Download or read book Essays on Multivariate Volatility and Dependence Models for Financial Time Series written by Diaa Noureldin and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Volatility and Time Series Econometrics written by Tim Bollerslev and published by OUP Oxford. This book was released on 2010-02-11 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robert Engle received the Nobel Prize for Economics in 2003 for his work in time series econometrics. This book contains 16 original research contributions by some the leading academic researchers in the fields of time series econometrics, forecasting, volatility modelling, financial econometrics and urban economics, along with historical perspectives related to field of time series econometrics more generally. Engle's Nobel Prize citation focuses on his path-breaking work on autoregressive conditional heteroskedasticity (ARCH) and the profound effect that this work has had on the field of financial econometrics. Several of the chapters focus on conditional heteroskedasticity, and develop the ideas of Engle's Nobel Prize winning work. Engle's work has had its most profound effect on the modelling of financial variables and several of the chapters use newly developed time series methods to study the behavior of financial variables. Each of the 16 chapters may be read in isolation, but they all importantly build on and relate to the seminal work by Nobel Laureate Robert F. Engle.
Download or read book Essays on Time Series Forecasting with Neural network Or Long dependence Autoregressive Models and Macroeconomic News Effects on Bond Yields written by Morvan Nongni Donfack and published by . This book was released on 2022 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis, organized in three chapters, focuses on modelling and forecasting economic and financial time series. The first two chapters propose new econometric models for analysing economic and financial data by relaxing unrealistic assumptions usually made in the literature. Chapter 1 develops a new volatility model named TVP[subscript ANN]-GARCH. The model offers rich dynamics to model financial data by allowing for a generalized autoregressive conditional heteroscedasticity (GARCH) structure in which parameters vary over time according to an artificial neural network (ANN). The use of ANNs for parameters dynamics is a valuable contribution as it helps to deal with the problem of likelihood evaluation (exhibited in time-varying parameters (TVP) models). It also allows for the use of additional explanatory variables. The chapter develops an original and efficient Sequential Monte Carlo sampler (SMC) to estimate the model. An empirical application shows that the model favourably compares to popular volatility processes in terms of out-of sample fit. The approach can easily be extended to any fixed-parameters model. Chapter 2 develops three parsimonious autoregressive (AR) lag polynomials that generate slowly decaying autocorrelation functions as generally observed financial and economic time series. The dynamics of the lag polynomials are similar to that of two well performing processes, namely the Markov-Switching Multifractal (MSM) and the Factorial Hidden Markov Volatility (FHMV) models. They are very flexible as they can be applied in many popular models such as ARMA, GARCH, and stochastic volatility processes. An empirical analysis highlights the usefulness of the lag polynomials for conditional mean and volatility forecasting. They could be considered as forecasting alternatives for economic and financial time series. The last chapter relies on a two steps predictive regression approach to identify the impact of US macroeconomic releases on three small open economies (Canada, United Kingdom, and Sweden) bond yields at high and low frequencies. Our findings suggest that US macro news are significantly more important in explaining yield curve dynamics in small open economies (SOEs) than domestic news itself. Not only US monetary policy news are important drivers of SOEs bond yield changes, but business cycle news also play a significant role.
Download or read book Modeling Financial Time Series with S PLUS written by Eric Zivot and published by Springer Science & Business Media. This book was released on 2007-10-10 with total page 998 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book represents an integration of theory, methods, and examples using the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the practice of financial econometrics. It is the first book to show the power of S-PLUS for the analysis of time series data. It is written for researchers and practitioners in the finance industry, academic researchers in economics and finance, and advanced MBA and graduate students in economics and finance. Readers are assumed to have a basic knowledge of S-PLUS and a solid grounding in basic statistics and time series concepts. This edition covers S+FinMetrics 2.0 and includes new chapters.
Download or read book Handbook of Volatility Models and Their Applications written by Luc Bauwens and published by John Wiley & Sons. This book was released on 2012-03-22 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt: A complete guide to the theory and practice of volatility models in financial engineering Volatility has become a hot topic in this era of instant communications, spawning a great deal of research in empirical finance and time series econometrics. Providing an overview of the most recent advances, Handbook of Volatility Models and Their Applications explores key concepts and topics essential for modeling the volatility of financial time series, both univariate and multivariate, parametric and non-parametric, high-frequency and low-frequency. Featuring contributions from international experts in the field, the book features numerous examples and applications from real-world projects and cutting-edge research, showing step by step how to use various methods accurately and efficiently when assessing volatility rates. Following a comprehensive introduction to the topic, readers are provided with three distinct sections that unify the statistical and practical aspects of volatility: Autoregressive Conditional Heteroskedasticity and Stochastic Volatility presents ARCH and stochastic volatility models, with a focus on recent research topics including mean, volatility, and skewness spillovers in equity markets Other Models and Methods presents alternative approaches, such as multiplicative error models, nonparametric and semi-parametric models, and copula-based models of (co)volatilities Realized Volatility explores issues of the measurement of volatility by realized variances and covariances, guiding readers on how to successfully model and forecast these measures Handbook of Volatility Models and Their Applications is an essential reference for academics and practitioners in finance, business, and econometrics who work with volatility models in their everyday work. The book also serves as a supplement for courses on risk management and volatility at the upper-undergraduate and graduate levels.
Download or read book Essays on Multivariate Volatility Models written by Trung Thanh Le and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis is an empirical study of how multivariate models can be applied to analyze the dependence between emerging financial markets and the US financial market. This thesis comprises of 3 complete papers which will use this data set as follows. The first paper is an comparative research on estimations and evaluations of 54 individual volatility models which belong to 10 different model classes being the Riskmetrics models, the Constant model (CCC), the Orthogonal-GARCH model (O-GARCH), the Dynamic Conditional Correlation model (DCC), the Asymmetric DCC model (ADCC), the Consistent DCC model (CDCC) and the Student's t-DCC model (TDCC). All of these models were estimated and then ranked by using both in-sample and out of sample performances. This research is to emphasize the importance of model selection in modeling the volatility of financial time series from emerging financial markets. The second paper uses the TDCC model which performed relatively well among the 54 volatility of financial time series from emerging financial markets. The second paper uses the TDCC model which performed relatively well among the 54 volatility models to analyze the volatilities and correlations of the emerging markets. Specifically, the pair-wise conditional correlations between each of the emerging markets and the US market, generated by the TDCC model, were used to perform empirical tests for the contagion of the 3 recent financial crises which are the Dotcom crisis in 2000, the Sub-prime in 2007-2008 and the Global financial crisis in 2008-2009. The use of the TDCC model which assumes a Student's t-distribution is greatly meaningful for the empirical tests for contagion as it deals with the fat-tailed behaviours of the financial data. The third paper is the application of multivariate copula, which provides a connection between the univariate distributions and the multivariate distribution inside the DCC model, to analyze the emerging data. The flexibility of the copula model that separates the multivariate distribution assumption from those univariate series allows us to have an efficient examination of the dependence structure of emerging financial markets. Following success of the copula models in recent studies, our research, which is the first to use the copula model to analyze high-dimensional data, confirms a significant improvement of the copula from the standard DCC model.
Download or read book Multivariate GARCH and Dynamic Copula Models for Financial Time Series written by Martin Grziska and published by Pro BUSINESS. This book was released on 2015-02-05 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 moresuitable to diversify a portfolio consisting of developed equity or bond indices than proprietary.
Download or read book Essays on Financial Time Series written by Isao Ishida and published by . This book was released on 2004 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Essays on Financial Return and Volatility Modeling written by Jing Wu (Ph. D.) and published by . This book was released on 2012 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: My dissertation consists of three essays focusing on modeling financial asset return and volatility. The first essay proposes a threshold GARCH model to describe the regimeswitching in volatility dynamics of financial asset returns. In the threshold model the switching of regimes is triggered by an observable threshold variable, while volatility follows a GARCH process within each regime. This model can be viewed as a special case of the random coefficient GARCH model. We establish theoretical conditions, which ensure that the return process in the threshold model is strictly stationary, as well as conditions for the existence of finite variance and fourth moment. A simulation study is further conducted to examine the finite sample properties of the maximum likelihood estimator. The second essay extends our study of the threshold GARCH model to an empirical application. The empirical results support the use of the threshold variable to identify the regime shifts in the volatility process. Especially when VIX is used as the threshold, we are able to separate the clustering of volatile periods corresponding to various financial crises. According to 5 common measures on forecasting evaluation, the threshold GARCH model provides better volatility forecasts for stocks as well as currency exchange rates. The third essay examines the effect of time structure on the estimation performance of independent component analysis (ICA) models and provides guidance in applying the ICA model to time series data. We compare the performance of the basic ICA model to the time series ICA model in which the cross-autocovariances are used as a measure to achieve independence. We conduct a simulation study to evaluate the time series ICA model under different time structure assumptions about the underlying components that generate financial time series. Moreover, the empirical results support the use of the time series ICA model.
Download or read book ANALYSIS OF FINANCIAL TIME SERIES 2ND ED written by Ruey S. Tsay and published by . This book was released on 2009-01-01 with total page 628 pages. Available in PDF, EPUB and Kindle. Book excerpt: Market_Desc: Ideal as a fundamental introduction to time series for MBA students or as a reference for researchers and practitioners in business and finance Special Features: · Timely topics and recent results include: Value at Risk (VaR); high-frequency financial data analysis; MCMC methods; derivative pricing using jump diffusion with closed-form formulas; VaR calculation using extreme value theory based on nonhomogeneous two-dimensional Poisson process; and multivariate volatility models with time-varying correlations.· New topics to this edition include: Finmetrics in S-plus; estimation of stochastic diffusion equations for derivative pricing; use of realized volatilities; state=space model; and Kalman filter.· The second edition also includes new developments in financial econometrics and more examples of applications in finance.· Emphasis is placed on empirical financial data.· Chapter exercises have been increased in an effort to further reinforce the methods and applications in the text. About The Book: This book provides a comprehensive and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described. The author begins with basic characteristics of financial time series data before covering three main topics: analysis and application of univariate financial time series; the return series of multiple assets; and Bayesian inference in finance methods. The overall objective of the book is to provide some knowledge of financial time series, introduce some statistical tools useful for analyzing these series, and gain experience in financial applications of various econometric methods.
Download or read book Essays on Multivariate Modelling of Financial Markets Using Copula and Sentiment Networks written by Anastasija Tetereva and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multivariate dependence structures play an important role in finance. The modelling and accurate prediction of multivariate financial time series is an important component of asset pricing and portfolio management. This doctoral thesis comprises three essays that address the question of multivariate dependencies using high-frequency data and innovative sources of information such as news analytics. These essays make complementary contributions to the field of financial econometrics and can be read independently of each other. The first essay focuses on the improvement of Value at Risk prediction based on highfrequency data. The novel concept of the realized hierarchical Archimedean copula is introduced. It is proposed estimating the structure and the parameters of the hierarchical Archimedean copula using the realized correlation matrix only. This approach allows one to estimate the multivariate distribution of daily returns based on intraday information. Moreover, the proposed estimator does not suffer from the curse of dimensionality. In this essay, the realized hierarchical Archimedean copula is applied to manage the risk of high-dimensional portfolios. The evidence of the superior forecasting power of our approach, compared to a set of existing models, is provided. The second essay investigates the role of news sentiment data in improving forecasts in financial econometrics. The objective of this paper is to answer the question regarding whether the class of stock-price-relevant news is wider than firm-specific announcements. For this purpose, causal links between news sentiments and excess returns are studied by means of an adaptive lasso. It is concluded that unexpected returns in the whole economy can be explained by news originating from the financial and energy sectors. In other words, the news spillover effects are dominating the direct effects of sectoral news. Therefore, including exogenous financial or energy sentim.
Download or read book Essays on Forecasting the Multivariate Variance covariance Matrix written by Robert O'Neill and published by . This book was released on 2011 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Essays on Multivariate Stochastic Volatility Models written by Sebastian Trojan and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first essay describes a very general stochastic volatility (SV) model specification with leverage, heavy tails, skew and switching regimes, using realized volatility (RV) as an auxiliary time series to improve inference on latent volatility. The information content of the range and of implied volatility using the VIX index is also analyzed. Database is the S & P 500 index. Asymmetry in the observation error is modeled by the generalized hyperbolic skew Student-t distribution, whose heavy and light tail enable substantial skewness. Resulting number of regimes and dynamics differ dependent on the auxiliary volatility proxy and are investigated in-sample for the financial crash period 2008/09 in more detail. An out-of-sample study comparing predictive ability of various model variants for a calm and a volatile period yields insights about the gains on forecasting performance from different volatility proxies. Results indicate that including RV or the VIX pays off mostly in more volatile market conditions, whereas in calmer environments SV specifications using no auxiliary series outperform. The range as volatility proxy provides a superior in-sample fit, but its predictive performance is found to be weak. The second essay presents a high frequency stochastic volatility model. Price duration and associated absolute price change in event time are modeled contemporaneously to fully capture volatility on the tick level, combining the SV and stochastic conditional duration (SCD) model. Estimation is with IBM stock intraday data 2001/10 (decimalization completed), taking a minimum midprice threshold of a half tick. Persistent information flow is extracted, featuring a positively correlated innovation term and negative cross effects in the AR(1) persistence matrix. Additionally, regime switching in both duration and absolute price change is introduced to increase nonlinear capabilities of the model. Thereby, a separate price jump.
Download or read book Three Essays in Bayesian Financial Econometrics written by Xin Jin and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Dynamic Models for Volatility and Heavy Tails written by Andrew C. Harvey and published by Cambridge University Press. This book was released on 2013-04-22 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: The volatility of financial returns changes over time and, for the last thirty years, Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models have provided the principal means of analyzing, modeling and monitoring such changes. Taking into account that financial returns typically exhibit heavy tails - that is, extreme values can occur from time to time - Andrew Harvey's new book shows how a small but radical change in the way GARCH models are formulated leads to a resolution of many of the theoretical problems inherent in the statistical theory. The approach can also be applied to other aspects of volatility. The more general class of Dynamic Conditional Score models extends to robust modeling of outliers in the levels of time series and to the treatment of time-varying relationships. The statistical theory draws on basic principles of maximum likelihood estimation and, by doing so, leads to an elegant and unified treatment of nonlinear time-series modeling.
Download or read book Analysis of Financial Time Series written by Ruey S. Tsay and published by Wiley-Interscience. This book was released on 2005-09-15 with total page 576 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides statistical tools and techniques needed to understand today's financial markets The Second Edition of this critically acclaimed text provides a comprehensive and systematic introduction to financial econometric models and their applications in modeling and predicting financial time series data. This latest edition continues to emphasize empirical financial data and focuses on real-world examples. Following this approach, readers will master key aspects of financial time series, including volatility modeling, neural network applications, market microstructure and high-frequency financial data, continuous-time models and Ito's Lemma, Value at Risk, multiple returns analysis, financial factor models, and econometric modeling via computation-intensive methods. The author begins with the basic characteristics of financial time series data, setting the foundation for the three main topics: Analysis and application of univariate financial time series Return series of multiple assets Bayesian inference in finance methods This new edition is a thoroughly revised and updated text, including the addition of S-Plus® commands and illustrations. Exercises have been thoroughly updated and expanded and include the most current data, providing readers with more opportunities to put the models and methods into practice. Among the new material added to the text, readers will find: Consistent covariance estimation under heteroscedasticity and serial correlation Alternative approaches to volatility modeling Financial factor models State-space models Kalman filtering Estimation of stochastic diffusion models The tools provided in this text aid readers in developing a deeper understanding of financial markets through firsthand experience in working with financial data. This is an ideal textbook for MBA students as well as a reference for researchers and professionals in business and finance.
Download or read book Essays on Financial Time Series Models written by Jonas Andersson and published by . This book was released on 1999 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt:
