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Essentials of Time Series for Financial Applications
Author: Massimo Guidolin, Manuela Pedio
Publisher: Academic Press
ISBN: 0128134100
Pages: 434
Year: 2018-05-29
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Essentials of Time Series for Financial Applications serves as an agile reference for upper level students and practitioners who desire a formal, easy-to-follow introduction to the most important time series methods applied in financial applications (pricing, asset management, quant strategies, and risk management). Real-life data and examples developed with EViews illustrate the links between the formal apparatus and the applications. The examples either directly exploit the tools that EViews makes available or use programs that by employing EViews implement specific topics or techniques. The book balances a formal framework with as few proofs as possible against many examples that support its central ideas. Boxes are used throughout to remind readers of technical aspects and definitions and to present examples in a compact fashion, with full details (workout files) available in an on-line appendix. The more advanced chapters provide discussion sections that refer to more advanced textbooks or detailed proofs. Provides practical, hands-on examples in time-series econometrics Presents a more application-oriented, less technical book on financial econometrics Offers rigorous coverage, including technical aspects and references for the proofs, despite being an introduction Features examples worked out in EViews (9 or higher)
Time Series
Author: Ngai Hang Chan
Publisher: John Wiley & Sons
ISBN: 1118030710
Pages: 330
Year: 2011-01-25
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A new edition of the comprehensive, hands-on guide to financial time series, now featuring S-Plus® and R software Time Series: Applications to Finance with R and S-Plus®, Second Edition is designed to present an in-depth introduction to the conceptual underpinnings and modern ideas of time series analysis. Utilizing interesting, real-world applications and the latest software packages, this book successfully helps readers grasp the technical and conceptual manner of the topic in order to gain a deeper understanding of the ever-changing dynamics of the financial world. With balanced coverage of both theory and applications, this Second Edition includes new content to accurately reflect the current state-of-the-art nature of financial time series analysis. A new chapter on Markov Chain Monte Carlo presents Bayesian methods for time series with coverage of Metropolis-Hastings algorithm, Gibbs sampling, and a case study that explores the relevance of these techniques for understanding activity in the Dow Jones Industrial Average. The author also supplies a new presentation of statistical arbitrage that includes discussion of pairs trading and cointegration. In addition to standard topics such as forecasting and spectral analysis, real-world financial examples are used to illustrate recent developments in nonstandard techniques, including: Nonstationarity Heteroscedasticity Multivariate time series State space modeling and stochastic volatility Multivariate GARCH Cointegration and common trends The book's succinct and focused organization allows readers to grasp the important ideas of time series. All examples are systematically illustrated with S-Plus® and R software, highlighting the relevance of time series in financial applications. End-of-chapter exercises and selected solutions allow readers to test their comprehension of the presented material, and a related Web site features additional data sets. Time Series: Applications to Finance with R and S-Plus® is an excellent book for courses on financial time series at the upper-undergraduate and beginning graduate levels. It also serves as an indispensible resource for practitioners working with financial data in the fields of statistics, economics, business, and risk management.
Regression Modeling with Actuarial and Financial Applications
Author: Edward W. Frees
Publisher: Cambridge University Press
ISBN: 0521760119
Pages: 565
Year: 2010
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This book teaches multiple regression and time series and how to use these to analyze real data in risk management and finance.
Essentials of Stochastic Finance
Author: Albert N Shiryaev
Publisher: World Scientific
ISBN: 9814495662
Pages: 852
Year: 1999-01-15
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This important book provides information necessary for those dealing with stochastic calculus and pricing in the models of financial markets operating under uncertainty; introduces the reader to the main concepts, notions and results of stochastic financial mathematics; and develops applications of these results to various kinds of calculations required in financial engineering. It also answers the requests of teachers of financial mathematics and engineering by making a bias towards probabilistic and statistical ideas and the methods of stochastic calculus in the analysis of market risks. Contents:Facts. Models:Main Concepts, Structures, and Instruments. Aims and Problems of Financial Theory and Financial EngineeringStochastic Models. Discrete TimeStochastic Models. Continuous TimeStatistical Analysis of Financial DataTheory:Theory of Arbitrage in Stochastic Financial Models. Discrete TimeTheory of Pricing in Stochastic Financial Models. Discrete TimeTheory of Arbitrage in Stochastic Financial Models. Continuous TimeTheory of Pricing in Stochastic Financial Models. Continuous Time Readership: Undergraduates and researchers in probability and statistics; applied, pure and financial mathematics; economics; chaos. Keywords:Stochastic Finance;Financial Theory;Financial Engineering;Financial MathematicsReviews: “This is a remarkable text, containing a huge amount of interesting material on modern stochastic finance. Especially the young (novice) researcher in the field will find it a very useful basis of results essential for further research. The set of references is impressive and the level of writing is clear and pedagogically sound … a much more in-depth treatment of a very wide and encompassing range of stochastic models is given. In summary: a text to be recommended warmly.” International Statistical Institute “It is a very comprehensive survey of the results from the theories of stochastic processes, time series and related statistical procedures relevant to finance applications. It also develops classical pricing models and results. It is written in a very lively style, in which the author effortlessly jumps from abstract mathematical frameworks to interesting historical remarks.” Mathematical Reviews “The author's choice of material is outstanding and well worth the time and effort it will require to get through … For anyone interested or working in the field and who have a good mathematical background, this book will be a valuable resource and a rich and stimulating source of intellectual pleasure.” Journal of Applied Mathematics and Stochastic Analysis “… as an encyclopedia of results and methods for financial analysis it is very impressive and certainly very useful as well.” Mathematics Abstracts
Applied Nonlinear Time Series Analysis
Author: Michael Small
Publisher: World Scientific
ISBN: 9812567771
Pages: 245
Year: 2005
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Nonlinear time series methods have developed rapidly over a quarter of a century and have reached an advanced state of maturity during the last decade. Implementations of these methods for experimental data are now widely accepted and fairly routine; however, genuinely useful applications remain rare. This book focuses on the practice of applying these methods to solve real problems. To illustrate the usefulness of these methods, a wide variety of physical and physiological systems are considered. The technical tools utilized in this book fall into three distinct, but interconnected areas: quantitative measures of nonlinear dynamics, MonteOCoCarlo statistical hypothesis testing, and nonlinear modeling. Ten highly detailed applications serve as case studies of fruitful applications and illustrate the mathematical techniques described in the text."
An Introduction to Analysis of Financial Data with R
Author: Ruey S. Tsay
Publisher: John Wiley & Sons
ISBN: 1119013461
Pages: 416
Year: 2014-08-21
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A complete set of statistical tools for beginning financial analysts from a leading authority Written by one of the leading experts on the topic, An Introduction to Analysis of Financial Data with R explores basic concepts of visualization of financial data. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-world empirical research. The author supplies a hands-on introduction to the analysis of financial data using the freely available R software package and case studies to illustrate actual implementations of the discussed methods. The book begins with the basics of financial data, discussing their summary statistics and related visualization methods. Subsequent chapters explore basic time series analysis and simple econometric models for business, finance, and economics as well as related topics including: Linear time series analysis, with coverage of exponential smoothing for forecasting and methods for model comparison Different approaches to calculating asset volatility and various volatility models High-frequency financial data and simple models for price changes, trading intensity, and realized volatility Quantitative methods for risk management, including value at risk and conditional value at risk Econometric and statistical methods for risk assessment based on extreme value theory and quantile regression Throughout the book, the visual nature of the topic is showcased through graphical representations in R, and two detailed case studies demonstrate the relevance of statistics in finance. A related website features additional data sets and R scripts so readers can create their own simulations and test their comprehension of the presented techniques. An Introduction to Analysis of Financial Data with R is an excellent book for introductory courses on time series and business statistics at the upper-undergraduate and graduate level. The book is also an excellent resource for researchers and practitioners in the fields of business, finance, and economics who would like to enhance their understanding of financial data and today's financial markets.
Analysis of Integrated and Cointegrated Time Series with R
Author: Bernhard Pfaff
Publisher: Springer Science & Business Media
ISBN: 0387759670
Pages: 190
Year: 2008-09-03
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This book is designed for self study. The reader can apply the theoretical concepts directly within R by following the examples.
Analysis of Financial Time Series
Author: Ruey S. Tsay
Publisher: John Wiley & Sons
ISBN: 0471746185
Pages: 576
Year: 2005-09-15
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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.
GARCH Models
Author: Christian Francq, Jean-Michel Zakoian
Publisher: John Wiley & Sons
ISBN: 1119957397
Pages: 504
Year: 2011-06-24
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This book provides a comprehensive and systematic approach to understanding GARCH time series models and their applications whilst presenting the most advanced results concerning the theory and practical aspects of GARCH. The probability structure of standard GARCH models is studied in detail as well as statistical inference such as identification, estimation and tests. The book also provides coverage of several extensions such as asymmetric and multivariate models and looks at financial applications. Key features: Provides up-to-date coverage of the current research in the probability, statistics and econometric theory of GARCH models. Numerous illustrations and applications to real financial series are provided. Supporting website featuring R codes, Fortran programs and data sets. Presents a large collection of problems and exercises. This authoritative, state-of-the-art reference is ideal for graduate students, researchers and practitioners in business and finance seeking to broaden their skills of understanding of econometric time series models.
Multivariate Time Series Analysis
Author: Ruey S. Tsay
Publisher: John Wiley & Sons
ISBN: 1118617754
Pages: 520
Year: 2013-11-11
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An accessible guide to the multivariate time series tools used in numerous real-world applications Multivariate Time Series Analysis: With R and Financial Applications is the much anticipated sequel coming from one of the most influential and prominent experts on the topic of time series. Through a fundamental balance of theory and methodology, the book supplies readers with a comprehensible approach to financial econometric models and their applications to real-world empirical research. Differing from the traditional approach to multivariate time series, the book focuses on reader comprehension by emphasizing structural specification, which results in simplified parsimonious VAR MA modeling. Multivariate Time Series Analysis: With R and Financial Applications utilizes the freely available R software package to explore complex data and illustrate related computation and analyses. Featuring the techniques and methodology of multivariate linear time series, stationary VAR models, VAR MA time series and models, unitroot process, factor models, and factor-augmented VAR models, the book includes: • Over 300 examples and exercises to reinforce the presented content • User-friendly R subroutines and research presented throughout to demonstrate modern applications • Numerous datasets and subroutines to provide readers with a deeper understanding of the material Multivariate Time Series Analysis is an ideal textbook for graduate-level courses on time series and quantitative finance and upper-undergraduate level statistics courses in time series. The book is also an indispensable reference for researchers and practitioners in business, finance, and econometrics.
Essentials of Technical Analysis for Financial Markets
Author: James Chen
Publisher: John Wiley & Sons
ISBN: 0470537299
Pages: 283
Year: 2010-05-03
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Comprehensive, accessible guide to technical analysis and markettrading strategies Essentials of Technical Analysis for Financial Markets isan all-encompassing handbook on navigating the financial marketssuccessfully using technical analysis. Clearly written,easy-to-understand, and straightforward, this guide focuses on thekey information needed by traders and investors to take on anyfinancial market effectively. Easy-to-use, at-your-fingertips information on using technicalanalysis to trade all major financial markets Explains how to navigate the markets successfully, includingthe top techniques for entries, exits, and risk management Straightforward descriptions of proven technical tradingmethods and strategies Filled with technical analysis insights, charts, andexamples With financial markets in "roller coaster" mode, technicalanalysis offers a unique advantage for managing risk and findinghigh-probability trading opportunities. Packed with insightful tipsand guidance, Essentials of Technical Analysis for FinancialMarkets provides proven trading strategies from one of today'stop technical strategists.
Time-Series Prediction and Applications
Author: Amit Konar, Diptendu Bhattacharya
Publisher: Springer
ISBN: 3319545973
Pages: 242
Year: 2017-04-23
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This book presents machine learning and type-2 fuzzy sets for the prediction of time-series with a particular focus on business forecasting applications. It also proposes new uncertainty management techniques in an economic time-series using type-2 fuzzy sets for prediction of the time-series at a given time point from its preceding value in fluctuating business environments. It employs machine learning to determine repetitively occurring similar structural patterns in the time-series and uses stochastic automaton to predict the most probabilistic structure at a given partition of the time-series. Such predictions help in determining probabilistic moves in a stock index time-series Primarily written for graduate students and researchers in computer science, the book is equally useful for researchers/professionals in business intelligence and stock index prediction. A background of undergraduate level mathematics is presumed, although not mandatory, for most of the sections. Exercises with tips are provided at the end of each chapter to the readers’ ability and understanding of the topics covered.
Introduction to Modern Time Series Analysis
Author: Gebhard Kirchgässner, Jürgen Wolters, Uwe Hassler
Publisher: Springer Science & Business Media
ISBN: 3642334369
Pages: 320
Year: 2012-10-08
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This book presents modern developments in time series econometrics that are applied to macroeconomic and financial time series, bridging the gap between methods and realistic applications. It presents the most important approaches to the analysis of time series, which may be stationary or nonstationary. Modelling and forecasting univariate time series is the starting point. For multiple stationary time series, Granger causality tests and vector autogressive models are presented. As the modelling of nonstationary uni- or multivariate time series is most important for real applied work, unit root and cointegration analysis as well as vector error correction models are a central topic. Tools for analysing nonstationary data are then transferred to the panel framework. Modelling the (multivariate) volatility of financial time series with autogressive conditional heteroskedastic models is also treated.
The Basics of Financial Econometrics
Author: Frank J. Fabozzi, Sergio M. Focardi, Svetlozar T. Rachev, Bala G. Arshanapalli
Publisher: John Wiley & Sons
ISBN: 1118727231
Pages: 448
Year: 2014-03-04
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An accessible guide to the growing field of financial econometrics As finance and financial products have become more complex, financial econometrics has emerged as a fast-growing field and necessary foundation for anyone involved in quantitative finance. The techniques of financial econometrics facilitate the development and management of new financial instruments by providing models for pricing and risk assessment. In short, financial econometrics is an indispensable component to modern finance. The Basics of Financial Econometrics covers the commonly used techniques in the field without using unnecessary mathematical/statistical analysis. It focuses on foundational ideas and how they are applied. Topics covered include: regression models, factor analysis, volatility estimations, and time series techniques. Covers the basics of financial econometrics—an important topic in quantitative finance Contains several chapters on topics typically not covered even in basic books on econometrics such as model selection, model risk, and mitigating model risk Geared towards both practitioners and finance students who need to understand this dynamic discipline, but may not have advanced mathematical training, this book is a valuable resource on a topic of growing importance.
The Structural Econometric Time Series Analysis Approach
Author: Arnold Zellner, Franz C. Palm
Publisher: Cambridge University Press
ISBN: 1139453432
Pages:
Year: 2004-10-21
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Bringing together a collection of previously published work, this 2004 book provides a discussion of major considerations relating to the construction of econometric models that work well to explain economic phenomena, predict future outcomes and be useful for policy-making. Analytical relations between dynamic econometric structural models and empirical time series MVARMA, VAR, transfer function, and univariate ARIMA models are established with important application for model-checking and model construction. The theory and applications of these procedures to a variety of econometric modeling and forecasting problems as well as Bayesian and non-Bayesian testing, shrinkage estimation and forecasting procedures are also presented and applied. Finally, attention is focused on the effects of disaggregation on forecasting precision and the Marshallian Macroeconomic Model that features demand, supply and entry equations for major sectors of economies is analysed and described. This volume will prove invaluable to professionals, academics and students alike.

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