Literatura académica sobre el tema "Stochastic modelling"
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Artículos de revistas sobre el tema "Stochastic modelling"
Varetsky, Y. y Z. Hanzelka. "STOCHASTIC MODELLING OF A HYBRID RENEWABLE ENERGY SYSTEM". Tekhnichna Elektrodynamika 2016, n.º 2 (10 de marzo de 2016): 58–62. http://dx.doi.org/10.15407/techned2016.02.058.
Texto completoDobrow, Robert P. "Applied Stochastic Modelling". Technometrics 44, n.º 1 (febrero de 2002): 91. http://dx.doi.org/10.1198/tech.2002.s667.
Texto completoAlbert, Jim. "Applied Stochastic Modelling". Journal of the American Statistical Association 97, n.º 457 (marzo de 2002): 354–55. http://dx.doi.org/10.1198/jasa.2002.s448.
Texto completoAuton, Tim. "Applied Stochastic Modelling". Journal of the Royal Statistical Society: Series D (The Statistician) 52, n.º 2 (julio de 2003): 244. http://dx.doi.org/10.1111/1467-9884.t01-2-00356.
Texto completoHartley, R., M. H. A. Davies y R. B. Vintner. "Stochastic Modelling and Control." Journal of the Operational Research Society 37, n.º 9 (septiembre de 1986): 928. http://dx.doi.org/10.2307/2582813.
Texto completoBlokker, Mirjam. "Stochastic Water Demand Modelling". Water Intelligence Online 10 (2011): 9781780400853. http://dx.doi.org/10.2166/9781780400853.
Texto completoStemler, Thomas, Johannes P. Werner, Hartmut Benner y Wolfram Just. "Stochastic modelling of intermittency". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368, n.º 1910 (13 de enero de 2010): 273–84. http://dx.doi.org/10.1098/rsta.2009.0196.
Texto completoHartley, R. "Stochastic Modelling and Control". Journal of the Operational Research Society 37, n.º 9 (noviembre de 1986): 928–29. http://dx.doi.org/10.1057/jors.1986.158.
Texto completoJones, P. W. "Stochastic Modelling and Analysis". Technometrics 30, n.º 3 (agosto de 1988): 361. http://dx.doi.org/10.1080/00401706.1988.10488425.
Texto completoCui, Lirong y Haitao Liao. "Stochastic modelling with applications". IMA Journal of Management Mathematics 32, n.º 1 (7 de septiembre de 2020): 1–2. http://dx.doi.org/10.1093/imaman/dpaa018.
Texto completoTesis sobre el tema "Stochastic modelling"
Ozkan, Erhun. "Stochastic Inventory Modelling". Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/3/12612097/index.pdf.
Texto completoBaduraliya, Chaminda Hasitha. "Stochastic modelling in finance". Thesis, University of Strathclyde, 2012. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=16938.
Texto completoChen, Peng. "Modelling the Stochastic Correlation". Thesis, KTH, Matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-188501.
Texto completoI det här examensarbetet fokuserar vi främst på att studera korrelation mellan aktier. Korrelationen mellan aktier har fått allt större uppmärksamhet. Vanligtvis antas korrelation vara konstant, trots att empiriska studier antyder att den är tidsvarierande. I det här examensarbetet studerar vi egenskaper hos korrelationen mellan Wienerprocesser och inför en stokastisk korrelationsmodell. Baserat på kalibreringsmetoder av Zetocha implementerar vi kalibrering för en ny uppsättning av marknadsdata.
Lopes, 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.
Löfdahl, Björn. "Stochastic modelling in disability insurance". Licentiate thesis, KTH, Matematisk statistik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-134233.
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Currie, James. "Stochastic modelling of TCR binding". Thesis, University of Leeds, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.590430.
Texto completoTsang, Wai-yin y 曾慧賢. "Aspects of modelling stochastic volatility". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31223515.
Texto completoFerreira, Jose Antonio de Sousa Jorge. "Some contributions to stochastic modelling". Thesis, University of Sheffield, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312790.
Texto completoLuo, Yang. "Stochastic modelling in biological systems". Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610145.
Texto completoDalton, Rowan. "Modelling stochastic multi-curve basis". Master's thesis, University of Cape Town, 2017. http://hdl.handle.net/11427/27102.
Texto completoLibros sobre el tema "Stochastic modelling"
Luo, Xiaoguang. GPS Stochastic Modelling. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34836-5.
Texto completoMorgan, Byron J. T. Applied stochastic modelling. 2a ed. Boca Raton: Chapman & Hall/CRC, 2008.
Buscar texto completoDavis, M. H. A. y R. B. Vinter. Stochastic Modelling and Control. Dordrecht: Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-009-4828-0.
Texto completoGermani, Alfredo, ed. Stochastic Modelling and Filtering. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/bfb0009045.
Texto completoIndia) International Conference on Stochastic Modelling (2002 Cochin. Advances in stochastic modelling. Editado por Artalejo J. R, Krishnamoorthy A y International Workshop on Retrial Queues (4th : 2002 : Cochin, India). Neshanic Station, NJ: Notable Publications, 2002.
Buscar texto completoB, Vinter R., ed. Stochastic modelling and control. London: Chapman and Hall, 1985.
Buscar texto completoWilkinson, Darren James. Stochastic modelling for systems biology. 2a ed. Boca Raton: Taylor & Francis, 2012.
Buscar texto completoChrister, Anthony H., Shunji Osaki y Lyn C. Thomas, eds. Stochastic Modelling in Innovative Manufacturing. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-59105-1.
Texto completoAdler, Robert J., Peter Müller y Boris L. Rozovskii, eds. Stochastic Modelling in Physical Oceanography. Boston, MA: Birkhäuser Boston, 1996. http://dx.doi.org/10.1007/978-1-4612-2430-3.
Texto completoHübl, Alexander. Stochastic Modelling in Production Planning. Wiesbaden: Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-19120-7.
Texto completoCapítulos de libros sobre el tema "Stochastic modelling"
Ghosh, Anindya, Bapi Saha y Prithwiraj Mal. "Stochastic Modelling". En Textile Engineering, 411–32. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003081234-12.
Texto completoSerovajsky, Simon. "Stochastic models". En Mathematical Modelling, 339–60. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003035602-18.
Texto completoHaas, Peter J. "Modelling Power". En Stochastic Petri Nets, 111–43. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/0-387-21552-2_4.
Texto completoTeng, Long, Matthias Ehrhardt y Michael Günther. "Modelling Stochastic Correlation". En Mathematics in Industry, 113–20. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23413-7_14.
Texto completoLindenschmidt, Karl-Erich. "Stochastic Modelling Framework". En River Ice Processes and Ice Flood Forecasting, 175–228. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28679-8_8.
Texto completoLindenschmidt, Karl-Erich. "Stochastic Modelling Framework". En River Ice Processes and Ice Flood Forecasting, 195–252. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-49088-0_8.
Texto completoDavis, M. H. A. y R. B. Vinter. "Stochastic models". En Stochastic Modelling and Control, 60–99. Dordrecht: Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-009-4828-0_2.
Texto completoRenard, Philippe, Andres Alcolea y David Ginsbourger. "Stochastic versus Deterministic Approaches". En Environmental Modelling, 133–49. Chichester, UK: John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118351475.ch8.
Texto completoLuo, Xiaoguang. "Introduction". En GPS Stochastic Modelling, 1–6. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34836-5_1.
Texto completoLuo, Xiaoguang. "Mathematical Background". En GPS Stochastic Modelling, 7–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34836-5_2.
Texto completoActas de conferencias sobre el tema "Stochastic modelling"
Ney, Hermann. "Stochastic modelling". En the workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2001. http://dx.doi.org/10.3115/1118037.1118042.
Texto completoGarbaczewski, Piotr. "Stochastic modelling of nonlinear dynamical systems". En Stochastic and chaotic dynamics in the lakes. AIP, 2000. http://dx.doi.org/10.1063/1.1302402.
Texto completoTautu, Petre. "Stochastic Modelling in Biology". En Proceedings of the Workshop. WORLD SCIENTIFIC, 1990. http://dx.doi.org/10.1142/9789814540711.
Texto completoMaher, Mike. "Stochastic Modelling of Sport". En 2012 Ninth International Conference on Quantitative Evaluation of Systems (QEST). IEEE, 2012. http://dx.doi.org/10.1109/qest.2012.40.
Texto completoSeddon, Keith, Behnam Pirouz y Timothy Fitton. "Stochastic beach profile modelling". En 18th International Seminar on Paste and Thickened Tailings. Australian Centre for Geomechanics, Perth, 2015. http://dx.doi.org/10.36487/acg_rep/1504_35_seddon.
Texto completoMARIANI, L., G. TURCHETTI y F. LUCIANI. "STOCHASTIC MODELS OF IMMUNE SYSTEM AGING". En Modelling Biomedical Signals. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812778055_0007.
Texto completoWilczyński, Bartek. "A stochastic extension of R. Thomas regulatory network modelling". En Stochastic Models in Biological Sciences. Warsaw: Institute of Mathematics Polish Academy of Sciences, 2008. http://dx.doi.org/10.4064/bc80-0-19.
Texto completoCH. IVANOV, PLAMEN y CHUNG-CHUAN LO. "STOCHASTIC APPROACHES TO MODELING OF PHYSIOLOGICAL RHYTHMS". En Modelling Biomedical Signals. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812778055_0003.
Texto completo"STOCHASTIC MODELLING IN HEALTHCARE SYSTEMS". En 1st International Conference on Simulation and Modeling Methodologies, Technologies and Applications. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003576101090115.
Texto completoSmieja, Jaroslaw. "Deterministic Modeling of Stochastic Gene Transcription Processes". En Modelling, Identification and Control. Calgary,AB,Canada: ACTAPRESS, 2014. http://dx.doi.org/10.2316/p.2014.809-018.
Texto completoInformes sobre el tema "Stochastic modelling"
Yanev, Nikolay M., Vessela K. Stoimenova y Dimitar V. Atanasov. Stochastic Modelling and Estimation of COVID-19 Population Dynamics. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, mayo de 2020. http://dx.doi.org/10.7546/crabs.2020.04.02.
Texto completoNg, B. Survey of Bayesian Models for Modelling of Stochastic Temporal Processes. Office of Scientific and Technical Information (OSTI), octubre de 2006. http://dx.doi.org/10.2172/900168.
Texto completoBenoit, N., D. Marcotte, J. W. Molson, A F Bajc y H. A. J. Russell. Stochastic hydrogeological modelling workflow in a glacial sedimentary basin, southern Ontario. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2020. http://dx.doi.org/10.4095/321107.
Texto completoFleming, Wendell H. y Harold J. Kushner. Numerical Methods and Approximation and Modelling Problems in Stochastic Control Theory. Fort Belvoir, VA: Defense Technical Information Center, noviembre de 1988. http://dx.doi.org/10.21236/ada218419.
Texto completoOsadetz, K. G., Z. Chen y H. Gao. SuperSD, Version 1.0: a pool-based stochastic simulation program for modelling the spatial distribution of undiscovered petroleum resources. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2003. http://dx.doi.org/10.4095/214036.
Texto completoAnsari, S. M., E. M. Schetselaar y J. A. Craven. Three-dimensional magnetotelluric modelling of the Lalor volcanogenic massive-sulfide deposit, Manitoba. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/328003.
Texto completoPerdigão, Rui A. P. y Julia Hall. Spatiotemporal Causality and Predictability Beyond Recurrence Collapse in Complex Coevolutionary Systems. Meteoceanics, noviembre de 2020. http://dx.doi.org/10.46337/201111.
Texto completoPerdigão, Rui A. P. New Horizons of Predictability in Complex Dynamical Systems: From Fundamental Physics to Climate and Society. Meteoceanics, octubre de 2021. http://dx.doi.org/10.46337/211021.
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