Essentials of Time Series for Financial Applications

Essentials of Time Series for Financial Applications
Author: Massimo Guidolin,Manuela Pedio
Publsiher: Academic Press
Total Pages: 434
Release: 2018-05-29
Genre: Business & Economics
ISBN: 9780128134108

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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

Time Series
Author: Ngai Hang Chan
Publsiher: Wiley-Interscience
Total Pages: 203
Release: 2002
Genre: Business & Economics
ISBN: STANFORD:36105111770926

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This title gives both conceptual and practical illustrations of financial time series. Examples and discussions in the later chapters of the book make recent developments in time series more accessible. Examples from finance are maximized as much as possible throughout the book.

Multivariate Time Series Analysis and Applications

Multivariate Time Series Analysis and Applications
Author: William W. S. Wei
Publsiher: Wiley
Total Pages: 680
Release: 2019-02-26
Genre: Mathematics
ISBN: 9781119502852

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An essential guide on high dimensional multivariate time series including all the latest topics from one of the leading experts in the field Following the highly successful and much lauded book, Time Series Analysis—Univariate and Multivariate Methods, this new work by William W.S. Wei focuses on high dimensional multivariate time series, and is illustrated with numerous high dimensional empirical time series. Beginning with the fundamentalconcepts and issues of multivariate time series analysis,this book covers many topics that are not found in general multivariate time series books. Some of these are repeated measurements, space-time series modelling, and dimension reduction. The book also looks at vector time series models, multivariate time series regression models, and principle component analysis of multivariate time series. Additionally, it provides readers with information on factor analysis of multivariate time series, multivariate GARCH models, and multivariate spectral analysis of time series. With the development of computers and the internet, we have increased potential for data exploration. In the next few years, dimension will become a more serious problem. Multivariate Time Series Analysis and its Applications provides some initial solutions, which may encourage the development of related software needed for the high dimensional multivariate time series analysis. Written by bestselling author and leading expert in the field Covers topics not yet explored in current multivariate books Features classroom tested material Written specifically for time series courses Multivariate Time Series Analysis and its Applications is designed for an advanced time series analysis course. It is a must-have for anyone studying time series analysis and is also relevant for students in economics, biostatistics, and engineering.

Stochastic Calculus and Financial Applications

Stochastic Calculus and Financial Applications
Author: J. Michael Steele
Publsiher: Springer Science & Business Media
Total Pages: 302
Release: 2012-12-06
Genre: Mathematics
ISBN: 9781468493054

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Stochastic calculus has important applications to mathematical finance. This book will appeal to practitioners and students who want an elementary introduction to these areas. From the reviews: "As the preface says, ‘This is a text with an attitude, and it is designed to reflect, wherever possible and appropriate, a prejudice for the concrete over the abstract’. This is also reflected in the style of writing which is unusually lively for a mathematics book." --ZENTRALBLATT MATH

An Introduction to Analysis of Financial Data with R

An Introduction to Analysis of Financial Data with R
Author: Ruey S. Tsay
Publsiher: John Wiley & Sons
Total Pages: 416
Release: 2014-08-21
Genre: Business & Economics
ISBN: 9781119013464

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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.

Practical Time Series Analysis

Practical Time Series Analysis
Author: Dr. Avishek Pal,Dr. PKS Prakash
Publsiher: Packt Publishing Ltd
Total Pages: 244
Release: 2017-09-28
Genre: Computers
ISBN: 9781788294195

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Step by Step guide filled with real world practical examples. About This Book Get your first experience with data analysis with one of the most powerful types of analysis—time-series. Find patterns in your data and predict the future pattern based on historical data. Learn the statistics, theory, and implementation of Time-series methods using this example-rich guide Who This Book Is For This book is for anyone who wants to analyze data over time and/or frequency. A statistical background is necessary to quickly learn the analysis methods. What You Will Learn Understand the basic concepts of Time Series Analysis and appreciate its importance for the success of a data science project Develop an understanding of loading, exploring, and visualizing time-series data Explore auto-correlation and gain knowledge of statistical techniques to deal with non-stationarity time series Take advantage of exponential smoothing to tackle noise in time series data Learn how to use auto-regressive models to make predictions using time-series data Build predictive models on time series using techniques based on auto-regressive moving averages Discover recent advancements in deep learning to build accurate forecasting models for time series Gain familiarity with the basics of Python as a powerful yet simple to write programming language In Detail Time Series Analysis allows us to analyze data which is generated over a period of time and has sequential interdependencies between the observations. This book describes special mathematical tricks and techniques which are geared towards exploring the internal structures of time series data and generating powerful descriptive and predictive insights. Also, the book is full of real-life examples of time series and their analyses using cutting-edge solutions developed in Python. The book starts with descriptive analysis to create insightful visualizations of internal structures such as trend, seasonality and autocorrelation. Next, the statistical methods of dealing with autocorrelation and non-stationary time series are described. This is followed by exponential smoothing to produce meaningful insights from noisy time series data. At this point, we shift focus towards predictive analysis and introduce autoregressive models such as ARMA and ARIMA for time series forecasting. Later, powerful deep learning methods are presented, to develop accurate forecasting models for complex time series, and under the availability of little domain knowledge. All the topics are illustrated with real-life problem scenarios and their solutions by best-practice implementations in Python. The book concludes with the Appendix, with a brief discussion of programming and solving data science problems using Python. Style and approach This book takes the readers from the basic to advance level of Time series analysis in a very practical and real world use cases.

Essentials of Stochastic Finance

Essentials of Stochastic Finance
Author: Albert N. Shiryaev
Publsiher: World Scientific
Total Pages: 834
Release: 1999
Genre: Business & Economics
ISBN: 9789810236052

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Readership: Undergraduates and researchers in probability and statistics; applied, pure and financial mathematics; economics; chaos.

The Essentials of Machine Learning in Finance and Accounting

The Essentials of Machine Learning in Finance and Accounting
Author: Mohammad Zoynul Abedin,M. Kabir Hassan,Petr Hajek,Mohammed Mohi Uddin
Publsiher: Routledge
Total Pages: 258
Release: 2021-06-21
Genre: Business & Economics
ISBN: 9781000394122

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Th­is book introduces machine learning in finance and illustrates how we can use computational tools in numerical finance in real-world context. ­These computational techniques are particularly useful in financial risk management, corporate bankruptcy prediction, stock price prediction, and portfolio management. ­The book also offers practical and managerial implications of financial and managerial decision support systems and how these systems capture vast amount of financial data. Business risk and uncertainty are two of the toughest challenges in the financial industry. Th­is book will be a useful guide to the use of machine learning in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.

SAS for Forecasting Time Series Third Edition

SAS for Forecasting Time Series  Third Edition
Author: John C. Brocklebank, Ph.D.,David A. Dickey, Ph.D.,Bong Choi
Publsiher: SAS Institute
Total Pages: 384
Release: 2018-03-14
Genre: Computers
ISBN: 9781629605449

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To use statistical methods and SAS applications to forecast the future values of data taken over time, you need only follow this thoroughly updated classic on the subject. With this third edition of SAS for Forecasting Time Series, intermediate-to-advanced SAS users—such as statisticians, economists, and data scientists—can now match the most sophisticated forecasting methods to the most current SAS applications. Starting with fundamentals, this new edition presents methods for modeling both univariate and multivariate data taken over time. From the well-known ARIMA models to unobserved components, methods that span the range from simple to complex are discussed and illustrated. Many of the newer methods are variations on the basic ARIMA structures. Completely updated, this new edition includes fresh, interesting business situations and data sets, and new sections on these up-to-date statistical methods: ARIMA models Vector autoregressive models Exponential smoothing models Unobserved component and state-space models Seasonal adjustment Spectral analysis Focusing on application, this guide teaches a wide range of forecasting techniques by example. The examples provide the statistical underpinnings necessary to put the methods into practice. The following up-to-date SAS applications are covered in this edition: The ARIMA procedure The AUTOREG procedure The VARMAX procedure The ESM procedure The UCM and SSM procedures The X13 procedure The SPECTRA procedure SAS Forecast Studio Each SAS application is presented with explanation of its strengths, weaknesses, and best uses. Even users of automated forecasting systems will benefit from this knowledge of what is done and why. Moreover, the accompanying examples can serve as templates that you easily adjust to fit your specific forecasting needs. This book is part of the SAS Press program.

Pattern Recognition and Classification in Time Series Data

Pattern Recognition and Classification in Time Series Data
Author: Volna, Eva,Kotyrba, Martin,Janosek, Michal
Publsiher: IGI Global
Total Pages: 282
Release: 2016-07-22
Genre: Computers
ISBN: 9781522505662

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Patterns can be any number of items that occur repeatedly, whether in the behaviour of animals, humans, traffic, or even in the appearance of a design. As technologies continue to advance, recognizing, mimicking, and responding to all types of patterns becomes more precise. Pattern Recognition and Classification in Time Series Data focuses on intelligent methods and techniques for recognizing and storing dynamic patterns. Emphasizing topics related to artificial intelligence, pattern management, and algorithm development, in addition to practical examples and applications, this publication is an essential reference source for graduate students, researchers, and professionals in a variety of computer-related disciplines.

Handbook of Financial Time Series

Handbook of Financial Time Series
Author: Torben Gustav Andersen,Richard A. Davis,Jens-Peter Kreiß,Thomas V. Mikosch
Publsiher: Springer Science & Business Media
Total Pages: 1050
Release: 2009-04-21
Genre: Business & Economics
ISBN: 9783540712978

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The Handbook of Financial Time Series gives an up-to-date overview of the field and covers all relevant topics both from a statistical and an econometrical point of view. There are many fine contributions, and a preamble by Nobel Prize winner Robert F. Engle.

Essentials of Personal Financial Planning

Essentials of Personal Financial Planning
Author: Susan M. Tillery,Thomas N. Tillery
Publsiher: John Wiley & Sons
Total Pages: 456
Release: 2018-09-21
Genre: Business & Economics
ISBN: 9781119421207

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ESSENTIALS OF PERSONAL FINANCIAL PLANNING Essentials of Personal Financial Planning was written to challenge the status quo by promoting personal financial planning (PFP) as a profession, not as a sales tool to gather assets under management or facilitate sales of insurance products. The book takes a comprehensive and integrated approach to PFP for accounting students, allowing them to view the profession through the lens of a CPA – with integrity and objectivity. This book systematically introduces the essentials of all the major PFP topics (estate, retirement, investments, insurance, and tax), as well as: The PFP process, concepts and regulatory environment. Professional responsibilities of a CPA personal financial planner and the requirements of the Statement on Standards in PFP Services. Time value of money concepts. The book then builds on these foundational concepts, showing their interconnectivity and professional opportunities, to provide a deeper understanding of PFP and its application. After reading this book, students will be able to apply the knowledge and skills gained from this course to have an immediate and long-term positive impact for themselves and for the clients they serve.

Introduction to R for Quantitative Finance

Introduction to R for Quantitative Finance
Author: Gergely Daróczi,Michael Puhle,Edina Berlinger,Péter Csóka,Daniel Havran,Márton Michaletzky,Zsolt Tulassay,Kata Váradi,Agnes Vidovics-Dancs
Publsiher: Packt Publishing Ltd
Total Pages: 164
Release: 2013-11-22
Genre: Computers
ISBN: 9781783280940

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This book is a tutorial guide for new users that aims to help you understand the basics of and become accomplished with the use of R for quantitative finance.If you are looking to use R to solve problems in quantitative finance, then this book is for you. A basic knowledge of financial theory is assumed, but familiarity with R is not required. With a focus on using R to solve a wide range of issues, this book provides useful content for both the R beginner and more experience users.

Hands on Time Series Analysis with Python

Hands on Time Series Analysis with Python
Author: B V Vishwas,ASHISH PATEL
Publsiher: Apress
Total Pages: 407
Release: 2020-08-25
Genre: Computers
ISBN: 1484259912

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Learn the concepts of time series from traditional to bleeding-edge techniques. This book uses comprehensive examples to clearly illustrate statistical approaches and methods of analyzing time series data and its utilization in the real world. All the code is available in Jupyter notebooks. You'll begin by reviewing time series fundamentals, the structure of time series data, pre-processing, and how to craft the features through data wrangling. Next, you'll look at traditional time series techniques like ARMA, SARIMAX, VAR, and VARMA using trending framework like StatsModels and pmdarima. The book also explains building classification models using sktime, and covers advanced deep learning-based techniques like ANN, CNN, RNN, LSTM, GRU and Autoencoder to solve time series problem using Tensorflow. It concludes by explaining the popular framework fbprophet for modeling time series analysis. After reading Hands -On Time Series Analysis with Python, you'll be able to apply these new techniques in industries, such as oil and gas, robotics, manufacturing, government, banking, retail, healthcare, and more. What You'll Learn: · Explains basics to advanced concepts of time series · How to design, develop, train, and validate time-series methodologies · What are smoothing, ARMA, ARIMA, SARIMA,SRIMAX, VAR, VARMA techniques in time series and how to optimally tune parameters to yield best results · Learn how to leverage bleeding-edge techniques such as ANN, CNN, RNN, LSTM, GRU, Autoencoder to solve both Univariate and multivariate problems by using two types of data preparation methods for time series. · Univariate and multivariate problem solving using fbprophet. Who This Book Is For Data scientists, data analysts, financial analysts, and stock market researchers

Data Science for Economics and Finance

Data Science for Economics and Finance
Author: Sergio Consoli,Diego Reforgiato Recupero,Michaela Saisana
Publsiher: Springer Nature
Total Pages: 355
Release: 2021
Genre: Application software
ISBN: 9783030668914

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This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.