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Dissertations / Theses on the topic 'Financial time series model'
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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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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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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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Nacaskul, Poomjai. "Evolutionary optimisation and financial model-trading." Thesis, Imperial College London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.298802.
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Wong, Wing-mei. "Some topics in model selection in financial time series analysis." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B23273112.
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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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王詠媚 and Wing-mei Wong. "Some topics in model selection in financial time series analysis." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31225366.
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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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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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YAN, HONGXUAN. "Generalised linear Gegenbauer long memory models for time series of counts with financial and insurance applications." Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/19660.
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The contribution of this thesis is on developing new models and applying them to analyse data in finance and insurance. The proposed generalised linear Gegenbauer autoregressive moving average models (GLGARMA) incorporate GARMA into the mean functions of the four different count distributions within a GLM framework under the parameter-driven and observation-driven approaches. Model properties in the time domain and spectral density function in the frequency domain are studied and compared to seasonal long memory model. The approximated spectral density function of the PD GLGARMA model is derived to facilitate Whittle likelihood estimation. To estimate and forecast these models, we adopt a Bayesian approach implemented using the R package Rstan. Various model selection criteria including deviance information criterion are evaluated to select some best-fitting models to undertake forecasting of future events. We test 136 open interest series for the types of long memory structures. The GLGARMA models outperform these models in both in-sample fittings and out-of-sample forecasts for each type of long memory structures. The prevalence of long memory in death count series is demonstrated. We extend this Lee-Carter type GLGARMA resulting in three modelling components, namely, period effect, graduation effect and long memory. Results show that the long memory structures enhance the accuracy of in-sample fitting, out-of-sample forecast and life expectancy. A stationary long memory mortality model with long memory cohort effect (LMLM) model is proposed. The in-sample fitting, out-of-sample forecast performances and life expectancies are calculated to exhibit the enhancement by adopting LMLM model. Assuming both constant interest rate and stochastic interest rate model with four dependency models, the annuity pricing and guaranteed annuity options demonstrate the enhancement of adopting LMLM model.
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Goodman, Richard Dwight. "A stochastic short term financial planning model using time series analysis." Ohio : Ohio University, 1989. http://www.ohiolink.edu/etd/view.cgi?ohiou1182436372.
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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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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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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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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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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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Hudson, Brent. "Modelling the Covariance Dynamics of Multivariate Financial Time Series." Thesis, The University of Sydney, 2011. http://hdl.handle.net/2123/8086.
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Investor performance in financial markets can be significantly affected by their ability to model market volatility and correlation over time. The effectiveness of various market activities such as option pricing, portfolio optimisation and risk management rely on the accuracy of such modelling. This thesis proposes a series of multivariate GARCH models that attempt to accurately capture the volatility and correlation dynamics of stock returns. A Bayesian approach is utilised to estimate model parameters, extending classical maximum likelihood (ML) approaches commonly used in the literature for these types of models. A Bayesian prior distribution is proposed for a VECH model that expands the model parameter space and implicitly enforces necessary and sufficient conditions for its positive definiteness and covariance stationarity. An application to a set of US and UK stock indices supports this approach for both parameter and volatility estimation compared to classical ML applied to a competing BEKK model. Volatility asymmetry in stock returns is also discussed, and model selection techniques applied to an extended VECH model to determine the location and size of the asymmetry for international stock markets. In addition to asymmetry, an allowance is made for skewness and excess kurtosis in a proposed copula-GARCH model and is shown to exist in returns for an arbitrary stock portfolio. Moreover, the proposed model also performs well in estimating Value at Risk (VaR) for this portfolio, compared to other univariate and multivariate GARCH models considered. This thesis demonstrates the advantages of using the Bayesian approach for parameter estimation over classical ML, as well as the need to accurately capture the many properties of stock returns in order to improve the modelling of market volatility and correlation.
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Watteel-Sprague, Regina N. "Investigations in financial time series, model selection, option pricing, and density estimation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ58193.pdf.
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Ghibellini, Alessandro. "Trend prediction in financial time series: a model and a software framework." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24708/.
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The research has the aim to build an autonomous support for traders which in future can be translated in an Active ETF. My thesis work is characterized for a huge focus on problem formulation and an accurate analysis on the impact of the input and the length of the future horizon on the results. I will demonstrate that using financial indicators already used by professional traders every day and considering a correct length of the future horizon, it is possible to reach interesting scores in the forecast of future market states, considering both accuracy, which is around 90% in all the experiments, and confusion matrices which confirm the good accuracy scores, without an expensive Deep Learning approach. In particular, I used a 1D CNN. I also emphasize that classification appears to be the best approach to address this type of prediction in combination with proper management of unbalanced class weights. In fact, it is standard having a problem of unbalanced class weights, otherwise the model will react for inconsistent trend movements. Finally I proposed a Framework which can be used also for other fields which allows to exploit the presence of the Experts of the sector and combining this information with ML/DL approaches.
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Bulla, Jan. "Application of Hidden Markov and Hidden Semi-Markov Models to Financial Time Series." Doctoral thesis, [S.l. : s.n.], 2006. http://swbplus.bsz-bw.de/bsz260867136inh.pdf.
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Surapaitoolkorn, Wantanee. "Bayesian inference for volatility models in financial time series." Thesis, Imperial College London, 2006. http://hdl.handle.net/10044/1/1249.
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The aim of the thesis is to study the two principal volatility models used in ¯nancial time series, and to perform inference using a Bayesian approach. The ¯rst model is the Deterministic Time-Varying volatility represented by Autoregressive Conditional Heteroscedastic (ARCH) models. The second model is the Stochastic Time Varying volatility or Stochastic Volatility (SV) model. The thesis concentrates on using Financial Foreign Exchange (FX) data including time series for four Asian countries of Thailand, Singapore, Japan and Hong Kong, and FX data sets from other countries. The time period this particular FX data set covers includes the recent biggest crisis in Asian ¯nancial markets in 1997. The analysis involves exploring high frequency ¯nancial FX data where the sets of data used are the daily and hourly opening FX rates. The key development of the thesis is the implementation of changepoint models to allow for non-stationarity in the volatility process. The changepoint approach has only rarely been implemented for volatility data. In this thesis, the changepoint model for SVtype volatility structures is formulated. The variable dimensional nature of the inference problem, that is, that the number as well as the locations of the volatility changepoints are unknown, is acknowledged and incorporated, as are the potential leptokurtic nature of ¯nancial returns. The Bayesian computational approach used for making inference about the model parameters is Markov Chain Monte Carlo (MCMC). Another contribution of this thesis is the study of reparameterizations of parameters in both ARCH and SV models. The objective is to improve the performance of the MCMC method.
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Wong, Chak K. J. "Latent factor models of high frequency financial time series." Thesis, University of Oxford, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.395319.
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方柏榮 and Pak-wing Fong. "Topics in financial time series analysis: theory and applications." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31241669.
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Milunovich, George Economics Australian School of Business UNSW. "Modelling and valuing multivariate interdependencies in financial time series." Awarded by:University of New South Wales. School of Economics, 2006. http://handle.unsw.edu.au/1959.4/25162.
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This thesis investigates implications of interdependence between stock market prices in the context of several financial applications including: portfolio selection, tests of market efficiency and measuring the extent of integration among national stock markets. In Chapter 2, I note that volatility spillovers (transmissions of risk) have been found in numerous empirical studies but that no one, to my knowledge, has evaluated their effects in the general portfolio framework. I dynamically forecast two multivariate GARCH models, one that accounts for volatility spillovers and one that does not, and construct optimal mean-variance portfolios using these two alternative models. I show that accounting for volatility spillovers lowers portfolio risk with statistical significance and that risk-averse investors would prefer realised returns from portfolios based on the volatility spillover model. In Chapter 3, I develop a structural MGARCH model that parsimoniously specifies the conditional covariance matrix and provides an identification framework. Using the model to investigate interdependencies between size-sorted portfolios from the Australian Stock Exchange, I gain new insights into the issue of asymmetric dependence. My findings not only confirm the observation that small stocks partially adjust to market-wide news embedded in the returns to large firms but also present evidence that suggests that small firms in Australia fail to even partially adjust (with statistical significance) to large firms??? shocks contemporaneously. All adjustments in small capitalisation stocks occur with a lag. Chapter 4 uses intra-daily data and develops a new method for measuring the extent of stock market integration that takes into account non-instantaneous adjustments to overnight news. This approach establishes the amounts of time that the New York, Tokyo and London stock markets take to fully adjust to overnight news and then uses this This thesis investigates implications of interdependence between stock market prices in the context of several financial applications including: portfolio selection, tests of market efficiency and measuring the extent of integration among national stock markets. In Chapter 2, I note that volatility spillovers (transmissions of risk) have been found in numerous empirical studies but that no one, to my knowledge, has evaluated their effects in the general portfolio framework. I dynamically forecast two multivariate GARCH models, one that accounts for volatility spillovers and one that does not, and construct optimal mean-variance portfolios using these two alternative models. I show that accounting for volatility spillovers lowers portfolio risk with statistical significance and that risk-averse investors would prefer realised returns from portfolios based on the volatility spillover model. In Chapter 3, I develop a structural MGARCH model that parsimoniously specifies the conditional covariance matrix and provides an identification framework. Using the model to investigate interdependencies between size-sorted portfolios from the Australian Stock Exchange, I gain new insights into the issue of asymmetric dependence. My findings not only confirm the observation that small stocks partially adjust to market-wide news embedded in the returns to large firms but also present evidence that suggests that small firms in Australia fail to even partially adjust (with statistical significance) to large firms??? shocks contemporaneously. All adjustments in small capitalisation stocks occur with a lag. Chapter 4 uses intra-daily data and develops a new method for measuring the extent of stock market integration that takes into account non-instantaneous adjustments to overnight news. This approach establishes the amounts of time that the New York, Tokyo and London stock markets take to fully adjust to overnight news and then uses this
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Noureldin, Diaa. "Essays on multivariate volatility and dependence models for financial time series." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:fdf82d35-a5e7-4295-b7bf-c7009cad7b56.
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This thesis investigates the modelling and forecasting of multivariate volatility and dependence in financial time series. The first paper proposes a new model for forecasting changes in the term structure (TS) of interest rates. Using the level, slope and curvature factors of the dynamic Nelson-Siegel model, we build a time-varying copula model for the factor dynamics allowing for departure from the normality assumption typically adopted in TS models. To induce relative immunity to structural breaks, we model and forecast the factor changes and not the factor levels. Using US Treasury yields for the period 1986:3-2010:12, our in-sample analysis indicates model stability and we show statistically significant gains due to allowing for a time-varying dependence structure which permits joint extreme factor movements. Our out-of-sample analysis indicates the model's superior ability to forecast the conditional mean in terms of root mean square error reductions and directional forecast accuracy. The forecast gains are stronger during the recent financial crisis. We also conduct out-of-sample model evaluation based on conditional density forecasts. The second paper introduces a new class of multivariate volatility models that utilizes high-frequency data. We discuss the models' dynamics and highlight their differences from multivariate GARCH models. We also discuss their covariance targeting specification and provide closed-form formulas for multi-step forecasts. Estimation and inference strategies are outlined. Empirical results suggest that the HEAVY model outperforms the multivariate GARCH model out-of-sample, with the gains being particularly significant at short forecast horizons. Forecast gains are obtained for both forecast variances and correlations. The third paper introduces a new class of multivariate volatility models which is easy to estimate using covariance targeting. The key idea is to rotate the returns and then fit them using a BEKK model for the conditional covariance with the identity matrix as the covariance target. The extension to DCC type models is given, enriching this class. We focus primarily on diagonal BEKK and DCC models, and a related parameterisation which imposes common persistence on all elements of the conditional covariance matrix. Inference for these models is computationally attractive, and the asymptotics is standard. The techniques are illustrated using recent data on the S&P 500 ETF and some DJIA stocks, including comparisons to the related orthogonal GARCH models.
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Grziska, Martin. "Multivariate GARCH and dynamic copula models for financial time series." Diss., Ludwig-Maximilians-Universität München, 2014. http://nbn-resolving.de/urn:nbn:de:bvb:19-179219.
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This thesis presents several non-parametric and parametric models for estimating dynamic dependence between financial time series and evaluates their ability to precisely estimate risk
measures. Furthermore, the different dependence models are used to analyze the integration
of emerging markets into the world economy. In order to analyze numerous dependence structures and to discover possible asymmetries, two distinct model classes are investigated: the
multivariate GARCH and Copula models. On the theoretical side a new dynamic dependence
structure for multivariate Archimedean Copulas is introduced which lifts the prevailing restriction to two dimensions and extends the multivariate dynamic Archimedean Copulas to more than two dimensions. On this basis a new mixture copula is presented using the newly invented multivariate dynamic dependence structure for the Archimedean Copulas and mixing it with multivariate elliptical copulas. Simultaneously a new process for modeling the time-varying weights of the mixture copula is introduced: this specification makes it possible to estimate various dependence structures within a single model.
The empirical analysis of different portfolios shows that all equity portfolios and the bond portfolios of the emerging markets exhibit negative asymmetries, i.e. increasing dependence during market downturns. However, the portfolio consisting of the developed market bonds does
not show any negative asymmetries. Overall, the analysis of the risk measures reveals that parametric models display portfolio risk more precisely than non-parametric models. However, no
single parametric model dominates all other models for all portfolios and risk measures. The
investigation of dependence between equity and bond portfolios of developed countries, proprietary, and secondary emerging markets reveals that secondary emerging markets are less
integrated into the world economy than proprietary. Thus, secondary emerging markets are
more suitable to diversify a portfolio consisting of developed equity or bond indices than proprietary
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Yfanti, Stavroula. "Non-linear time series models with applications to financial data." Thesis, Brunel University, 2014. http://bura.brunel.ac.uk/handle/2438/9247.
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The purpose of this thesis is to investigate the financial volatility dynamics through the GARCH modelling framework. We use univariate and multivariate GARCH-type models enriched with long memory, asymmetries and power transformations. We study the financial time series volatility and co-volatility taking into account the structural breaks detected and focusing on the effects of the corresponding financial crisis events. We conclude to provide a complete framework for the analysis of volatility with major policy implications and benefits for the current risk management practices. We first investigate the volume-volatility link for different investor categories and orders, around the Asian crisis applying a univariate dual long memory model. Our analysis suggests that the behaviour of volatility depends upon volume, but also that the nature of this dependence varies with time and the source of volume. We further apply the vector AR-DCC-FIAPARCH and the UEDCC-AGARCH models to several stock indices daily returns, taking into account the structural breaks of the time series linked to major economic events including crisis shocks We find significant cross effects, time-varying shock and volatility spillovers, time-varying persistence in the conditional variances, as well as long range volatility dependence, asymmetric volatility response to positive and negative shocks and the power of returns that best fits the volatility pattern. We observe higher dynamic correlations of the stock markets after a crisis event, which means increased contagion effects between the markets, a continuous herding investors’ behaviour, as the in-crisis correlations remain high, and a higher level of correlations during the recent financial crisis than during the Asian. Finally, we study the High-frEquency-bAsed VolatilitY (HEAVY) models that combine daily returns with realised volatility. We enrich the HEAVY equations through the HYAPARCH formulation to propose the HYDAP-HEAVY (HYperbolic Double Asymmetric Power) and provide a complete framework to analyse the volatility process.
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Neslihanoglu, Serdar. "Validating and extending the two-moment capital asset pricing model for financial time series." Thesis, University of Glasgow, 2014. http://theses.gla.ac.uk/5658/.
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This thesis contributes to the ongoing discussion about the financial and statistical modelling of returns on financial stock markets. It develops the asset pricing model concept which has received continuous attention for almost 50 years in the area of finance, as a method by which to identify the stochastic behaviour of financial data when making investment decisions, such as portfolio choices, and determining market risk. The best known and most widely used asset pricing model detailed in the finance literature is the Two-Moment Capital Asset Pricing Model (CAPM) (consistent with the Linear Market Model), which was developed by Sharpe-Lintner- Mossin in the 1960s to explore systematic risk in a mean-variance framework and is the benchmark model for this thesis. However, this model has now been criticised as misleading and insufficient as a tool for characterising returns in financial stock markets. This is partly a consequence of the presence of non-normally distributed returns and non-linear relationships between asset and market returns. The inadequacies of the Two-Moment CAPM are qualified in this thesis, and the extensions are proposed that improve on both model fit and forecasting abilities. To validate and extend the benchmark Linear Market Model, the empirical work presented in this thesis centres around three related extensions. The first extension compares the Linear Market Model’s modelling and forecasting abilities with those of the time-varying Linear Market Model (consistent with the conditional Two-Moment CAPM) for 19 Turkish industry sector portfolios. Two statistical modelling techniques are compared: a class of GARCH-type models, which allow for non-constant variance in stock market returns, and state space models, which allow for the systematic covariance risk to change linearly over time in the time-varying Linear Market Model. The state space modelling is shown to outperform the GARCH-type modelling. The second extension concentrates on comparing the performance of the Linear Market Model, with models for higher order moments, including polynomial extensions and a Generalised Additive Model (GAM). In addition, time-varying versions of the Linear Market Model and polynomial extensions, in the form of state space models, are considered. All these models are applied to 18 global markets during three different time periods: the entire period from July 2002 to July 2012, from July 2002 to just before the October 2008 financial crisis, and from after the October 2008 financial crisis to July 2012. Although the more complex unconditional models are shown to improve slightly on the Linear Market Model, the state space models again improve substantially on all the unconditional models. The final extension focuses on comparing the performance of four possible multivariate state space forms of the time-varying Linear Market Models, using data on the same 18 global markets, utilising correlations between markets. This approach is shown to improve further on the performance of the univariate state space models. The thesis concludes by drawing together three related themes: the inappropriateness of the Linear Market Model, the extent to which multivariate modelling improves the univariate market model and the state of the world’s stock markets.
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Kwan, Chun-kit. "Statistical inference for some financial time series models with conditional heteroscedasticity." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B39794027.
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Kwan, Chun-kit, and 關進傑. "Statistical inference for some financial time series models with conditional heteroscedasticity." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B39794027.
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He, Ni. "Self-organising local regressive models for nonstationary financial time series modelling." Thesis, University of Manchester, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.490183.
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This thesis presents financial time series modelling methods based on the Self-Organising Map (SOM). The principal modelling technique is the local model approach, which aims to fit simple models to localised segments ofthe data. The simple model uses parametric linear regression to describe the variation of the data. The local model approach combines the simplicity of a parametric method with the flexibility of a non-parametric method. Temporal SOM, in addition, integrates temporal context in normal SOM algorithm for a more appropriate analysis of sequential data. The proposed methods are presented in three forms. The first is the temporal SOM with local support vector regressive models. This method uses the recurrent self-organising map to partition the original data space into several disjointed regions and then support vector machines are used to build predictive regressive models. The second approach is the self-organising mixture autoregressive network (SOMAR), the key proposed method in this thesis. It aims to describe and model non-stationary time series by means ofmixture autoregressive local models constructed from topologically clustered time-series segments. The SOMAR network uses the sum (of the absolute value) of autocorrelation coefficients as the similarity measure to identify the winning local autoregressive model. The last approach uses a hybrid system formed by a mixture ofregressive models and economical indicators, which generate either sell or buy signals by monitoring the overbought and oversold status of trading assets. Extensive experiments are presented and the performance ofthe proposed methods are analysed. It is shown that the proposed method, in particular, the SOMAR network, can yield better than global regressive models, econometric time series models, and random walk models.
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Li, Xiaoyu. "Memory and persistence in models of volatility in financial time series." Thesis, University of Exeter, 2016. http://hdl.handle.net/10871/25947.
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This thesis first investigates the moment and memory properties of exponential-type conditional heteroscedasticity models. This primarily includes exponential generalised autoregressive conditional heteroscedastic (EGARCH) models, the fractionally integrated EGARCH model of Bollerslev and Mikkelsen (1996) (FIEGARCH(BM)), the hyperbolic EGARCH (HYEGARCH) model and the FIEGARCH(DL) model, as presented in Chapter 2. The moment conditions of these models are derived from previous literature, and the memory properties are measured by using the near-epoch dependence (NED) functions of an independent process approach. The existence of moments supports the limited memory properties of these models. This study shows that exponential autoregressive conditional heteroscedastic (EARCH)(∞) processes may exhibit geometric memory, hyperbolic memory or long memory. The EGARCH is a case of a geometric memory process. The FIEGARCH(BM) and HY/FIEGARCH(DL) processes can exhibit hyperbolic memory or long memory, depending on the sign of the memory parameter. The study also derives the functional central limit theorem (FCLT) or fractional FCLT for the relevant processes in these exponential-type conditional heteroscedasticity models. Finally, the results of the simulation show that the HYEGARCH model has a hyperbolic memory and that the FIEGARCH(DL) model can capture long memory in absolute return series. Next, the study investigates the asymptotic properties of the quasi-maximum likelihood estimator (QMLE) in autoregressive moving average (ARMA) models with EGARCH or HY/FIEGARCH(DL) errors in Chapter 3. This part of the study aims to investigate the asymptotic theory of the ARMA(1,1)-EGARCH(1,1) models and that of the pure HY/FIEGARCH(DL) models. First, the literature on the asymptotic properties of the ARMA-GARCH and EGARCH processes is reviewed. The conditions for the consistency and asymptotic normality of the QMLE of the ARMA-EGARCH models are then demonstrated. This analysis also provides an investigation of that of the QMLE in the HY/FIEGARCH(DL) processes. A Monte Carlo simulation is used to study the properties of the QMLE in the pure HY/FIEGARCH(DL) processes. Lastly, in a study co-authored with Professor James Davidson, we derive a simple sufficient condition for strict stationarity in the ARCH(∞) class of processes with conditional heteroscedasticity. The concept of persistence in these processes is explored, and is the subject of a set of simulations showing how persistence depends on both the pattern of the lag coefficients of the ARCH model and the distribution of the driving shocks. The results are used to argue that an alternative to the usual method of ARCH/GARCH volatility forecasting should be considered.
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Costa, Maria da Conceição Cristo Santos Lopes. "Optimal alarms systems and its application to financial time series." Doctoral thesis, Universidade de Aveiro, 2014. http://hdl.handle.net/10773/12872.
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Doutoramento em MatemáticaThis thesis focuses on the application of optimal alarm systems to non linear time series models. The most common classes of models in the analysis of real-valued and integer-valued time series are described. The construction of optimal alarm systems is covered and its applications explored. Considering models with conditional heteroscedasticity, particular attention is given to the Fractionally Integrated Asymmetric Power ARCH, FIAPARCH(p; d; q) model and an optimal alarm system is implemented, following both classical and Bayesian methodologies. Taking into consideration the particular characteristics of the APARCH(p; q) representation for financial time series, the introduction of a possible counterpart for modelling time series of counts is proposed: the INteger-valued Asymmetric Power ARCH, INAPARCH(p; q). The probabilistic properties of the INAPARCH(1; 1) model are comprehensively studied, the conditional maximum likelihood (ML) estimation method is applied and the asymptotic properties of the conditional ML estimator are obtained. The final part of the work consists on the implementation of an optimal alarm system to the INAPARCH(1; 1) model. An application is presented to real data series.
Esta tese centra-se na aplicação de sistemas de alarme ótimos a modelos de séries temporais não lineares. As classes de modelos mais comuns na análise de séries temporais de valores reais e de valores inteiros são descritas com alguma profundidade. É abordada a construção de sistemas de alarme ótimos e as suas aplicações são exploradas. De entre os modelos com heterocedasticidade condicional é dada especial atenção ao modelo ARCH Fraccionalmente Integrável de Potência Assimétrica, FIAPARCH(p; d; q), e é feita a implementação de um sistema de alarme ótimo, considerando ambas as metodologias clássica e Bayesiana. Tomando em consideração as características particulares do modelo APARCH(p; q) na aplicação a séries de dados financeiros, é proposta a introdução do seu homólogo para a modelação de séries temporais de contagens: o modelo ARCH de valores INteiros e Potência Assimétrica, INAPARCH(p; q). As propriedades probabilísticas do modelo INAPARCH(1; 1) são extensivamente estudadas, é aplicado o método da máxima verosimilhança (MV) condicional para a estimação dos parâmetros do modelo e estudadas as propriedades assintóticas do estimador de MV condicional. Na parte final do trabalho é feita a implementação de um sistema de alarme ótimo ao modelo INAPARCH(1; 1) e apresenta-se uma aplicação a séries de dados reais.
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Van, Zyl Verena Helen. "Searching for histogram patterns due to macroscopic fluctuations in financial time series." Thesis, Stellenbosch : University of Stellenbosch, 2007. http://hdl.handle.net/10019.1/3078.
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Thesis (MComm (Business Management))--University of Stellenbosch, 2007.ENGLISH ABSTRACT: his study aims to investigate whether the phenomena found by Shnoll et al. when applying histogram pattern analysis techniques to stochastic processes from chemistry and physics are also present in financial time series, particularly exchange rate and index data. The phenomena are related to fine structure of non-smoothed frequency distributions drawn from statistically insufficient samples of changes and their patterns in time. Shnoll et al. use the notion of macroscopic fluctuations to explain the behaviour of sequences of histograms. Histogram patterns in time adhere to several laws that could not be detected when using time series analysis methods. In this study general approaches are reviewed that may be used to model financial markets and the volatility of price processes in particular. Special emphasis is placed on the modelling of highfrequency data sets and exchange rate data. Following previous studies of the Shnoll phenomena from other fields, different steps of the histogram sequence analysis are carried out to determine whether the findings of Shnoll et al. could also be applied to financial market data. The findings of this thesis widen the understanding of time varying volatility and can aid in financial risk measurement and management. Outcomes of the study include an investigation of time series characteristics in terms of the formation of discrete states, the detection of the near zone effect as proclaimed by Shnoll et al., the periodic recurrence of histogram shapes as well as the synchronous variation in data sets measured in the same time intervals.
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Addo, Peter Martey. "Modern approaches for nonlinear data analysis of economic and financial time series." Thesis, Paris 1, 2014. http://www.theses.fr/2014PA010033/document.
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L’axe principal de la thèse est centré sur des approches non-linéaires modernes d’analyse des données économiques et financières, avec une attention particulière sur les cycles économiques et les crises financières. Un consensus dans la littérature statistique et financière s’est établie autour du fait que les variables économiques ont un comportement non-linéaire au cours des différentes phases du cycle économique. En tant que tel, les approches/modèles non-linéaires sont requis pour saisir les caractéristiques du mécanisme de génération des données intrinsèquement asymétriques, que les modèles linéaires sont incapables de reproduire.À cet égard, la thèse propose une nouvelle approche interdisciplinaire et ouverte à l’analyse des systèmes économiques et financiers. La thèse présente des approches robustes aux valeurs extrêmes et à la non-stationnarité, applicables à la fois pour des petits et de grands échantillons, aussi bien pour des séries temporelles économiques que financières. La thèse fournit des procédures dites étape par étape dans l’analyse des indicateurs économiques et financiers en intégrant des concepts basés sur la méthode de substitution de données, des ondelettes, espace incorporation de phase, la m´méthode retard vecteur variance (DVV) et des récurrences parcelles. La thèse met aussi en avant des méthodes transparentes d’identification, de datation des points de retournement et de l´évaluation des impacts des crises économiques et financières. En particulier, la thèse fournit également une procédure pour anticiper les crises futures et ses conséquences.L’étude montre que l’intégration de ces techniques dans l’apprentissage de la structure et des interactions au sein et entre les variables économiques et financières sera très utile dans l’élaboration de politiques de crises, car elle facilite le choix des méthodes de traitement appropriées, suggérées par les données.En outre, une nouvelle procédure pour tester la linéarité et la racine unitaire dans un cadre non-linéaire est proposé par l’introduction d’un nouveau modèle – le modèle MT-STAR – qui a des propriétés similaires au modèle ESTAR mais réduit les effets des problèmes d’identification et peut aussi représenter l’asymétrie dans le mécanisme d’ajustement vers l’équilibre. Les distributions asymptotiques du test de racine unitaire proposées sont non-standards et sont calculées. La puissance du test est évaluée par simulation et quelques illustrations empiriques sur les taux de change réel montrent son efficacité. Enfin, la thèse développe des modèles multi-variés Self-Exciting Threshold Autoregressive avec des variables exogènes (MSETARX) et présente une méthode d’estimation paramétrique. La modélisation des modèles MSETARX et des problèmes engendrés par son estimation sont brièvement examinésThis thesis centers on introducing modern non-linear approaches for data analysis in economics and finance with special attention on business cycles and financial crisis. It is now well stated in the statistical and economic literature that major economic variables display non-linear behaviour over the different phases of the business cycle. As such, nonlinear approaches/models are required to capture the features of the data generating mechanism of inherently asymmetric realizations, since linear models are incapable of generating such behavior.In this respect, the thesis provides an interdisciplinary and open-minded approach to analyzing economic and financial systems in a novel way. The thesis presents approaches that are robust to extreme values, non-stationarity, applicable to both short and long data length, transparent and adaptive to any financial/economic time series. The thesis provides step-by-step procedures in analyzing economic/financial indicators by incorporating concepts based on surrogate data method, wavelets, phase space embedding, ’delay vector variance’ (DVV) method and recurrence plots. The thesis also centers on transparent ways of identifying, dating turning points, evaluating impact of economic and financial crisis. In particular, the thesis also provides a procedure on how to anticipate future crisis and the possible impact of such crisis. The thesis shows that the incorporation of these techniques in learning the structure and interactions within and between economic and financial variables will be very useful in policy-making, since it facilitates the selection of appropriate processing methods, suggested by the data itself.In addition, a novel procedure to test for linearity and unit root in a nonlinear framework is proposed by introducing a new model – the MT-STAR model – which has similar properties of the ESTAR model but reduces the effects of the identification problem and can also account for asymmetry in the adjustment mechanism towards equilibrium. The asymptotic distributions of the proposed unit root test is non-standard and is derived.The power of the test is evaluated through a simulation study and some empirical illustrations on real exchange rates show its accuracy. Finally, the thesis defines a multivariate Self–Exciting Threshold Autoregressive with eXogenous input (MSETARX) models and present an estimation procedure for the parameters. The modeling procedure for the MSETARX models and problems of estimation are briefly considered
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Zeng, Songlin. "Nonlinear Time Series Models with Applications in Macroeconomics and Finance." Thesis, Cergy-Pontoise, 2013. http://www.theses.fr/2013CERG0638.
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Les trois chapitres suivants examinent: 1) si les taux de change réels d'Asie du Sud-Est sont nonlinéaire, 2) l'inférence bayésienne sur le modèle de série temporelle nonlinéaire avec des applications sur le taux de change réel,et 3) la cyclicité et effet de rebond dans le marché boursier.Depuis la fin des années nonante, les analyses théorique et empirique consacrée au taux de change réel suggèrent que la dynamique pourrait être bien estimés par les modèles non linéaires. Le premier chapitre examine cette possibilité utilisant les données mensuelles de l'ASEAN-5, et il s'étend la recherche existante dans deux directions. Tout d'abord, nous utilisons récemment mis au point des tests de racine unitaire ce qui permettra d'assouplir les modèles non linéaires stationnaires dans le cadre du d'autre alternative que l'couramment utilisés à SETAR ou ESTAR modèle. Deuxièmement, bien que différents modèles nonlinéaires survivre aux tests de mis-spécification, une expérience Monte Carlo à partir de généralisées fonctions de réponse impulsionnelle est utilisé pour comparer leur pertinence relative. Nos résultats i) soutenir l'hypothèse de retour nonlinéaire à la moyenne , et donc la parité de pouvoir d'achat, dans la moitié des cas et ii) indiquent MRLSTAR et ESTAR comme les plus probables processus générant des taux de change réels.Le deuxième chapitre analyse ACR modèle. Nous proposons une approche bayésienne complète d'inférence et une attention particulière est portée sur les paramètres des variables de seuil. Nous discutons le choix des distributions a priori et proposer une chaîne de Markov algorithme de Monte Carlo pour estimer les paramètres et les variables latentes. Une étude de simulation et de l'application à des données taux de change réelles illustrer l'analyse.Le troisième chapitre explore que les différentes formes de recouvrements dans les marchés financiers peuvent présenter dans un modèle de Markov Switching. Elle s'appuie sur les effets de rebond d'abord analysé par Kim, Morley et Piger [2005] dans le cycle des affaires et généralisé par Bec, Bouabdallah et Ferrara [2011] pour permettre une plus souple de type rebond.Nos résultats i) montrer que l'effet de rebond est statistiquement significative et importante dans tous les cas, mais l'Allemagne où la preuve est moins claire et ii) l'impact négatif permanent de marchés baissiers sur l'indice est notablement réduite lorsque le rebond est explicitement pris en compteThe following three chapters investigate: 1) whether Southeast Asian real exchange rates are nonlinear mean reverting, 2) bayesian inference on nonlinear time series model with applications in real exchange rate, and 3)cyclicality and bounce-back effect in stock market. Since the late nineties, both theoretical and empirical analyses devoted to the real exchange rate suggest that their dynamics might be well approximated by nonlinear models. This paper examines this possibility for post-1970 monthly ASEAN-5 data, extending the existing research in two directions. First, we use recently developed unit root tests which allow for more flexible nonlinear stationary models under the alternative than the commonly used Self-Exciting Threshold or Exponential Smooth Transition AutoRegressions. Second, while different nonlinear models survive the mis-specification tests, a Monte Carlo experiment from generalized impulse response functions is used to compare their relative relevance. Our results support the nonlinear mean-reverting hypothesis, and hence the Purchasing Power Parity, in half the cases and point to the Multiple Regime-Logistic Smooth Transition and the Self-Exciting Threshold AutoRegressive models as the most likely data generating processes of these real exchange rates.Various nonlinear threshold models are employed to mimic the real exchange rate dynamics. A natural question arises: Which model does the best job of modeling the real exchange rate process? It is difficult and not straightforward to formally compare the nonlinear models within classic approach. In the second chapter, we propose to use Bayesian approach to address this issue. The second part of my dissertation actually uses a Bayesian method to estimate some nonlinear time series models, the ACR model, SETAR model, and MAR model. We propose a full Bayesian inference approach and particular attention is paid to the parameters of the threshold variables. We discuss the choice of the prior distributions and propose a Markov-chain Monte Carlo algorithm for estimating both the parameters and the latent variables. A simulation study and the application to real exchange rate data illustrate the analysis. Our empirical results of the second chapter show that i) Bayesian estimations closely match those of the Maximum likelihood for French real exchange rate vis-a-vis Deutsche Mark; ii)the speed of real exchange rate's adjustment to equilibrium level is overestimated if heterogeneous variances in two regimes is not taken into account; iii) ACR model is preferred to other nonlinear threshold models, SETAR and MAR; iv) within ACR class models, the suitable transition function form is selected based on Bayes factor.This paper proposes an empirical study of the shape of recoveries in financial markets from a bounce-back augmented Markov Switching model. It relies on models first applied by Kim, Morley et Piger [2005] to the business cycle analysis. These models are estimated for monthly stock market returns data of five developed countries for the post-1970 period. Focusing on a potential bounce-back effect in financial markets, its presence and shape are formally tested. Our results show that i) the bounce-back effect is statistically significant and large in all countries, but Germany where evidence is less clear-cut and ii) the negative permanent impact of bear markets on the stock price index is notably reduced when the rebound is explicitly taken into account
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Spanhel, Fabian [Verfasser], and Stefan [Akademischer Betreuer] Mittnik. "A copula-based approach to model serial dependence in financial time series / Fabian Spanhel ; Betreuer: Stefan Mittnik." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2015. http://d-nb.info/1131551893/34.
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Al-Momani, Mohammad H. "Financial Transfer and Its Impact on the Level of Democracy: A Pooled Cross-Sectional Time Series Model." Thesis, University of North Texas, 2003. https://digital.library.unt.edu/ark:/67531/metadc4243/.
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This dissertation is a pooled time series, cross-sectional, quantitative study of the impact of international financial transfer on the level of democracy. The study covers 174 developed and developing countries from 1976 through 1994. Through evaluating the democracy and democratization literature and other studies, the dissertation develops a theory and testable hypotheses about the impact of the international variables foreign aid and foreign direct investment on levels of democracy. This study sought to determine whether these two financial variables promote or nurture democracy and if so, how?
A pooled time-series cross-sectional model is developed employing these two variables along with other relevant control variables. Control variables included the presence of the Cold War and existence of formal alliance with the United States, which account for the strategic dimension that might affect the financial transfer - level of democracy linkage. The model also includes an economic development variable (per capita Gross National Product) to account for the powerful impact for economic development on the level of democracy, as well as a control for each country's population size. By addressing and the inclusion of financial, economic, strategic, and population size effects, I consider whether change in these variables affect the level of democracy and in which direction.
The dissertation tests this model by employing several techniques. The variables are subjected to bivariate and multivariate analysis including bivariate correlations, analysis of variance, and ordinary least square (OLS) multivariate regression with robust matrix and a lagged dependent variable. Panel corrected standard error (PCSE) was also employed to empirically test the pooled timeseries cross-sectional multivariate model. The dissertation analytical section concludes with path analysis testing which showed the impact of each of the independent variables on the dependent variable.
The findings indicate less impact of international financial variables upon the level of democracy than hypothesized. Foreign assistance correlates negatively with economic development levels and has no effect on democracy levels. In contrast, foreign direct investment associates positively to economic development levels and, through increased economic development, contributes to democracy.
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Tadjuidje, Kamgaing Joseph. "Competing neural networks as models for non stationary financial time series changepoint analysis /." [S.l. : s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=974108014.
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Andersson, Markus. "Multivariate Financial Time Series and Volatility Models with Applications to Tactical Asset Allocation." Thesis, KTH, Matematisk statistik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175326.
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The financial markets have a complex structure and the modelling techniques have recently been more and more complicated. So for a portfolio manager it is very important to find better and more sophisticated modelling techniques especially after the 2007-2008 banking crisis. The idea in this thesis is to find the connection between the components in macroeconomic environment and portfolios consisting of assets from OMX Stockholm 30 and use these relationships to perform Tactical Asset Allocation (TAA). The more specific aim of the project is to prove that dynamic modelling techniques outperform static models in portfolio theory.Den finansiella marknaden är av en väldigt komplex struktur och modelleringsteknikerna har under senare tid blivit allt mer komplicerade. För en portföljförvaltare är det av yttersta vikt att finna mer sofistikerade modelleringstekniker, speciellt efter finanskrisen 2007-2008. Idéen i den här uppsatsen är att finna ett samband mellan makroekonomiska faktorer och aktieportföljer innehållande tillgångar från OMX Stockholm 30 och använda dessa för att utföra Tactial Asset Allocation (TAA). Mer specifikt är målsättningen att visa att dynamiska modelleringstekniker har ett bättre utfall än mer statiska modeller i portföljteori.
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Miazhynskaia, Tatiana, Georg Dorffner, and Engelbert J. Dockner. "Non-linear versus non-gaussian volatility models in application to different financial markets." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 2003. http://epub.wu.ac.at/1598/1/document.pdf.
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We used neural-network based modelling to generalize the linear econometric return models and compare their out-of-sample predictive ability in terms of different performance measures under three density specifications. As error measures we used the likelihood values on the test sets as well as standard volatility measures. The empirical analysis was based on return series of stock indices from different financial markets. The results indicate that for all markets there was found no improvement in the forecast by non-linear models over linear ones, while nongaussian models significantly dominate the gaussian models with respect to most performance measures. The likelihood performance measure mostly favours the linear model with Student-t distribution, but the significance of its superiority differs between the markets. (author's abstract)Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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Zhu, Jia Jun. "A language for financial chart patterns and template-based pattern classification." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3950603.
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Nguyen, Trong Nghia. "Deep Learning Based Statistical Models for Business and Financial Data." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/26944.
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We investigate a wide range of statistical models commonly used in many business and financial econometrics applications and propose flexible ways to combine these highly interpretable models with powerful predictive models in the deep learning literature to leverage the advantages and compensate the disadvantages of each of the modelling approaches. Our approaches of utilizing deep learning techniques for financial data are different from the recently proposed deep learning-based models in the financial econometrics literature in several perspectives. First, we do not overlook well-established structures that have been successfully used in statistical modelling. We flexibly incorporate deep learning techniques to the statistical models to capture the data effects that cannot be explained by the simple linear components of those models. Our proposed modelling frameworks therefore normally include two components: a linear part to explain linear dependencies and a deep learning-based part to capture data effects rather than linearity possibly exhibited in the underlying process. Second, we do not use the neural network structures in the same fashion as they are implemented in the deep learning literature but modify those black-box methods to make them more explainable and hence improve the interpretability of the proposed models. As the results, our hybrid models not only perform better than the pure deep learning techniques in term of interpretation but also often produce more accurate out-of-sample forecasts than the counterpart statistical frameworks. Third, we propose advanced Bayesian inference methodologies to efficiently quantify the uncertainty about the model estimation and prediction. For the proposed high dimensional deep learning-based models, performing efficient Bayesian inference is extremely challenging and is often ignored in the engineer-oriented papers, which generally prefer the frequentist estimation approaches mainly due to the simplicity.
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Forstinger, Sarah Verfasser], and Yuanhua [Verfasser] [Feng. "Modelling and forecasting financial and economic time series using different semiparametric ACD models / Sarah Forstinger, Yuanhua Feng." Paderborn : Universitätsbibliothek, 2018. http://d-nb.info/1168721490/34.
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Forstinger, Sarah [Verfasser], and Yuanhua [Verfasser] Feng. "Modelling and forecasting financial and economic time series using different semiparametric ACD models / Sarah Forstinger, Yuanhua Feng." Paderborn : Universitätsbibliothek, 2018. http://d-nb.info/1168721490/34.
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Rahmani, Mohammadsaeid. "Volatility Modelling Using Long-Memory- GARCH Models, Applications of S&P/TSX Composite Index." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/35064.
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The statements that include sufficient detail to identify the probability distributions of future prices are asset price dynamics. In this research, using the empirical methods that could explain the historical prices and discuss about how prices change we investigate various important characteristics of the dynamics of asset pricing. The volatility changes can explain very important facts about the asset returns. Volatility could gauge the variability of prices over time. In order to do the volatility modelling we use the conditional heteroskedasticitc models. One of the most powerful tools to do so is using the idea of autoregressive conditional heteroskedastic process or ARCH models, which fill the gap in both academic and practical literature.
In this work we detect the asymmetric volatility effect and investigate long memory properties in volatility in Canadian stock market index, using daily data from 1979 through 2015. On one hand, we show that there is an asymmetry in the equity market index. This is an important indication of how information impacts the market. On the other hand, we investigate for the long-range dependency in volatility and discuss how the shocks are persistence. By using the long memory-GARCH models, we not only take care of both short and long memory, but also we compute the d parameter that stands for the fractional decay of the series. By considering the breaks in our dataset, we compare our findings on different conditions to find the most suitable fit. We present the best fit for GARCH, EGARCH, APARCH, GJR-GARCH, FIGARCH, FIAPARCH, and FIEGARCH models.
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Zeng, Ning. "The usefulness of econometric models with stochastic volatility and long memory : applications for macroeconomic and financial time series." Thesis, Brunel University, 2009. http://bura.brunel.ac.uk/handle/2438/3903.
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This study aims to examine the usefulness of econometric models with stochastic volatility and long memory in the application of macroeconomic and financial time series. An ARFIMA-FIAPARCH process is used to estimate the two main parameters driving the degree of persistence in the US real interest rate and its uncertainty. It provides evidence that the US real interest rates exhibit dual long memory and suggests that much more attention needs to be paid to the degree of persistence and its consequences for the economic theories which are still inconsistent with the finding of either near-unit-root or long memory mean-reverting behavior. A bivariate GARCH-type of model with/without long-memory is constructed to concern the issue of temporal ordering of inflation, output growth and their respective uncertainties as well as all the possible causal relationships among the four variables in the US/UK, allowing several lags of the conditional variances/levels used as regressors in the mean/variance equations. Notably, the findings are quite robust to changes in the specification of the model. The applicability and out-of-sample forecasting ability of a multivariate constant conditional correlation FIAPARCH model are analysed through a multi-country study of national stock market returns. This multivariate specification is generally applicable once power, leverage and long-memory effects are taken into consideration. In addition, both the optimal fractional differencing parameter and power transformation are remarkably similar across countries.
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Silvennoinen, Annastiina. "Essays on autoregressive conditional heteroskedasticity." Doctoral thesis, Stockholm : Economic Research Institute, Stockholm School of Economics (EFI), 2006. http://www2.hhs.se/EFI/summary/711.htm.
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Bates, Brandon. "Essays in Financial Economics and Econometrics." Thesis, Harvard University, 2011. http://dissertations.umi.com/gsas.harvard:10419.
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In the first essay, I study the power of predictive regressions in a world of forecastable returns and find it to be quite poor. Using a simple model, I investigate the properties of short- and long-horizon regressions. The mechanisms biasing coefficients in short-horizon regressions differ from those affecting longer horizons. Further, I demonstrate that R\(^2s\) are biased and give an estimable bias correction. A calibration exercise shows sample lengths will be insufficient to determine what predicts asset returns until beyond the year 2100. The problem is not isolated to highly persistent predictors; even modestly persistent predictors have difficulties. Further, long-horizon regressions have inferior power relative to their single-period counterparts. These results present a predicament. If return predictability exists, then our ability to identify its source using predictive regressions alone is exceedingly poor. The second essay, written with James Stock and Mark Watson, considers the estimation of approximate dynamic factor models when there is temporal instability in the factors, factor loadings, and errors. We demonstrate that estimators for the factors and for the number of those factors are consistent for their population values even when affected by these instabilities. Further, we characterize the inferential theory in our framework for the estimated factors and for diffusion index forecasts and factor-augmented vector autoregressions that make use of the estimated factors. These results illustrate the broad robustness factor models have against temporal instability. In the third essay, co-author Peter Tufano and I consider the complex accounting rules, explicit fund sponsor supports, and government actions, that grant US money market mutual fund investors an implicit put option allowing them to redeem their shares at a fixed price of $1.00, regardless of the portfolio's market value. We describe the institutional features that generate these options, identify their writers, and estimate their premia. Using a hypothetical MMMF, we find that currently, non-redeeming shareholders, fund sponsors, and the government collectively bear annual premia of 22 to 44 basis points to give MMMF shareholders the right to redeem their shares at $1.00 rather than at the market value of the fund portfolio. These premia rose dramatically during the financial crisis, with the put value potentially being over 50 basis points.