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.
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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, many decisions are based on the volatility of a specific stock or index, which is closely related to the variance. Therefore, it is important to develop good statistical models able to describe time-varying variances. 2
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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 which led us to uncover the true source of the multifractal behaviour of financial log-prices, which has been long debated in the literature. We addressed the presence of the bias by proposing a method which manages to filter out its presence and we validate it by applying it to synthetic time-series with known scaling properties and on empirical ones. We also found that this bias is due to the stability under aggregation of the log-returns which, due to their long memory, are processes which for high aggregation tend to a random variable which displays an exact multifractal scaling. Finally we focused the attention on linking the scaling properties of log-returns to their cross-correlation properties within a given market finding an intriguing non-linear relationship between the two quantities.
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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 theory. Our Monte Carlo results reveal good finite sample size and power properties. In the second essay, a Monte Carlo experiment is conducted to investigate the relative out-of-sample predictive ability of a class of conditional variance models when either a structural break or long memory is allowed. Our Monte Carlo results reveal that if the true volatility process is stationary short memory and its persistence level is not too high, but is contaminated by a structural break, the presence of the structural break is of importance in choosing a proper size of estimation window in the short-run forecast. If the persistence level is very high, spurious long memory may often dominate the true structural break in the longer-run forecast. For data generation processes without any structural break, the forecasting models, which can characterise the properties of the true conditional variance process, are favourable. In the last essay, we analyse the properties of the S&P 500 stock index return volatility process using historical and realised measures of volatility. We investigate a true property of the stochastic volatility processes by means of econometric tests, which may disentangle true or spurious long memory. The realised variance and realised kernel of the US stock market return exhibit true long memory. However, the historical volatility process shows some evidence of spurious long memory. We examine relative out-of-sample performance of one-day-ahead forecasts, with emphasis on the predictive content of structural changes and long memory. A class of ARFIMA models consistently produces the best-performing forecasts compared to a class of GARCH models. Among the GARCH models, it is shown that a rolling window GARCH forecast and GARCH forecasts which account for breaks outperform the long memory-based GARCH models even with the long memory proxy process.
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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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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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Yiu, 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.
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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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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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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 Information Theory and its central concept of entropy are not widely used in the economic sciences, a methodology chapter was therefore included in chapter 2 that gives an overview on the theoretical background and statistical features of the entropy measures used in the three main studies. In the first two studies the focus is on mutual information and transfer entropy. Both measures are used to identify dependencies between two exchange rates. The chosen measures generalise, in a well defined manner, correlation and Granger causality. A different entropy measure, the approximate entropy, is used in the third study to analyse the serial structure of S&P realised volatility. The study of drawdowns and drawups has so far been concentrated on their uni- variate characteristics. Encoding the drawdown information of a time series into a time series of discrete values, Chapter 3 uses entropy measures to analyse the correlation and cross correlations of drawdowns and drawups. The method to encode the drawdown information is explained and applied to daily and hourly EUR/USD and GBP/USD exchange rates from 2001 to 2012. For the daily series, we find evidence of dependence among the largest draws (i.e. 5% and 95% quantiles), but it is not as strong as the correlation between the daily returns of the same pair of FX rates. There is also dependence between lead/lagged values of these draws. Similar and stronger findings were found among the hourly data. We further use transfer entropy to examine the spill over and lead-lag information flow between drawup/drawdown of the two exchange rates. Such information flow is indeed detectable in both daily and hourly data. The amount of information transferred is considerably higher for the hourly than the daily data. Both daily and hourly series show clear evidence of information flowing from EUR/USD to GBP/USD and, slightly stronger, in the reverse direction. Robustness tests, using effective transfer entropy, show that the information measured is not due to noise. Chapter 4 uses state space models of volatility to investigate volatility spill overs between exchange rates. Our use of entropy related measures in the investigation of dependencies of two state space series is novel. A set of five daily exchange rates from emerging and developed economies against the dollar over the period 1999 to 2012 is used. We find that among the currency pairs, the co-movement of EUR/USD and CHF/USD volatility states show the strongest observed relationship. With the use of transfer entropy, we find evidence for information flows between the volatility state series of AUD, CAD and BRL.Chapter 5 uses the entropy of S&P realised volatility in detecting changes of volatility regime in order to re-examine the theme of market volatility timing of hedge funds. A one-factor model is used, conditioned on information about the entropy of market volatility, to measure the dynamic of hedge funds equity exposure. On a cross section of around 2500 hedge funds with a focus on the US equity markets we find that, over the period from 2000 to 2014, hedge funds adjust their exposure dynamically in response to changes in volatility regime. This adds to the literature on the volatility timing behaviour of hedge fund manager, but using entropy as a model independent measure of volatility regime. Finally, chapter 6 summarises and concludes with some suggestions for future research.
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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.Cataloged 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.
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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.Includes bibliographical references (leaves 89-92).
by Paul Stephan Mashikian.
M.Eng.
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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 FinanceEsta 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.
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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 utilisant des modèles FIGARCH pour les données de l'énergie. Les résultats montrent que la méthode d'estimation Exact Local Whittle de Shimotsu et Phillips [2005] est la meilleure méthode de détection de longue mémoire et la volatilité du pétrole exhibe une forte évidence de phénomène de mémoire longue. Ensuite, nous analysons le risque de marché des séries de rendements univariées de marchés boursier, qui est mesurée par le risque systématique (bêta) à différents horizons temporels. Les résultats montrent que le Bêta n'est pas stable, en raison de multi-trading stratégies des investisseurs. Les résultats basés sur l'analyse montrent que le risque mesuré par la VaR est plus concentrée aux plus hautes fréquences. La deuxième partie (chapitres 4 et 5) traite l'estimation de la variance et la corrélation conditionnelle des séries temporelles multivariées. Nous considérons deux classes de séries temporelles: les séries temporelles stationnaires (rendements) et les séries temporelles non-stationnaires (séries en niveaux)This 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
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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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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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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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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&P 500 are included in the study. The results obtained from the deep neural network are compared to benchmarks from a logistic regression network, a multilayer perceptron and a naive benchmark. The results obtained from the deep neural network are better and more stable than the benchmarks. The findings support that deep learning methods will find their way in finance due to their reliability and good performance.
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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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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 conditional first moment properties of financial returns. We propose multivariate extensions of the popular Variance Ratio (VR) statistic, aiming to test linear predictability of returns and weak-form market efficiency. We construct asymptotic distribution theories for the statistics and scalar functions thereof under the null hypothesis of no predictability. The imposed assumptions are weaker than those widely adopted in the literature, and in our view more credible with regard to the underlying data generating process we expect for stock returns. It is also shown that the limit theories can be extended to the long horizon and large dimension cases, and also to allow for a time varying risk premium. Our methods are applied to CRSP weekly returns from 1962 to 2013; the joint tests of the multivariate hypothesis reject the null at the 1% level for all horizons considered. Chapter 2 is about nonparametric estimation of conditional moments. We propose a local constant type estimator that operates with an infinite number of conditioning variables; this enables a direct estimation of many objects of econometric interest that have dependence upon the infinite past. We show pointwise and uniform consistency of the estimator and establish its asymptotic nomality in various static and dynamic regressions context. The optimal rate of estimation turns out to be of logarithmic order, and the precise rate depends on the Lambert W function, the smoothness of the regression operator and the dependence of the data in a non-trivial way. The theories are applied to investigate the intertemporal risk-return relation for the aggregate stock market. We report an overall positive risk-return relation on the S&P 500 daily data from 1950-2017, and find evidence of strong time variation and counter-cyclical behaviour in risk aversion. Lastly, Chapter 3 concerns nonparametric volatility estimation with high frequency time series. While data observed at finer time scale than daily provide rich information, their distinctive empirical properties bring new challenges in their analysis. We propose a Fourier domain based estimator for multivariate ex-post volatility that is robust to two major hurdles in high frequency finance: asynchronicity in observations and the presence of microstructure noise. Asymptotic properties are derived under some mild conditions. Simulation studies show our method outperforms time domain estimators when two assets with different liquidity are traded asynchronously.
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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 companies. The first matrix represents distances between sets of news articles, while the other represents the pairwise distances between the financial performances. The detected correlation varies with time and reaches statistically significant. The next study focuses on testing if news can be used as a proxy for future financial performance in a profitable trading strategy. The one proposed here uses our previous findings to select the stock for which news affects most strongly on financial performance. The results show that this strategy outperforms competitive baselines. Based on the proposed framework, a textual feature ranking method is proposed. This method assigns weights for textual features, and those weights are optimised to maximise the value of the relation to be quantified. A gradient descent algorithm is applied to obtain the optimal weights. There are two findings: first, named entity related words are weighted more than other words. second, optimal weights lead to a significantly better indicator for selecting winner stocks hence better profitable strategies. Lastly, the popular convolutional neural work is used to implement a novel financial forecasting approach, which uses the stock chart as input. The results show that this approach can provide effective predictions of future stock price movements.
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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 results. Although models with restrictive specifications may provide slightly better out-of-sample forecasts in directional movements of the yields, the economic gains seem to be small. In the second essay, we jointly model the nominal and real term structure of the UK interest rates using a three-factor essentially affine no-arbitrage term structure model. The model-implied expected inflation rates are then used in the subsequent analysis on its nonlinear relationship with the FTSE 100 index return premiums, utilizing a smooth transition vector autoregressive model. We find the model implied expected inflation rates remain below the actual inflation rates after the independence of the Bank of England in 1997, and the recent sharp decline of the expected inflation rates may lend support to the standing ground of the central bank for keeping interest rates low. The nonlinearity test on the relationship between the FTSE 100 index return premiums and the expected inflation rates shows that there exists a nonlinear adjustment on the impact from lagged expected inflation rates to current return premiums. The third essay provides us additional insight into the nature of the aggregate stock market volatilities and its relationship to the expected returns, in a Markov switching model framework, using centuries-long aggregate stock market data from six countries (Australia, Canada, Sweden, Switzerland, UK and US). We find that the Markov switching model assuming both regime dependent mean and volatility with a 3-regime specification is capable to captures the extreme movements of the stock market which are short-lived. The volatility feedback effect that we studied on each of these six countries shows a positive sign on anticipating a high volatility regime of the current trading month. The investigation on the coherence in regimes over time for the six countries shows different results for different pairs of countries. In the last essay, we decompose the term premium of the North American CDX investment grade index into a permanent and a stationary component using a Markov switching unobserved component model. We explain the evolution of the two components in relating them to monetary policy and stock market variables. We establish that the inversion of the CDX index term premium is induced by sudden changes in the unobserved stationary component, which represents the evolution of the fundamentals underpinning the risk neutral probability of default in the economy. We find strong evidence that the unprecedented monetary policy response from the Fed during the crisis period was effective in reducing market uncertainty and helped to steepen the term structure of the CDX index, thereby mitigating systemic risk concerns. The impact of stock market volatility on flattening the term premium was substantially more robust in the crisis period. We also show that equity returns make a significant contribution to the CDX term premium over the entire sample period.
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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 estimation in order to detect outliers. The models are evaluated by how well they detect outliers and how often they misclassify inliers as well as the run time of the models. It is found that all the models performs approximately equally good, on the data sets used in thesis and the simulations done, in terms of how well the methods find outliers, apart from the test static method which performs worse than the others. Furthermore it is found that definition of an outlier is very crucial to how well a model detects the outliers. For the application of this thesis, the run time is an important aspect, and with this in mind an autoregressive model with a Student’s t-noise distribution is found to be the best one, both with respect to how well it detects outliers, misclassify inliers and run time of the model.I 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.
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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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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 method to determine the number of groups to use as well as how to allocate the assets to groups. We show through simulation that the method does well in identifying the groups, and apply the method to real data, showing its performance. We also develop and apply a Bayesian approach to this same problem.
Furthermore, we propose an instantaneous measure of correlation that can be used in many volatility models, and in fact show that it outperforms the popular sample Pearson's correlation coefficient for small sample sizes, thus opening the door to applications in fields other than finance.
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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 a single empirical time series and to the presence of extreme events whose distribution follows a power law, we cannot exclude that our results could be relevant to fields of complex systems physics different from finance.Questa 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.
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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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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 trading, we apply the SHARP model to forecast the bid-ask spreads of a large sample of NYSE equity stocks. [...]
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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 time series with consideration given to their dynamic nature. In this thesis, the prediction procedures will be carried out in three phases. The first phase proposes a new hybrid dynamic model based on Ensemble Empirical Mode Decomposition (EEMD), Back Propagation Neural Network (BPNN), Recurrent Neural Network (RNN), Support Vector Regression (SVR) and EEMD-Genetic Algorithm (GA)-Weighted Average (WA) to predict stock index closing price. EEMD in this phase is introduced as a preprocessing step to historical observation for the first time in the literature. The experimental results show that the EEMDD-GA-WA model performance is a notch above the other methods utilised in this phase. The second phase proposes a new hybrid static model based on Wavelet Transform (WT), RNN, Support Vector Machine (SVM), Nave Bayes and WT-GA-WA to predict the exact change of the stock index closing price. In this phase, the experimental results showed that the proposed WT-GA-WA model outperformed the rest of the models utilised in this phase. Moreover, the input data that are fed into the hybrid model in this phase are technical indicators. The third phase in this research introduces a new Hybrid Heuristic-Rules-based System (HHRS) for stock price prediction. This phase intends to combine the output of the hybrid models in phase one and two in order to enhance the final prediction results. Thus,to the best of our knowledge, this study is the only one to have carried out and tested this approach with a real data set. The results show that the HHRS outperformed all suggested models over all the data sets. Thus, this indicates that combining di↵erent techniques with diverse types of information could enhance prediction accuracy.
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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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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.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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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 & Poor 500 Composite Stock Index, presents recent empirical findings and stylized facts in the financial market from 1987 to 1996 and gives a brief introduction to the research field of capital asset return volatitlity models and properties of high frequency financial data. As the title indicates, the survey is restricted to research on the well known Standard & Poor 500 index. The second essay, with the title, Stylized Facts of Daily Return Series and the Hidden Markov Model, investigates the properties of the hidden Markov Model, HMM, and its capability of reproducing stylized facts of financial high frequency data. The third essay, Modelling the Conditional Mean and Conditional Variance: A combined Smooth Transition and Hidden Markov Approach with an Application to High Frequency Series, investigates the consequences of combining a nonlinear parameterized conditional mean with an HMM for the conditional variance when characterization of stylized facts is considered. Finally, the fourth essay entitled, Volatility Forecasting for Option Pricing on Exchange Rates and Stock Prices, investigates the volatility forecasting performance of some of the most frequently used capital asset return volatility models such as the GARCH with normal and t-distributed errors, the EGARCH and the HMM. The prediction error minimization approach is also investigated. Each essay is self-contained and could, in principle, be read in any order chosen by the reader. This, however, requires a working knowledge of the properties of the HMM. For readers less familiar with the research field the first essay may serve as an helpful introduction to the following three essays.Diss. Stockholm : Handelshögsk.
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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 provide negative answers. Phase space reconstruction is achieved by time delay embedding resulting in multiple embedded vectors. The cognitive approach suggested, is inspired by the capability of some chartists to predict the direction of an index by looking at the price time series. Thus, in this work, the calculation of the embedding dimension and the separation, in Takens‘ embedding theorem for phase space reconstruction, is not limited to False Nearest Neighbor, Differential Entropy or other specific method, rather, this work is interested in all embedding dimensions and separations that are regarded as different ways of looking at a time series by different chartists, based on their expectations. Prior to the prediction, the embedded vectors of the phase space are classified with Fuzzy-ART, then, for each class a back propagation Neural Network is trained to predict the last element of each vector, whereas all previous elements of a vector are used as features.
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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. The 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
info:eu-repo/semantics/nonPublished
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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 PhilosophyThis 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.
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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.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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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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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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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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Kwok, 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.
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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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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 crisis period. Chapter 3 explores density forecasts of the yield curve which, unlike the point forecasts, provide a full account of possible uncertainties surrounding the forecasts. We contribute by evaluating predictive performance of the recently developed stochastic-volatility arbitrage-free Nelson-Siegel models of Chris- tensen et al. (2010). The one-month-ahead predictive densities of the models appear to be inferior compared to those of their constant-volatility counter- parts. The advantage of modelling time-varying volatilities becomes evident only when forecasting interest rates at longer horizons. Chapter 3 deals with a more general problem of forecasting time series under structural change and long memory noise. Presence of long memory in the data is often easily confused with structural change. Wrongly account- ing for one when the other is present may lead to serious forecast failure. In our search for a forecast method that can perform reliably in presence of both features we extend the recent work of Giraitis et al. (2013). A forecast strategy with data-dependent discounting is adopted and typical robust-to- structural-change methods such as rolling window regression, forecast averag- ing and exponentially weighted moving average methods are exploited. We provide detailed theoretical analyses of forecast optimality by considering cer- tain types of structural changes and various degrees of long range dependence in noise. An extensive Monte Carlo study and empirical application to many UK time series ensure usefulness of adaptive forecast methods.
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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 monthly inflation time series show the validity of the new methodology. Next, we are concerned with the issue of bootstrap in strongly dependent data. We introduce a fractional differencing bootstrap methodology that allows the implementation of any resampling method in such series. Evidence of robustness is given by Monte Carlo experiments using various block and residuals resampling schemes. The second part of the thesis investigates the issue of forecasting macroeconomic variables. Heuristic methods for the optimisation of information criteria are employed and their forecasting performance is compared to the standard choices in the literature. The empirical application in Euro Area dataset suggests that the non-standard methods should be taken into consideration as they provide better forecasts on average. The last part of the thesis investigates the applied performance of covariance shrinkage in the portfolio optimisation problem when the universe of assets is large. Our approach suggests the use of a shrinkage coefficient that optimises functions with financial interpretation. Empirical results provide evidence that the shrinkage portfolios obtained using the suggested approach are characterised by higher Sharpe Ratios, cumulative returns and profit/loss ratio.
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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 empirical results show that the new models have both superior in- and out-of-sample properties. We show that the superior performance applies to a wide range of quantities of interest, including volatilities, covolatilities, betas and scenario-based risk measures. Time-varying volatility is common in macroeconomic data and has been incorporated into macroeconomic models in recent work. The second essay estimates dynamic panel data models with stochastic volatility by maximizing an approximate likelihood obtained via Rao-Blackwellized particle filters. Monte Carlo studies reveal the good and stable performance of our particle filter-based estimator. When the volatility of volatility is high, or when regressors are absent but stochastic volatility exists, our approach can be better than the maximum likelihood estimator which neglects stochastic volatility and GMM estimators. In the third essay, we test for the stable factor structure against considerable time variation in the factor loadings in the form of martingales. We obtain the asymptotic distribution of the test statistic by deriving the conditions under which the estimation error of the common factors is asymptotically negligible for the test statistic. Monte Carlo simulations show that the proposed test performs well. We apply the test to a panel of macroeconomic and financial variables in the UK and find the evidence of unstable factor structure during the recent financial crisis.
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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 impractical for large data sets as it may require unacceptably long computation time. In this study, new procedures that date cycles using the approach proposed by Litvine and Bismans (2015), were introduced, and were speciffically developed to be feasible for large time series data sets. These procedures are the stochastic generation and adaptation (SGA), buy-sell adapted Extrema importance identity sequence retrieval (BSA-EIISR) and buysell adapted bottom-up (BSA-BU) methods. An existing optimisation technique, known as particle swarm optimisation (PSO), was also employed. A statistical comparison was then made between these methods, including HR-FE2. This involved evaluating, on simulated data, the performance of the algorithms in terms of objective function value and computation time on different time series lengths, Hurst exponent, and number of buy-sell points. The SRace methodology (T. Zhang, Georgiopoulos, and Anagnostopoulos 2013) was then applied to these results in order to determine the most effcient methods. It was determined that, statistically, SGA, BSA-EIISR and BSA-BU are the most effcient methods. Number of buysell points was found to have the largest effect on relative performance of these methods. In some cases, the Hurst exponent also has a small effect on relative performance.
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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. However, most frequently used measures of interaction have certain limitations to their practical use, such as the assumption of normality or computational complexity. This thesis has two major aims; firstly, to address this lack of availability of suitable methods by presenting a set of approaches to dynamically measure symmetric and asymmetric interactions, i.e. causality, in multivariate non-Gaussian signals in a computationally efficient (online) framework, and secondly, to make use of these approaches to analyse multivariate financial time series in order to extract interesting and practically useful information from financial data. Most of our proposed approaches are primarily based on independent component analysis, a blind source separation method which makes use of higher-order statistics to capture information about the mixing process which gives rise to a set of observed signals. Knowledge about this information allows us to investigate the information coupling dynamics, as well as to study the asymmetric flow of information, in multivariate non-Gaussian data streams. We extend our multivariate interaction models, using a variety of statistical techniques, to study the scale-dependent nature of interactions and to analyse dependencies in high-dimensional systems using complex coupling networks. We carry out a detailed theoretical, analytical and empirical comparison of our proposed approaches with some other frequently used measures of interaction, and demonstrate their comparative utility, efficiency and accuracy using a set of practical financial case studies, focusing primarily on the foreign exchange spot market.
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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 characterise the dimensionality of embedded financial data. The stochastic approach includes here an estimation of the unconditioned and conditional return distributions using parametric, non- and semi-parametric density estimation techniques. Finally, it will be shown how elements from these two approaches could be combined to achieve a more realistic model for financial time series.
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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 observations. The novelty of the research lies in the design of predictive filters by maximising their spectrum flatness of forecast errors. The filters are then applied to forecast linear combinations of daily open, high, low and close prices of financial time series. Given the assumption that there are no structural breaks or switching regimes in a time series, the sufficient and necessary conditions that a time series can be predicted with zero errors by linear filters are examined. It is concluded that a band-limited time series can be predicted with zero errors by a predictive filter that has a constant magnitude response and constant group delay over the bandwidth of the time series. Because real world time series are not band-limited thus cannot be forecasted without errors, statistical tests of spectrum flatness which evaluate the departure of the spectral density from a constant value are introduced as measures of the predictability of time series. Properties of a time series are then investigated in the frequency domain using its spectrum flatness. A predictive filter is designed by maximising the error spectrum flatness that is equivalent to maximise the “whiteness” of forecast errors in the frequency domain. The focus is then placed on forecasting real world financial time series. By applying spectrum flatness tests, it is found that the property of the spectrum of a linear combination of daily open, high, low and close prices, which is called target prices, is different from that of a random walk process as there are much more low frequency components than high frequency ones in its spectrum. Therefore, an objective function is proposed to derive the target price time series from the historical observations of daily open, high, low and close prices. A predictive filter is then applied to obtain the one-step ahead forecast of the target prices, while profitable trading strategies are designed based on the forecast of target prices series. As a result, more than 70% success ratio could be achieved in terms of one-step ahead out-of-sample forecast of direction changes of the target price time series by taking the S&P500 index for example.
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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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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 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.