Tesis sobre el tema "Financial time series"
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Yin, Jiang Ling. "Financial time series analysis". Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2492929.
Texto completoRuiz, Ortega Esther. "Heteroscedasticity in financial time series". Thesis, London School of Economics and Political Science (University of London), 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308386.
Texto completoBuonocore, Riccardo Junior. "Complexity in financial time-series". Thesis, King's College London (University of London), 2018. https://kclpure.kcl.ac.uk/portal/en/theses/complexity-in-financial-timeseries(7c54cd37-fd3a-475b-83c1-539a55b4e3f9).html.
Texto completoLee, Seonhwi. "Essays in financial time series". Thesis, University of Exeter, 2015. http://hdl.handle.net/10871/18569.
Texto completoIshida, Isao. "Essays on financial time series /". Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2004. http://wwwlib.umi.com/cr/ucsd/fullcit?p3153696.
Texto completoMercurio, Danilo. "Adaptive estimation for financial time series". Doctoral thesis, [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=972597263.
Texto completoYiu, Fu-keung y 饒富強. "Time series analysis of financial index". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B31267804.
Texto completoKaranasos, Menelaos. "Essays on financial time series models". Thesis, Birkbeck (University of London), 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286252.
Texto completoDunne, Peter Gerard. "Essays in financial time-series analysis". Thesis, Queen's University Belfast, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.337690.
Texto completoSchwill, Stephan. "Entropy analysis of financial time series". Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/entropy-analysis-of-financial-time-series(7e0c84fe-5d0b-41bc-96c6-5e41ffa5b8fe).html.
Texto completoGartheeban, Ganeshapillai. "Learning connections in financial time series". Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/93061.
Texto completoCataloged from PDF version of thesis.
Includes bibliographical references (pages 125-132).
Much of modern financial theory is based upon the assumption that a portfolio containing a diversified set of equities can be used to control risk while achieving a good rate of return. The basic idea is to choose equities that have high expected returns, but are unlikely to move together. Identifying a portfolio of equities that remain well diversified over a future investment period is difficult. In our work, we investigate how to use machine learning techniques and data mining to learn cross-sectional patterns that can be used to design diversified portfolios. Specifically, we model the connections among equities from different perspectives, and propose three different methods that capture the connections in different time scales. Using the "correlation" structure learned using our models, we show how to build selective but well-diversified portfolios. We show that these portfolios perform well on out of sample data in terms of minimizing risk and achieving high returns. We provide a method to address the shortcomings of correlation in capturing events such as large losses (tail risk). Portfolios constructed using our method significantly reduce tail risk without sacrificing overall returns. We show that our method reduces the worst day performance from -15% to -9% and increases the Sharpe ratio from 0.63 to 0.71. We also provide a method to model the relationship between the equity return that is unexplained by the market return (excess return) and the amount of sentiment in news releases that hasn't been already reflected in the price of equities (excess sentiment). We show that a portfolio built using this method generates an annualized return of 34% over a 10-year time period. In comparison, the S&P 500 index generated 5% return in the same time period.
by Gartheeban Ganeshapillai.
Ph. D.
Mashikian, Paul Stephan. "Multiresolution models of financial time series". Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/43483.
Texto completoIncludes bibliographical references (leaves 89-92).
by Paul Stephan Mashikian.
M.Eng.
Correia, Maria Inês Costa. "Cluster analysis of financial time series". Master's thesis, Instituto Superior de Economia e Gestão, 2020. http://hdl.handle.net/10400.5/21016.
Texto completoEsta dissertação aplica o método da Signature como medida de similaridade entre dois objetos de séries temporais usando as propriedades de ordem 2 da Signature e aplicando-as a um método de Clustering Asimétrico. O método é comparado com uma abordagem de Clustering mais tradicional, onde a similaridade é medida usando Dynamic Time Warping, desenvolvido para trabalhar com séries temporais. O intuito é considerar a abordagem tradicional como benchmark e compará-la ao método da Signature através do tempo de computação, desempenho e algumas aplicações. Estes métodos são aplicados num conjunto de dados de séries temporais financeiras de Fundos Mútuos do Luxemburgo. Após a revisão da literatura, apresentamos o método Dynamic Time Warping e o método da Signature. Prossegue-se com a explicação das abordagens de Clustering Tradicional, nomeadamente k-Means, e Clustering Espectral Assimétrico, nomeadamente k-Axes, desenvolvido por Atev (2011). O último capítulo é dedicado à Investigação Prática onde os métodos anteriores são aplicados ao conjunto de dados. Os resultados confirmam que o método da Signature têm efectivamente potencial para Machine Learning e previsão, como sugerido por Levin, Lyons and Ni (2013).
This thesis applies the Signature method as a measurement of similarities between two time-series objects, using the Signature properties of order 2, and its application to Asymmetric Spectral Clustering. The method is compared with a more Traditional Clustering approach where similarities are measured using Dynamic Time Warping, developed to work with time-series data. The intention for this is to consider the traditional approach as a benchmark and compare it to the Signature method through computation times, performance, and applications. These methods are applied to a financial time series data set of Mutual Exchange Funds from Luxembourg. After the literature review, we introduce the Dynamic Time Warping method and the Signature method. We continue with the explanation of Traditional Clustering approaches, namely k-Means, and Asymmetric Clustering techniques, namely the k-Axes algorithm, developed by Atev (2011). The last chapter is dedicated to Practical Research where the previous methods are applied to the data set. Results confirm that the Signature method has indeed potential for machine learning and prediction, as suggested by Levin, Lyons, and Ni (2013).
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Khalfaoui, Rabeh. "Wavelet analysis of financial time series". Thesis, Aix-Marseille, 2012. http://www.theses.fr/2012AIXM1083.
Texto completoThis thesis deals with the contribution of wavelet methods on modeling economic and financial time series and consists of two parts: the univariate time series and multivariate time series. In the first part (chapters 2 and 3), we adopt univariate case. First, we examine the class of non-stationary long memory processes. A simulation study is carried out in order to compare the performance of some semi-parametric estimation methods for fractional differencing parameter. We also examine the long memory in volatility using FIGARCH models to model energy data. Results show that the Exact local Whittle estimation method of Shimotsu and Phillips [2005] is the better one and the oil volatility exhibit strong evidence of long memory. Next, we analyze the market risk of univariate stock market returns which is measured by systematic risk (beta) at different time horizons. Results show that beta is not stable, due to multi-trading strategies of investors. Results based on VaR analysis show that risk is more concentrated at higher frequency. The second part (chapters 4 and 5) deals with estimation of the conditional variance and correlation of multivariate time series. We consider two classes of time series: the stationary time series (returns) and the non-stationary time series (levels). We develop a novel approach, which combines wavelet multi-resolution analysis and multivariate GARCH models, i.e. the wavelet-based multivariate GARCH approach. However, to evaluate the volatility forecasts we compare the performance of several multivariate models using some criteria, such as loss functions, VaR estimation and hedging strategies
Yiu, Fu-keung. "Time series analysis of financial index /". Hong Kong : University of Hong Kong, 1996. http://sunzi.lib.hku.hk/hkuto/record.jsp?B18003047.
Texto completoHaas, Markus. "Dynamic mixture models for financial time series /". Berlin : Pro Business, 2004. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=012999049&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
Texto completoTan, Victor Khoon-Lee. "Integrating modelling techniques for financial time series". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0005/NQ43275.pdf.
Texto completoBatres-Estrada, Bilberto. "Deep learning for multivariate financial time series". Thesis, KTH, Matematisk statistik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-168751.
Texto completoLondon, Mark Daniel. "Complexity and criticality in financial time series". Thesis, De Montfort University, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.434034.
Texto completoHong, Seok Young. "Nonparametric methods in financial time series analysis". Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/283218.
Texto completoQu, Haizhou. "Financial forecasting using time series and news". Thesis, University of York, 2018. http://etheses.whiterose.ac.uk/22508/.
Texto completoMiao, Robin. "Nonlinear time series analysis in financial applications". Thesis, University of Bath, 2012. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558857.
Texto completoSedman, Robin. "Online Outlier Detection in Financial Time Series". Thesis, KTH, Matematisk statistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-228069.
Texto completoI detta examensarbete undersöks olika modeller för outlierdetektering i finansiella tidsserier. De finansiella tidsserierna är prisserier som indexpriser eller tillgångspriser. Outliers är i detta examensarbete definierade som extrema och falska punkter, men denna definition undersöks och revideras också. Två olika tidsseriemodeller undersöks: en autoregressiv (AR) och en generel au-toregressiv betingad heteroskedasticitet1 (GARCH) tidsseriemodell, samt en hypotesprövning2 baserad på GARCH-modellen. Dessutom undersöks en icke-parametrisk modell, vilken använder sig utav uppskattning av täthetsfunktionen med hjälp av kärnfunktioner3 för att detektera out-liers. Modellerna utvärderas utifrån hur väl de upptäcker outliers, hur ofta de kategoriserar icke-outliers som outliers samt modellens körtid. Det är konstaterat att alla modeller ungefär presterar lika bra, baserat på den data som används och de simuleringar som gjorts, i form av hur väl outliers är detekterade, förutom metoden baserad på hypotesprövning som fungerar sämre än de andra. Vidare är det uppenbart att definitionen av en outlier är väldigt avgörande för hur bra en modell detekterar outliers. För tillämpningen av detta examensarbete, så är körtid en viktig faktor, och med detta i åtanke är en autoregressiv modell med Students t-brusfördelning funnen att vara den bästa modellen, både med avseende på hur väl den detekterar outliers, felaktigt detekterar inliers som outliers och modellens körtid.
Hu, Wei Long. "Candlestick pattern classification in financial time series". Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3950658.
Texto completoJarjour, Riad. "Clustering financial time series for volatility modeling". Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6439.
Texto completoBovina, Dario. "Scaling and modelization of financial time series". Doctoral thesis, Università degli studi di Padova, 2009. http://hdl.handle.net/11577/3426464.
Texto completoQuesta tesi è dedicata allo studio dell'invarianza di scala delle serie temporali finanziarie. Il nostro risultato principale è che il multiscaling osservato sinora in molte serie temporali finanziarie potrebbe essere solo un effetto spurio. Abbiamo dimostrato che un meccanismo probabilistico può influire sul calcolo dell'esponente di Hurst e portare ad un multiscaling fittizio anche per serie temporali provenienti da processi strettamente simple scaling. Tale meccanismo interviene nell'analisi statistica di una singola serie empirica ed è basato sulle code a potenza della PDF dei ritorni. Siccome questo effetto si deve solamente alla presenza di eventi estremi che seguono una legge a potenza e alla disponibilità di un'unica serie temporale, non possiamo escludere che il nostro risultato abbia rilevanza in campi della fisica dei sistemi complessi diversi dalla finanza.
Mroz, Magda [Verfasser]. "Time-varying copula models for financial time series / Magda Mroz". Ulm : Universität Ulm. Fakultät für Mathematik und Wirtschaftswissenschaften, 2012. http://d-nb.info/1027341578/34.
Texto completoCattivelli, Luca. "Econometric techniques for forecasting financial time series in discrete time". Doctoral thesis, Scuola Normale Superiore, 2019. http://hdl.handle.net/11384/85721.
Texto completoAlhnaity, Bashar. "Financial engineering modelling using computational intelligent techniques : financial time series prediction". Thesis, Brunel University, 2015. http://bura.brunel.ac.uk/handle/2438/13652.
Texto completoJenkins, James D. "Financial ratio time series models in defense industries". Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1994. http://handle.dtic.mil/100.2/ADA293744.
Texto completoWang, Fangfang Ghysels Eric. "Statistical analysis of some financial time series models". Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2009. http://dc.lib.unc.edu/u?/etd,2918.
Texto completoTitle from electronic title page (viewed Jun. 23, 2010). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Statistics and Operations Research Statistics." Discipline: Statistics and Operations Research; Department/School: Statistics and Operations Research.
Åsbrink, Stefan E. "Nonlinearities and regime shifts in financial time series". Doctoral thesis, Handelshögskolan i Stockholm, Ekonomisk Statistik (ES), 1997. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-866.
Texto completoDiss. Stockholm : Handelshögsk.
Sawaya, Antonio. "Financial time series analysis : Chaos and neurodynamics approach". Thesis, Högskolan Dalarna, Datateknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:du-4810.
Texto completoCoroneo, Laura. "Essays on modelling and forecasting financial time series". Doctoral thesis, Universite Libre de Bruxelles, 2009. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210284.
Texto completoThe first chapter investigates the distribution of high frequency financial returns, with special emphasis on the intraday seasonality. Using quantile regression, I show the expansions and shrinks of the probability law through the day for three years of 15 minutes sampled stock returns. Returns are more dispersed and less concentrated around the median at the hours near the opening and closing. I provide intraday value at risk assessments and I show how it adapts to changes of dispersion over the day. The tests performed on the out-of-sample forecasts of the value at risk show that the model is able to provide good risk assessments and to outperform standard Gaussian and Student’s t GARCH models.
The second chapter shows that macroeconomic indicators are helpful in forecasting the yield curve. I incorporate a large number of macroeconomic predictors within the Nelson and Siegel (1987) model for the yield curve, which can be cast in a common factor model representation. Rather than including macroeconomic variables as additional factors, I use them to extract the Nelson and Siegel factors. Estimation is performed by EM algorithm and Kalman filter using a data set composed by 17 yields and 118 macro variables. Results show that incorporating large macroeconomic information improves the accuracy of out-of-sample yield forecasts at medium and long horizons.
The third chapter statistically tests whether the Nelson and Siegel (1987) yield curve model is arbitrage-free. Theoretically, the Nelson-Siegel model does not ensure the absence of arbitrage opportunities. Still, central banks and public wealth managers rely heavily on it. Using a non-parametric resampling technique and zero-coupon yield curve data from the US market, I find that the no-arbitrage parameters are not statistically different from those obtained from the Nelson and Siegel model, at a 95 percent confidence level. I therefore conclude that the Nelson and Siegel yield curve model is compatible with arbitrage-freeness.
Doctorat en Sciences économiques et de gestion
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Zeng, Zhanggui. "Financial Time Series Analysis using Pattern Recognition Methods". University of Sydney, 2008. http://hdl.handle.net/2123/3558.
Texto completoThis thesis is based on research on financial time series analysis using pattern recognition methods. The first part of this research focuses on univariate time series analysis using different pattern recognition methods. First, probabilities of basic patterns are used to represent the features of a section of time series. This feature can remove noise from the time series by statistical probability. It is experimentally proven that this feature is successful for pattern repeated time series. Second, a multiscale Gaussian gravity as a pattern relationship measurement which can describe the direction of the pattern relationship is introduced to pattern clustering. By searching for the Gaussian-gravity-guided nearest neighbour of each pattern, this clustering method can easily determine the boundaries of the clusters. Third, a method that unsupervised pattern classification can be transformed into multiscale supervised pattern classification by multiscale supervisory time series or multiscale filtered time series is presented. The second part of this research focuses on multivariate time series analysis using pattern recognition. A systematic method is proposed to find the independent variables of a group of share prices by time series clustering, principal component analysis, independent component analysis, and object recognition. The number of dependent variables is reduced and the multivariate time series analysis is simplified by time series clustering and principal component analysis. Independent component analysis aims to find the ideal independent variables of the group of shares. Object recognition is expected to recognize those independent variables which are similar to the independent components. This method provides a new clue to understanding the stock market and to modelling a large time series database.
Laurent, Sébastien. "Asymmetry and fat-tails in financial time series". [Maastricht : Maastricht : Universiteit Maastricht] ; University Library, Maastricht University [Host], 2002. http://arno.unimaas.nl/show.cgi?fid=5991.
Texto completoOp omslag: Faculty of Economics and Business Administration, Department of Quantitative Economics, Universiteit Maastricht. Met lit. opg. - Met samenvatting in het Nederlands.
Kwok, Sai-man Simon. "Statistical inference of some financial time series models". Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B36885654.
Texto completoÅsbrink, Stefan E. "Nonlinearities and regime shifts in financial time series /". Stockholm : Economic Research Institute, Stockholm School of Economics [Ekonomiska forskningsinstitutet vid Handelshögsk.] (EFI), 1997. http://www.hhs.se/efi/summary/439.htm.
Texto completoAvaritsioti, Eleni. "Financial time series prediction in the wavelet domain". Thesis, Imperial College London, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.502386.
Texto completoKwok, Sai-man Simon y 郭世民. "Statistical inference of some financial time series models". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B36885654.
Texto completoBu, Ruijun. "Essays in financial econometrics and time series analysis". Thesis, University of Liverpool, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.433051.
Texto completoMansur, Mohaimen. "Essays on forecasting financial and economic time series". Thesis, Queen Mary, University of London, 2014. http://qmro.qmul.ac.uk/xmlui/handle/123456789/8576.
Texto completoPapailias, Fotis. "Essays in economic and financial time series analysis". Thesis, Queen Mary, University of London, 2012. http://qmro.qmul.ac.uk/xmlui/handle/123456789/3351.
Texto completoXu, Wen. "Essays on time series econometrics and financial econometrics". Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:995e52eb-d0a9-410c-9877-c09d0ed092e0.
Texto completoKapp, Konrad Phillip. "Optimal cycle dating of large financial time series". Thesis, Nelson Mandela Metropolitan University, 2017. http://hdl.handle.net/10948/17767.
Texto completoShah, Nauman. "Statistical dynamical models of multivariate financial time series". Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:428015e6-8a52-404e-9934-0545c80da4e1.
Texto completoLesch, Ragnar H. "Modelling nonlinear stochastic dynamics in financial time series". Thesis, Aston University, 2000. http://publications.aston.ac.uk/13260/.
Texto completoLi, Bin. "Forecasting financial time series using linear predictive filters". Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/11176.
Texto completoHeinen, Andreas. "Modelling time series counts data in financial microstructure /". Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2004. http://wwwlib.umi.com/cr/ucsd/fullcit?p3130202.
Texto completoZeng, Zhanggui. "Financial Time Series Analysis using Pattern Recognition Methods". Thesis, The University of Sydney, 2006. http://hdl.handle.net/2123/3558.
Texto completo