intento modelar las principales características de las series financieras, como a persistencia y curtosis. Los modelos de volatilidad estocástica estimados son extensiones directas de los modelos de Gallant y Tauchen (2001), donde incluyo un elemento de retro-alimentación. Este elemento es de extrema importancia porque permite captar el hecho de que períodos de alta volatilidad están, en general, seguidos de periodos de gran volatilidad y viceversa. En este capítulo, como en toda la tesis, uso el método de estimación eficiente de momentos de Gallant y Tauchen (1996). De la estimación surgen dos modelos posibles de describir los datos, el modelo logarítmico con factor de volatilidad y retroalimentación y el modelo logarítmico con dos factores de volatilidad. Como no es posible elegir entre ellos basados en los tests efectuados en la fase de la estimación, tendremos que usar el método de reprogección para obtener mas herramientas de comparación. El modelo con un factor de volatilidad se comporta muy bien y es capaz de captar la "quiebra" de los mercados financieros de 1987.
En el segundo capítulo, hago la evaluación del modelo con dos factores de volatilidad en términos de predicción y comparo esa predicción con las obtenidas con los modelos GARCH y ARFIMA. La evaluación de la predicción para los tres modelos es hecha con la ayuda del R2 de las regresiones individuales de la volatilidad "realizada" en una constante y en las predicciones. Los resultados empíricos indican un mejor comportamiento del modelo en tiempo continuo. Es más, los modelos GARCH y ARFIMA parecen tener problemas en seguir la marcha de la volatilidad "realizada".
Finalmente, en el tercer capítulo hago una extensión del modelo de volatilidad estocástica de memoria larga de Harvey (2003). O sea, introduzco un factor de volatilidad de corto plazo. Este factor extra aumenta la curtosis y ayuda a captar la persistencia (que es captada con un proceso integrado fraccional, como en Harvey (1993)). Los resultados son probados y el modelo implementado empíricamente.
The purpose of my thesis is to model and forecast the volatility of the financial series of returns by using both continuous and discrete time stochastic volatility models.
In my first chapter I try to fit the main characteristics of the financial series of returns such as: volatility persistence, volatility clustering and fat tails of the distribution of the returns.The estimated logarithmic stochastic volatility models are direct extensions of the Gallant and Tauchen's (2001) by including the feedback feature. This feature is of extreme importance because it allows to capture the low variability of the volatility factor when the factor is itself low (volatility clustering) and it also captures the increase in volatility persistence that occurs when there is an apparent change in the pattern of volatility at the very end of the sample. In this chapter, as well as in all the thesis, I use Efficient Method of Moments of Gallant and Tauchen (1996) as an estimation method. From the estimation step, two models come out, the logarithmic model with one factor of volatility and feedback (L1F) and the logarithmic model with two factors of volatility (L2). Since it is not possible to choose between them based on the diagnostics computed at the estimation step, I use the reprojection step to obtain more tools for comparing models. The L1F is able to reproject volatility quite well without even missing the crash of 1987.
In the second chapter I fit the continuous time model with two factors of volatility of Gallant and Tauchen (2001) for the return of a Microsoft share. The aim of this chapter is to evaluate the volatility forecasting performance of the continuous time stochastic volatility model comparatively to the ones obtained with the traditional GARCH and ARFIMA models. In order to inquire into this, I estimate using the Efficient Method of Moments (EMM) of Gallant and Tauchen (1996) a continuous time stochastic volatility model for the logarithm of asset price and I filter the underlying volatility using the reprojection technique of Gallant and Tauchen (1998). Under the assumption that the model is correctly specified, I obtain a consistent estimator of the integrated volatility by fitting a continuous time stochastic volatility model to the data. The forecasting evaluation for the three estimated models is going to be done with the help of the R2 of the individual regressions of realized volatility on the volatility forecasts obtained from the estimated models. The empirical results indicate the better performance of the continuous time model in the out-of-sample periods compared to the ones of the traditional GARCH and ARFIMA models. Further, these two last models show difficulties in tracking the growth pattern of the realized volatility. This probably is due to the change of pattern in volatility in this last part of the sample.
Finally, in the third chapter I come back to the model specification and I extend the long memory stochastic volatility model of Harvey (1993) by introducing a short run volatility factor. This extra factor increases kurtosis and helps the model capturing volatility persistence (that it is captured by a fractionally integrated process as in Harvey (1993) ). Futhermore, considering some restrictions of the parameters it is possible to fit the empirical fact of small first order autocorrelation of squared returns. All these results are proved theoretically and the model is implemented empirically using the S&P 500 composite index returns. The empirical results show the superiority of the model in fitting the main empirical facts of the financial series of returns.
Libros sobre el tema "Stochastic Volatility"
Takahashi, Makoto, Yasuhiro Omori y Toshiaki Watanabe. Stochastic Volatility and Realized Stochastic Volatility Models. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0935-3.
Texto completoHafner, Reinhold. Stochastic Implied Volatility. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17117-8.
Texto completoStochastic volatility modeling. Boca Raton: CRC Press, 2016.
Buscar texto completoFornari, Fabio y Antonio Mele. Stochastic Volatility in Financial Markets. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4533-0.
Texto completoHarvey, Andrew. The econometrics of stochastic volatility. London: London School of Economics Financial Markets Group, 1993.
Buscar texto completoBishwal, Jaya P. N. Parameter Estimation in Stochastic Volatility Models. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-03861-7.
Texto completoMelino, Angelo. Pricing foreign currency options with stochastic volatility. Toronto: Dept. of Economics; Institute for Policy Analysis, University of Toronto, 1988.
Buscar texto completoHafner, Reinhold. Stochastic implied volatility: A factor-based model. Berlin: Springer, 2004.
Buscar texto completoSandmann, G. Maximum likelihood estimation of stochastic volatility models. London: London School of Economics, Financial Markets Group, 1996.
Buscar texto completoAït-Sahalia, Yacine. Maximum likelihood estimation of stochastic volatility models. Cambridge, MA: National Bureau of Economic Research, 2004.
Buscar texto completoCapítulos de libros sobre el tema "Stochastic Volatility"
Chiarella, Carl, Xue-Zhong He y Christina Sklibosios Nikitopoulos. "Stochastic Volatility". En Dynamic Modeling and Econometrics in Economics and Finance, 315–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45906-5_15.
Texto completoAndersen, Torben G. y Luca Benzoni. "Stochastic Volatility". En Complex Systems in Finance and Econometrics, 694–726. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-7701-4_38.
Texto completoAndersen, Torben G. y Luca Benzoni. "Stochastic Volatility". En Encyclopedia of Complexity and Systems Science, 1–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-27737-5_527-3.
Texto completoLorig, Matthew y Ronnie Sircar. "Stochastic Volatility". En Financial Signal Processing and Machine Learning, 135–61. Chichester, UK: John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781118745540.ch7.
Texto completoPrivault, Nicolas. "Stochastic Volatility". En Introduction to Stochastic Finance with Market Examples, 249–76. 2a ed. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003298670-8.
Texto completoAndersen, Torben G. y Luca Benzoni. "Stochastic Volatility". En Encyclopedia of Complexity and Systems Science, 8783–815. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-30440-3_527.
Texto completoAusting, Peter. "Stochastic Volatility". En Smile Pricing Explained, 71–95. London: Palgrave Macmillan UK, 2014. http://dx.doi.org/10.1057/9781137335722_7.
Texto completoTakahashi, Makoto, Yasuhiro Omori y Toshiaki Watanabe. "Stochastic Volatility Model". En Stochastic Volatility and Realized Stochastic Volatility Models, 7–30. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0935-3_2.
Texto completoTakahashi, Makoto, Yasuhiro Omori y Toshiaki Watanabe. "Asymmetric Stochastic Volatility Model". En Stochastic Volatility and Realized Stochastic Volatility Models, 31–55. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0935-3_3.
Texto completoTakahashi, Makoto, Yasuhiro Omori y Toshiaki Watanabe. "Introduction". En Stochastic Volatility and Realized Stochastic Volatility Models, 1–6. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0935-3_1.
Texto completoActas de conferencias sobre el tema "Stochastic Volatility"
Tian, Yu, Zili Zhu, Fima Klebaner y Kais Hamza. "A Hybrid Stochastic Volatility Model Incorporating Local Volatility". En 2012 Fourth International Conference on Computational and Information Sciences (ICCIS). IEEE, 2012. http://dx.doi.org/10.1109/iccis.2012.20.
Texto completoGonzaga, Alex C. "Seasonal long-memory stochastic volatility". En 11TH INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2013: ICNAAM 2013. AIP, 2013. http://dx.doi.org/10.1063/1.4826027.
Texto completoSimandl, Miroslav y Tomas Soukup. "Gibbs sampler to stochastic volatility models". En 2001 European Control Conference (ECC). IEEE, 2001. http://dx.doi.org/10.23919/ecc.2001.7076061.
Texto completoHsu, Ai-Chi, Hsiao-Fen Hsiao y Shih-Jui Yang. "A Grey-Artificial Neural Network Stochastic Volatility Model for Return Volatility". En 2009 International Conference on Management and Service Science (MASS). IEEE, 2009. http://dx.doi.org/10.1109/icmss.2009.5301917.
Texto completoKanniainen, Juho. "Cause of Stock Return Stochastic Volatility: Query by Way of Stochastic Calculus". En Recent Advances in Stochastic Modeling and Data Analysis. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709691_0003.
Texto completoYu, Jun y Zhenlin Yang. "A class of nonlinear stochastic volatility models". En 9th Joint Conference on Information Sciences. Paris, France: Atlantis Press, 2006. http://dx.doi.org/10.2991/jcis.2006.87.
Texto completoFigà-Talamanca, Gianna y Maria Letizia Guerra. "Fuzzy Option Value with Stochastic Volatility Models". En 2009 Ninth International Conference on Intelligent Systems Design and Applications. IEEE, 2009. http://dx.doi.org/10.1109/isda.2009.243.
Texto completoSun, You-fa, Cheng-ke Zhang y Jing-guang Gao. "Feedback and stochastic volatility stock pricing model". En 2008 International Conference on Management Science and Engineering (ICMSE). IEEE, 2008. http://dx.doi.org/10.1109/icmse.2008.4669069.
Texto completoDu, Jun y Yang Liu. "Credit Risk Pricing with Multivariate Stochastic Volatility". En 2009 International Joint Conference on Computational Sciences and Optimization, CSO. IEEE, 2009. http://dx.doi.org/10.1109/cso.2009.50.
Texto completoTang, J. y S. S. T. Yau. "Exotic option, stochastic volatility and incentive scheme". En COMPUTATIONAL FINANCE 2006. Southampton, UK: WIT Press, 2006. http://dx.doi.org/10.2495/cf060181.
Texto completoInformes sobre el tema "Stochastic Volatility"
Campbell, John, Stefano Giglio, Christopher Polk y Robert Turley. An Intertemporal CAPM with Stochastic Volatility. Cambridge, MA: National Bureau of Economic Research, septiembre de 2012. http://dx.doi.org/10.3386/w18411.
Texto completoAit-Sahalia, Yacine y Robert Kimmel. Maximum Likelihood Estimation of Stochastic Volatility Models. Cambridge, MA: National Bureau of Economic Research, junio de 2004. http://dx.doi.org/10.3386/w10579.
Texto completoFernandez-Villaverde, Jesus, Pablo Guerrón-Quintana y Juan Rubio-Ramírez. Estimating Dynamic Equilibrium Models with Stochastic Volatility. Cambridge, MA: National Bureau of Economic Research, septiembre de 2012. http://dx.doi.org/10.3386/w18399.
Texto completoMulligan, Casey. Robust Aggregate Implications of Stochastic Discount Factor Volatility. Cambridge, MA: National Bureau of Economic Research, enero de 2004. http://dx.doi.org/10.3386/w10210.
Texto completoKristensen, Dennis y Shin Kanaya. Estimation of stochastic volatility models by nonparametric filtering. Institute for Fiscal Studies, marzo de 2015. http://dx.doi.org/10.1920/wp.cem.2015.0915.
Texto completoTrolle, Anders y Eduardo Schwartz. Unspanned Stochastic Volatility and the Pricing of Commodity Derivatives. Cambridge, MA: National Bureau of Economic Research, diciembre de 2006. http://dx.doi.org/10.3386/w12744.
Texto completoAlizadeh, Sassan, Michael Brandt y Francis Diebold. High- and Low-Frequency Exchange Rate Volatility Dynamics: Range-Based Estimation of Stochastic Volatility Models. Cambridge, MA: National Bureau of Economic Research, marzo de 2001. http://dx.doi.org/10.3386/w8162.
Texto completoDiebold, Francis, Frank Schorfheide y Minchul Shin. Real-Time Forecast Evaluation of DSGE Models with Stochastic Volatility. Cambridge, MA: National Bureau of Economic Research, septiembre de 2016. http://dx.doi.org/10.3386/w22615.
Texto completoBaldivieso, Sebastian. Sensitivity Diagnostics and Adaptive Tuning of the Multivariate Stochastic Volatility Model. Portland State University Library, febrero de 2020. http://dx.doi.org/10.15760/etd.7296.
Texto completoChacko, George y Luis Viceira. Dynamic Consumption and Portfolio Choice with Stochastic Volatility in Incomplete Markets. Cambridge, MA: National Bureau of Economic Research, octubre de 1999. http://dx.doi.org/10.3386/w7377.
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