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Dissertations / Theses on the topic 'Financial time series'

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Published: 4 June 2021
Last updated: 25 July 2025

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1

Yin, Jiang Ling. "Financial time series analysis." Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2492929.

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2

Ruiz, 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.

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This thesis deals with two different topics, both related to modelling time-varying variances in high frequency financial time series. The first topic concerns the estimation of unobserved component models with autoregressive conditional heteroscedastic (ARCH) effects. The second topic concerns the quasi-maximum likelihood estimation of stochastic variance processes. These are an alternative to ARCH processes for modelling conditionally heteroscedastic time series. The motivation of the work is based on the increasing interest in the financial area in modelling volatility. In financial markets
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3

Buonocore, 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.

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Many aspects contribute to make financial markets one of the most challenging system to understand. The aim of this thesis is to study some aspects of their complexity by focusing on univariate e multivariate properties of log-returns time-series, namely multifractality and cross-dependence. In this thesis, we started by performing a thorough analysis of the scaling properties of synthetic time-series with different known scaling properties. This enabled us to do two things: find the presence of a strong bias in the estimation of the scaling exponents, and interpret measurement on real data wh
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4

Lee, Seonhwi. "Essays in financial time series." Thesis, University of Exeter, 2015. http://hdl.handle.net/10871/18569.

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This thesis consists of three essays on topics in financial time series with particular emphases on specification testing, structural breaks and long memory. The first essay develops an asymptotically valid specification testing framework for the Realised GARCH model of Hansen et al. (2012). The misspecification tests account for the joint dependence between return and the realised measure of volatility and thus extend the existing literature for testing the adequacy of GARCH models. The testing procedure is constructed based on the conditional moment principle and the first-order asymptotic t
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5

Ishida, 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.

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6

Mercurio, Danilo. "Adaptive estimation for financial time series." Doctoral thesis, [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=972597263.

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7

Yiu, Fu-keung, and 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.

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8

Karanasos, Menelaos. "Essays on financial time series models." Thesis, Birkbeck (University of London), 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286252.

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9

Dunne, 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.

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10

Schwill, 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.

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This thesis applies entropy as a model independent measure to address research questions concerning the dynamics of various financial time series. The thesis consists of three main studies as presented in chapters 3, 4 and 5. Chapters 3 and 4 apply an entropy measure to conduct a bivariate analysis of drawdowns and drawups in foreign exchange rates. Chapter 5 investigates the dynamics of investment strategies of hedge funds using entropy of realised volatility in a conditioning model. In all three studies, methods from information theory are applied in novel ways to financial time series. As I
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11

Gartheeban, Ganeshapillai. "Learning connections in financial time series." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/93061.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 125-132).<br>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
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12

Mashikian, Paul Stephan. "Multiresolution models of financial time series." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/43483.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.<br>Includes bibliographical references (leaves 89-92).<br>by Paul Stephan Mashikian.<br>M.Eng.
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13

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.

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Mestrado em Mathematical Finance<br>Esta 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 mét
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14

Khalfaoui, Rabeh. "Wavelet analysis of financial time series." Thesis, Aix-Marseille, 2012. http://www.theses.fr/2012AIXM1083.

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Cette thèse traite la contribution des méthodes d'ondelettes sur la modélisation des séries temporelles économiques et financières et se compose de deux parties: une partie univariée et une partie multivariée. Dans la première partie (chapitres 2 et 3), nous adoptons le cas univarié. Premièrement, nous examinons la classe des processus longue mémoire non-stationnaires. Une étude de simulation a été effectuée afin de comparer la performance de certaines méthodes d'estimation semi-paramétrique du paramètre d'intégration fractionnaire. Nous examinons aussi la mémoire longue dans la volatilité en
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15

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.

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16

Haas, 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.

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17

Tan, 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.

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18

Batres-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.

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Deep learning is a framework for training and modelling neural networks which recently have surpassed all conventional methods in many learning tasks, prominently image and voice recognition. This thesis uses deep learning algorithms to forecast financial data. The deep learning framework is used to train a neural network. The deep neural network is a Deep Belief Network (DBN) coupled to a Multilayer Perceptron (MLP). It is used to choose stocks to form portfolios. The portfolios have better returns than the median of the stocks forming the list. The stocks forming the S&amp;P 500 are included
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19

London, 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.

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20

Hong, Seok Young. "Nonparametric methods in financial time series analysis." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/283218.

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The fundamental objective of the analysis of financial time series is to unveil the random mechanism, i.e. the probability law, underlying financial data. The effort to identify the truth that governs the observations involves proposing and estimating reasonable statistical models that well explain the empirical features of data. This thesis develops some new nonparametric tools that can be exploited in this context; the efficacy and validity of their use are supported by computational advancements and surging availability of large/complex (`big') data sets. Chapter 1 investigates the conditio
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21

Qu, Haizhou. "Financial forecasting using time series and news." Thesis, University of York, 2018. http://etheses.whiterose.ac.uk/22508/.

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This thesis focuses on the field of financial forecasting. Most studies that use the financial news as an input in the prediction process, take it for granted that news has an effect on financial markets. The starting point for this research is the need to question this assumption, and if confirmed, to attempt to quantify it. Therefore, the first study investigates the correlation between news and stock performance based on a dataset covering both trading data and news of 25 companies. We propose a novel framework to quantify the relationship based on two matrices of pairwise distances between
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22

Miao, Robin. "Nonlinear time series analysis in financial applications." Thesis, University of Bath, 2012. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558857.

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The purpose of this thesis is to examine the nonlinear relationships between financial (and economic) variables within the field of financial econometrics. The thesis comprises two reviews of literatures, one on nonlinear time series models andthe other one on term structure of interest rates, and four empirical essays on financialapplications using nonlinear modelling techniques. The first empirical essay compares different model specifications of a Markov switching CIR model on the term structure of UK interest rates. We find the least restricted model provides the best in-sample estimation
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23

Sedman, 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.

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In this Master’s thesis, different models for outlier detection in financial time series are examined. The financial time series are price series such as index prices or asset prices. Outliers are, in this thesis, defined as extreme and false points, but this definition is also investigated and revised. Two different time series models are examined: an autoregressive (AR) and a generalized autoregressive conditional heteroskedastic (GARCH) time series model, as well as one test statistic method based on the GARCH model. Additionally, a nonparametric model is examined, which utilizes kernel density e
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24

Hu, Wei Long. "Candlestick pattern classification in financial time series." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3950658.

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25

Jarjour, Riad. "Clustering financial time series for volatility modeling." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6439.

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The dynamic conditional correlation (DCC) model and its variants have been widely used in modeling the volatility of multivariate time series, with applications in portfolio construction and risk management. While popular for its simplicity, the DCC uses only two parameters to model the correlation dynamics, regardless of the number of assets. The flexible dynamic conditional correlation (FDCC) model attempts to remedy this by grouping the stocks into various clusters, each with its own set of parameters. However, it assumes the grouping is known apriori. In this thesis we develop a systematic
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26

Bovina, Dario. "Scaling and modelization of financial time series." Doctoral thesis, Università degli studi di Padova, 2009. http://hdl.handle.net/11577/3426464.

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This thesis is devoted to the characterization of the invariance under rescaling of financial time series. Our main result is that the multiscaling observed until now in many financial time series could be a spurious effect. We proved that a probabilistic mechanism working in the empirical statistical analysis of a single time series and based on the power law tails of the density function of the returns, can affect the outcoming Hurst exponent and leads to a strong spurious multiscaling even for a strictly simple scaling underlying process. Since this effect is due only to the availability of
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27

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.

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28

Cattivelli, Luca. "Econometric techniques for forecasting financial time series in discrete time." Doctoral thesis, Scuola Normale Superiore, 2019. http://hdl.handle.net/11384/85721.

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This thesis is a collection of three essays on financial econometrics with a common background in ultra-high frequency modeling of market activity. In the first essay, we propose an accurate and fast-to-estimate forecasting model for discrete valued time series with long memory and seasonality.1 The modelling is achieved with an autoregressive conditional Poisson process that features seasonality and heterogeneous autoregressive components (whence the acronym SHARP: Seasonal Heterogeneous AutoRegressive Poisson). Motivated by the prominent role of the bid-ask spread as a transaction cost for tra
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29

Alhnaity, Bashar. "Financial engineering modelling using computational intelligent techniques : financial time series prediction." Thesis, Brunel University, 2015. http://bura.brunel.ac.uk/handle/2438/13652.

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Prediction of financial time series is described as one of the most challenging tasks of time series prediction, due to its characteristics and dynamic nature. In any investment activity, having an accurate prediction system will significantly benefit investors by guiding decision making, especially in trading, asset management and risk management. Thus, the attempts to build such systems have attracted the attention of practitioners in the market and also researchers for many decades. Furthermore, the purpose of this thesis is to investigate and develop a new approach to predicting financial
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30

Jenkins, 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.

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31

Wang, 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.

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Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2010.<br>Title 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.
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32

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.

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This volume contains four essays on various topics in the field of financial econometrics. All four discuss the properties of high frequency financial data and its implications on the model choice when an estimate of the capital asset return volatility is in focus. The interest lies both in characterizing "stylized facts" in such series with time series models and in predicting volatility. The first essay, entitled A Survey of Recent Papers Considering the Standard &amp; Poor 500 Composite Stock Index, presents recent empirical findings and stylized facts in the financial market from 1987 to 1
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33

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.

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This work aims at combining the Chaos theory postulates and Artificial Neural Networks classification and predictive capability, in the field of financial time series prediction. Chaos theory, provides valuable qualitative and quantitative tools to decide on the predictability of a chaotic system. Quantitative measurements based on Chaos theory, are used, to decide a-priori whether a time series, or a portion of a time series is predictable, while Chaos theory based qualitative tools are used to provide further observations and analysis on the predictability, in cases where measurements provid
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34

Coroneo, 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.

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This thesis is composed of three chapters which propose some novel approaches to model and forecast financial time series. The first chapter focuses on high frequency financial returns and proposes a quantile regression approach to model their intraday seasonality and dynamics. The second chapter deals with the problem of forecasting the yield curve including large datasets of macroeconomics information. While the last chapter addresses the issue of modelling the term structure of interest rates. <p><p>The first chapter investigates the distribution of high frequency financial returns, with sp
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35

Zeng, Zhanggui. "Financial Time Series Analysis using Pattern Recognition Methods." University of Sydney, 2008. http://hdl.handle.net/2123/3558.

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Doctor of Philosophy<br>This 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 wh
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36

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.

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Proefschrift Universiteit Maastricht.<br>Op omslag: Faculty of Economics and Business Administration, Department of Quantitative Economics, Universiteit Maastricht. Met lit. opg. - Met samenvatting in het Nederlands.
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37

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.

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38

Åsbrink, 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.

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39

Avaritsioti, 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.

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40

Kwok, Sai-man Simon, and 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.

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41

Bu, 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.

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42

Mansur, 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.

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This thesis comprises three main chapters focusing on a number of issues related to forecasting economic and nancial time series. Chapter 2 contains a detailed empirical study comparing forecast perfor- mance of a number of popular term structure models in predicting the UK yield curve. Several questions are addressed and investigated, such as whether macroeconomic information helps in forecasting yields and whether predict- ing performance of models change over time. We nd evidence of signi cant time-variation in forecast accuracy of competing models, particularly during the recent nancial cr
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Papailias, 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.

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The research presented in this thesis contributes to four areas in the Economic and Financial Time Series Analysis literature. These include the topics of (i) Selection of Long Memory Time Series Models, (ii) Bootstrapping Strongly Dependent Data, (iii) Forecasting Key Macroeconomic Variables and (iv) Portfolio Optimisation. The first part focuses on strongly dependent series. It aims to establish an asymptotically consistent information criterion for long memory processes when the long memory parameter is semi parametrically estimated. A set of Monte Carlo experiments and the analysis of mont
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44

Xu, 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.

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My DPhil thesis includes three essays on time series econometrics and financial econometrics, preceded by a brief introduction. The first essay proposes a new class of multivariate volatility models utilizing realized measures of asset volatility and covolatility extracted from high-frequency data. Dimension reduction for estimation of large covariance matrices is achieved by imposing a factor structure with time-varying conditional factor loadings. The models are applied to modeling the conditional covariance data of large U.S. financial institutions during the financial crisis, where empiric
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45

Kapp, Konrad Phillip. "Optimal cycle dating of large financial time series." Thesis, Nelson Mandela Metropolitan University, 2017. http://hdl.handle.net/10948/17767.

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The study of cycles in the context of economic time series has been active for many decades, if not centuries; however, it was only in recent decades that more formal approaches for identifying cycles have been developed. Litvine and Bismans (2015) proposed a new approach for dating cycles in financial time series, for purposes of optimising buysell strategies. In this approach, cycle dating is presented as an optimisation problem. They also introduced a method for optimising this problem, known as the hierarchical method (using full evaluation 2, or HR-FE2). However, this method may be imprac
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46

Shah, 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.

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The last few years have witnessed an exponential increase in the availability and use of financial market data, which is sampled at increasingly high frequencies. Extracting useful information about the dependency structure of a system from these multivariate data streams has numerous practical applications and can aid in improving our understanding of the driving forces in the global financial markets. These large and noisy data sets are highly non-Gaussian in nature and require the use of efficient and accurate interaction measurement approaches for their analysis in a real-time environment.
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47

Lesch, Ragnar H. "Modelling nonlinear stochastic dynamics in financial time series." Thesis, Aston University, 2000. http://publications.aston.ac.uk/13260/.

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For analysing financial time series two main opposing viewpoints exist, either capital markets are completely stochastic and therefore prices follow a random walk, or they are deterministic and consequently predictable. For each of these views a great variety of tools exist with which it can be tried to confirm the hypotheses. Unfortunately, these methods are not well suited for dealing with data characterised in part by both paradigms. This thesis investigates these two approaches in order to model the behaviour of financial time series. In the deterministic framework methods are used to char
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48

Li, Bin. "Forecasting financial time series using linear predictive filters." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/11176.

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Forecasting financial time series is regarded as one of the most challenging applications of time series prediction due to their dynamic nature. However, it is the fundamental element of most investment activities thus attracting the attention of practitioners and researchers for many decades. The purpose of this research is to investigate and develop novel methods for the prediction of financial time series considering their dynamic nature. The predictive performance of asset prices time series themselves is exploited by applying digital signal processing methods to their historical observati
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49

Heinen, 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.

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50

Zeng, Zhanggui. "Financial Time Series Analysis using Pattern Recognition Methods." Thesis, The University of Sydney, 2006. http://hdl.handle.net/2123/3558.

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This 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 dir
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