Modeling Financial Time Series with S-PLUS

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Author: Eric Zivot,Jiahui Wang

Publisher: Springer Science & Business Media

ISBN: 0387217630

Category: Business & Economics

Page: 632

View: 6102

The field of financial econometrics has exploded over the last decade 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. This 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 Second Edition is updated to cover S+FinMetrics 2.0 and includes new chapters on copulas, nonlinear regime switching models, continuous-time financial models, generalized method of moments, semi-nonparametric conditional density models, and the efficient method of moments. Eric Zivot is an associate professor and Gary Waterman Distinguished Scholar in the Economics Department, and adjunct associate professor of finance in the Business School at the University of Washington. He regularly teaches courses on econometric theory, financial econometrics and time series econometrics, and is the recipient of the Henry T. Buechel Award for Outstanding Teaching. He is an associate editor of Studies in Nonlinear Dynamics and Econometrics. He has published papers in the leading econometrics journals, including Econometrica, Econometric Theory, the Journal of Business and Economic Statistics, Journal of Econometrics, and the Review of Economics and Statistics. Jiahui Wang is an employee of Ronin Capital LLC. He received a Ph.D. in Economics from the University of Washington in 1997. He has published in leading econometrics journals such as Econometrica and Journal of Business and Economic Statistics, and is the Principal Investigator of National Science Foundation SBIR grants. In 2002 Dr. Wang was selected as one of the "2000 Outstanding Scholars of the 21st Century" by International Biographical Centre.

Zeitreihenmodelle

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Author: Andrew C. Harvey

Publisher: De Gruyter Oldenbourg

ISBN: 9783486230062

Category:

Page: 379

View: 7018

Gegenstand des Werkes sind Analyse und Modellierung von Zeitreihen. Es wendet sich an Studierende und Praktiker aller Disziplinen, in denen Zeitreihenbeobachtungen wichtig sind.

Analysis of Financial Time Series

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Author: Ruey S. Tsay

Publisher: John Wiley & Sons

ISBN: 9781118017098

Category: Mathematics

Page: 720

View: 798

This book provides a broad, mature, 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 Bayesian inference in finance methods Key features of the new edition include additional coverage of modern day topics such as arbitrage, pair trading, realized volatility, and credit risk modeling; a smooth transition from S-Plus to R; and expanded empirical financial data sets. 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.

Time Series

Applications to Finance with R and S-Plus

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Author: Ngai Hang Chan

Publisher: John Wiley & Sons

ISBN: 1118030710

Category: Mathematics

Page: 330

View: 494

A new edition of the comprehensive, hands-on guide to financialtime 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 tothe conceptual underpinnings and modern ideas of time seriesanalysis. Utilizing interesting, real-world applications and thelatest software packages, this book successfully helps readersgrasp the technical and conceptual manner of the topic in order togain a deeper understanding of the ever-changing dynamics of thefinancial world. With balanced coverage of both theory and applications, thisSecond Edition includes new content to accurately reflect thecurrent state-of-the-art nature of financial time series analysis.A new chapter on Markov Chain Monte Carlo presents Bayesian methodsfor time series with coverage of Metropolis-Hastings algorithm,Gibbs sampling, and a case study that explores the relevance ofthese techniques for understanding activity in the Dow JonesIndustrial Average. The author also supplies a new presentation ofstatistical arbitrage that includes discussion of pairs trading andcointegration. In addition to standard topics such as forecastingand spectral analysis, real-world financial examples are used toillustrate 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 tograsp the important ideas of time series. All examples aresystematically illustrated with S-Plus® and R software,highlighting the relevance of time series in financialapplications. End-of-chapter exercises and selected solutions allowreaders to test their comprehension of the presented material, anda related Web site features additional data sets. Time Series: Applications to Finance with R and S-Plus® isan excellent book for courses on financial time series at theupper-undergraduate and beginning graduate levels. It also servesas an indispensible resource for practitioners working withfinancial data in the fields of statistics, economics, business,and risk management.

Ökonometrie für Dummies

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Author: Roberto Pedace

Publisher: John Wiley & Sons

ISBN: 3527801529

Category: Business & Economics

Page: 388

View: 6566

Theorien verstehen und Techniken anwenden Was haben die Gehälter von Spitzensportlern und der Mindestlohn gemeinsam? Richtig, man kann sie mit Ökonometrie erforschen. Im Buch steht, wie es geht. Und nicht nur dafür, sondern für viele weitere Gebiete lohnt es sich, der zunächst etwas trocken und sperrig anmutenden Materie eine Chance zu geben. Lernen Sie von den Autoren, wie Sie spannende Fragen formulieren, passende Variablen festlegen, treffsichere Modelle entwerfen und Ihre Aussagen auf Herz und Nieren prüfen. Werden Sie sicher im Umgang mit Hypothesentests, Regressionsmodellen, Logit- & Probit-Modellen und allen weiteren gängigen Methoden der Ökonometrie. So begleitet Ökonometrie für Dummies Sie Schritt für Schritt und mit vielen Beispielen samt R Output durch dieses spannende Thema.

Modeling Techniques in Predictive Analytics with Python and R

A Guide to Data Science

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Author: Thomas W. Miller

Publisher: FT Press

ISBN: 013389214X

Category: Computers

Page: 448

View: 2750

Master predictive analytics, from start to finish Start with strategy and management Master methods and build models Transform your models into highly-effective code—in both Python and R This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math. Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work—and maximize their value. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code. If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more. All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you’re new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you’re already a modeler, programmer, or manager, you’ll learn crucial skills you don’t already have. Using Python and R, Miller addresses multiple business challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic code that delivers actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods. Use Python and R to gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more

Fight Club

Roman

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Author: Chuck Palahniuk

Publisher: Goldmann Verlag

ISBN: 3894808357

Category: Fiction

Page: 256

View: 9317

Sie sind jung, sie sind stark – und sie sind gelangweilt: Normale, berufstätige Männer und Familienväter auf der Suche nach einem Mittel gegen die Leere in ihrem Leben. Sie treffen sich auf Parkplätzen und in Kellern von Bars, um mit nackten Fäusten gegeneinander zu kämpfen. Der Anführer dieser „Fight Clubs“ ist Tyler Durden, und er ist besessen von dem Plan, furchtbare Rache an einer Welt zu nehmen, in der es keine menschliche Wärme mehr gibt ...

Modeling Techniques in Predictive Analytics

Business Problems and Solutions with R, Revised and Expanded Edition

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Author: Thomas W. Miller

Publisher: FT Press

ISBN: 0133886190

Category: Computers

Page: 384

View: 8999

To succeed with predictive analytics, you must understand it on three levels: Strategy and management Methods and models Technology and code This up-to-the-minute reference thoroughly covers all three categories. Now fully updated, this uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you’re new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you’re already a modeler, programmer, or manager, it will teach you crucial skills you don’t yet have. Unlike competitive books, this guide illuminates the discipline through realistic vignettes and intuitive data visualizations–not complex math. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R code, interpreting results, and more. Every chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work–and maximize their value. Reflecting extensive student and instructor feedback, this edition adds five classroom-tested case studies, updates all code for new versions of R, explains code behavior more clearly and completely, and covers modern data science methods even more effectively. All data sets, extensive R code, and additional examples available for download at http://www.ftpress.com/miller If you want to make the most of predictive analytics, data science, and big data, this is the book for you. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business cases and challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic R programs that deliver actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Throughout, Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. This edition adds five new case studies, updates all code for the newest versions of R, adds more commenting to clarify how the code works, and offers a more detailed and up-to-date primer on data science methods. Gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more

Mathematische Statistik

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Author: Bartel L. van der Waerden

Publisher: Springer-Verlag

ISBN: 3642649742

Category: Mathematics

Page: 360

View: 5277

Caves and Karst Across Time

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Author: Yongli Gao,E. Calvin Alexander Jr.

Publisher: Geological Society of America

ISBN: 081372516X

Category: Caves

Page: 300

View: 6347

"Knowledge and understanding of cave and karst systems have evolved dramatically since the creation of the Geological Society of America in 1888. This book, which came out of a session during GSA's 2013 Annual Meeting, highlights the changes in the study and application of cave and karst systems since GSA's origin, while looking ahead to future advancements"--

Einführung in die Statistik der Finanzmärkte

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Author: Jürgen Franke,Wolfgang Karl Härdle,Christian Matthias Hafner

Publisher: Springer-Verlag

ISBN: 3642170498

Category: Business & Economics

Page: 428

View: 2521

Modelle der Zeitreihenanalyse

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Author: Manfred Deistler,Wolfgang Scherrer

Publisher: Springer-Verlag

ISBN: 331968664X

Category: Mathematics

Page: 159

View: 5614

Dieses Buch bietet eine einheitliche und geschlossene Darstellung von Theorie und Modellen, die der Zeitreihenanalyse zugrunde liegen. Das Schwergewicht liegt dabei beim schwach stationären Fall und bei linearen Modellen: Im ersten Teil wird die Theorie allgemeiner multivariater schwach stationärer Prozesse in Zeit-und Frequenzbereich, einschließlich deren Prognose und Filterung hergeleitet. Der zweite Teil beschäftigt sich mit multivariaten AR-, ARMA- und Zustandsraum-Systemen als den wichtigsten Modellklassen für stationäre Prozesse. In diesem Rahmen werden Yule-Walker Gleichungen, die Faktorisierung rationaler Spektren, das Kalman Filter und die Struktur von ARMA-und Zustandsraum-Systemen beschrieben. Ziel des Buches ist es die wesentlichen Konzepte, Ideen, Methoden und Resultate in mathematisch sauberer Form darzustellen und somit eine solide Fundierung für Studenten und Forscher in Feldern wie datengetriebener Modellierung, Prognose und Filterung, wie sie etwa für die Kontrolltheorie, Ökonometrie, Signalverarbeitung und Statistik relevant sind, zu bieten.

MPI - Eine Einführung

Portable parallele Programmierung mit dem Message-Passing Interface

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Author: William Gropp,Ewing Lusk,Anthony Skjellum

Publisher: Walter de Gruyter GmbH & Co KG

ISBN: 3486841009

Category: Computers

Page: 387

View: 6987

Message Passing Interface (MPI) ist ein Protokoll, das parallel Berechnungen auf verteilten, heterogenen, lose-gekoppelten Computersystemen ermöglicht.

Einführung in die Zeitreihenanalyse

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Author: Jens-Peter Kreiß,Georg Neuhaus

Publisher: Springer-Verlag

ISBN: 3540335714

Category: Mathematics

Page: 388

View: 1268

Das Buch führt in die grundlegenden Bereiche der klassischen Zeitreihenanalyse ein. Deshalb spielen in den ersten Kapiteln die Begriffe Stationarität und Autokovarianz- bzw. Autokorrelationsstruktur eine wesentliche Rolle. Ergänzend zu den grundlegenden Modellen werden aber auch schon zu Beginn eine Reihe von Beispielen diskutiert. Mit Hilfe des Spektralsatzes und der Filterung stationärer Zeitreihen kann die wichtige Klasse der ARMA-Modelle sehr effizient und erschöpfend behandelt werden. Die asymptotischen Resultate des Textes beruhen auf einem zentralen Grenzwertresultat für sog. schwach abhängige Zufallsvariable. Es zeigt sich, dass dieses Resultat sowohl die Behandlung linearer Zeitreihenmodelle wie gewisser nichtlinearer und für den Bereich der Finanzzeitreihen wichtiger Zeitreihen erlaubt. Im Weiteren werden dann Schätzmethoden im Spektralbereich von Zeitreihen diskutiert. Neben dem Periodogram werden ebenso auch sog. geglättete Spektraldichteschätzer vollständig behandelt. Kapitel über Modellwahlverfahren und die wesentlichen Grundlagen multivariater Zeitreihen sowie einiger Anhänge, die den Text weitestgehend autark lesbar machen sollen, schließen das Buch ab.

ANALYSIS OF FINANCIAL TIME SERIES, 2ND ED

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Author: Ruey S. Tsay

Publisher: N.A

ISBN: 9788126523696

Category:

Page: 628

View: 6479

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.

Überflieger

Warum manche Menschen erfolgreich sind - und andere nicht

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Author: Malcolm Gladwell

Publisher: Campus Verlag

ISBN: 3593405016

Category: Political Science

Page: 272

View: 1209

Malcolm Gladwell, Bestsellerautor und Star des amerikanischen Buchmarkts, hat die wahren Ursachen des Erfolgs untersucht und darüber ein lehrreiches, faszinierendes Buch geschrieben. Es steckt voller Geschichten und Beispiele, die zeigen, dass auch außergewöhnlicher Erfolg selten etwas mit individuellen Eigenschaften zu tun hat, sondern mit Gegebenheiten, die es dem einen leicht und dem anderen unmöglich machen, erfolgreich zu sein. Die Frage ist nicht, wie jemand ist, sondern woher er kommt: Welche Bedingungen haben diesen Menschen hervorgebracht? Auf seiner anregenden intellektuellen Erkundung der Welt der Überflieger erklärt Gladwell unter anderem das Geheimnis der Softwaremilliardäre, wie man ein herausragender Fußballer wird, warum Asiaten so gut in Mathe sind und was die Beatles zur größten Band aller Zeiten machte.