Essentials Of Time Series For Financial Applications

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Essentials of Time Series for Financial Applications

Essentials of Time Series for Financial Applications
  • Author : Massimo Guidolin,Manuela Pedio
  • Publisher : Academic Press
  • Release Date : 2018-05-29
  • Total pages : 434
  • ISBN : 9780128134108
  • File Size : 35,7 Mb
  • Total Download : 336
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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
  • Publisher : Wiley-Interscience
  • Release Date : 2002
  • Total pages : 203
  • ISBN : STANFORD:36105111770926
  • File Size : 32,5 Mb
  • Total Download : 329
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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.

Essentials of Time Series for Financial Applications

Essentials of Time Series for Financial Applications
  • Author : Massimo Guidolin,Manuela Pedio
  • Publisher : Academic Press
  • Release Date : 2018-06-14
  • Total pages : 434
  • ISBN : 0128134097
  • File Size : 17,7 Mb
  • Total Download : 407
  • DOWNLOAD BOOK

Download Essentials of Time Series for Financial Applications in PDF, Epub, and Kindle

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)

Multivariate Time Series Analysis and Applications

Multivariate Time Series Analysis and Applications
  • Author : William W. S. Wei
  • Publisher : John Wiley & Sons
  • Release Date : 2019-03-18
  • Total pages : 536
  • ISBN : 9781119502852
  • File Size : 17,9 Mb
  • Total Download : 145
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Download Multivariate Time Series Analysis and Applications in PDF, Epub, and Kindle

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.

ARCH Models and Financial Applications

ARCH Models and Financial Applications
  • Author : Christian Gourieroux
  • Publisher : Springer Science & Business Media
  • Release Date : 2012-12-06
  • Total pages : 229
  • ISBN : 9781461218609
  • File Size : 18,9 Mb
  • Total Download : 817
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The classical ARMA models have limitations when applied to the field of financial and monetary economics. Financial time series present nonlinear dynamic characteristics and the ARCH models offer a more adaptive framework for this type of problem. This book surveys the recent work in this area from the perspective of statistical theory, financial models, and applications and will be of interest to theorists and practitioners. From the view point of statistical theory, ARCH models may be considered as specific nonlinear time series models which allow for an exhaustive study of the underlying dynamics. It is possible to reexamine a number of classical questions such as the random walk hypothesis, prediction interval building, presence of latent variables etc., and to test the validity of the previously studied results. There are two main categories of potential applications. One is testing several economic or financial theories concerning the stocks, bonds, and currencies markets, or studying the links between the short and long run. The second is related to the interventions of the banks on the markets, such as choice of optimal portfolios, hedging portfolios, values at risk, and the size and times of block trading.

Regression Modeling with Actuarial and Financial Applications

Regression Modeling with Actuarial and Financial Applications
  • Author : Edward W. Frees
  • Publisher : Cambridge University Press
  • Release Date : 2010
  • Total pages : 585
  • ISBN : 9780521760119
  • File Size : 32,6 Mb
  • Total Download : 807
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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.

An Introduction to Analysis of Financial Data with R

An Introduction to Analysis of Financial Data with R
  • Author : Ruey S. Tsay
  • Publisher : John Wiley & Sons
  • Release Date : 2014-08-21
  • Total pages : 416
  • ISBN : 9781119013464
  • File Size : 18,7 Mb
  • Total Download : 713
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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.

Stochastic Calculus and Financial Applications

Stochastic Calculus and Financial Applications
  • Author : J. Michael Steele
  • Publisher : Springer Science & Business Media
  • Release Date : 2012-12-06
  • Total pages : 302
  • ISBN : 9781468493054
  • File Size : 51,5 Mb
  • Total Download : 181
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Download Stochastic Calculus and Financial Applications in PDF, Epub, and Kindle

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

Dynamic Models for Volatility and Heavy Tails

Dynamic Models for Volatility and Heavy Tails
  • Author : Andrew C. Harvey
  • Publisher : Cambridge University Press
  • Release Date : 2013-04-22
  • Total pages : 281
  • ISBN : 9781107034723
  • File Size : 19,8 Mb
  • Total Download : 655
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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.

The Basics of Financial Econometrics

The Basics of Financial Econometrics
  • Author : Frank J. Fabozzi,Sergio M. Focardi,Svetlozar T. Rachev,Bala G. Arshanapalli
  • Publisher : John Wiley & Sons
  • Release Date : 2014-03-04
  • Total pages : 448
  • ISBN : 9781118727232
  • File Size : 17,9 Mb
  • Total Download : 158
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Download The Basics of Financial Econometrics in PDF, Epub, and Kindle

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.

Multivariate Time Series Analysis

Multivariate Time Series Analysis
  • Author : Ruey S. Tsay
  • Publisher : John Wiley & Sons
  • Release Date : 2013-11-11
  • Total pages : 520
  • ISBN : 9781118617755
  • File Size : 11,9 Mb
  • Total Download : 822
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An accessible guide to the multivariate time series toolsused in numerous real-world applications Multivariate Time Series Analysis: With R and FinancialApplications is the much anticipated sequel coming from one ofthe most influential and prominent experts on the topic of timeseries. Through a fundamental balance of theory and methodology,the book supplies readers with a comprehensible approach tofinancial econometric models and their applications to real-worldempirical research. Differing from the traditional approach to multivariate timeseries, the book focuses on reader comprehension by emphasizingstructural specification, which results in simplified parsimoniousVAR MA modeling. Multivariate Time Series Analysis: With R andFinancial Applications utilizes the freely available Rsoftware package to explore complex data and illustrate relatedcomputation and analyses. Featuring the techniques and methodologyof multivariate linear time series, stationary VAR models, VAR MAtime series and models, unitroot process, factor models, andfactor-augmented VAR models, the book includes: • Over 300 examples and exercises to reinforce thepresented content • User-friendly R subroutines and research presentedthroughout to demonstrate modern applications • Numerous datasets and subroutines to provide readerswith a deeper understanding of the material Multivariate Time Series Analysis is an ideal textbookfor graduate-level courses on time series and quantitative financeand upper-undergraduate level statistics courses in time series.The book is also an indispensable reference for researchers andpractitioners in business, finance, and econometrics.

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
  • Publisher : Routledge
  • Release Date : 2021-06-20
  • Total pages : 275
  • ISBN : 9781000394122
  • File Size : 13,9 Mb
  • Total Download : 187
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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.

Essentials of Stochastic Finance

Essentials of Stochastic Finance
  • Author : Albert N. Shiryaev
  • Publisher : World Scientific
  • Release Date : 1999
  • Total pages : 852
  • ISBN : 9789810236052
  • File Size : 12,7 Mb
  • Total Download : 865
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Readership: Undergraduates and researchers in probability and statistics; applied, pure and financial mathematics; economics; chaos.

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
  • Publisher : SAS Institute
  • Release Date : 2018-03-14
  • Total pages : 384
  • ISBN : 9781629605449
  • File Size : 45,5 Mb
  • Total Download : 768
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Download SAS for Forecasting Time Series, Third Edition in PDF, Epub, and Kindle

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.

Oracle Essentials

Oracle Essentials
  • Author : Rick Greenwald,Robert Stackowiak,Jonathan Stern
  • Publisher : "O'Reilly Media, Inc."
  • Release Date : 2004-02-11
  • Total pages : 400
  • ISBN : 9780596552350
  • File Size : 12,5 Mb
  • Total Download : 790
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An enormous system comprising myriad technologies, options, and releases, Oracle's complexities have spawned numerous areas of specialization. For each area of specialization there are equally specialized how-to books and manuals. O'Reilly's Oracle Essentials claims a unique place among these books. Rather than focusing on one area, the book explains the foundational concepts of the Oracle technology and the core technical and business aspects of using it.The new edition of this classic book, Oracle Essentials, 3rd Edition: Oracle Database 10g, distills a vast amount of knowledge into an easy-to-read volume covering every aspect of the Oracle database. Readers of all levels will learn about Oracle's features and technologies, including the product line, architecture, data structures, networking, concurrency, tuning, and much more.Featuring focused text, abundant illustrations, and helpful hints, the new edition offers a valuable overview of Oracle's Database 10g--the industry's first database to support grid computing. Recent releases such as Oracle 9i and 8i are also covered. The book contains chapters on: Oracle products, options, and overall architecture for Oracle 10g and prior recent releases Installing and running Oracle: how to configure, start up, and shut down the database, and various networking issues Oracle data structures, datatypes, and ways of extending datatypes, with an introduction to Oracle objects (e.g., tables, views, indexes) Managing Oracle: security, the Oracle Enterprise Manager, fragmentation and reorganization, and backup and recovery Oracle performance: characteristics of disk, memory, and CPU tuning Multi-user concurrency, online transaction processing (OLTP), and high availability Hardware architectures (e.g., SMP, MPP, NUMA) and their impact on Oracle Data warehousing and distributed databases Network deployment: using Oracle as an Internet computing platform and for grid computing What's new in Oracle 10g: a summary of the database changes described in the book Oracle Essentials, 3rd Edition: Oracle Database 10g was written for anyone whose job involves managing or building systems using Oracle DBMS technology or working with staff that uses Oracle technology. This book is the perfect all-in-one source for understanding the complexities and capabilities of Oracle.

Time Series with Python

Time Series with Python
  • Author : Bob Mather
  • Publisher : Unknown
  • Release Date : 2020-04-13
  • Total pages : 222
  • ISBN : 0648783073
  • File Size : 22,8 Mb
  • Total Download : 131
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Are you looking to learn more about Time Series, but struggling to find them in traditional Data Science textbooks? This book is your answer. Time Series is an exciting and important part of Data Analysis. Time Series Data is more readily available than most forms of data and answers questions that cross-sectional data struggle to do. It also has more real world application in the prediction of future events. However it is not generally found in a traditional data science toolkit. There is also limited centralized resources on the applications of Time Series, especially using traditional programming languages such as Python. This book solves all these problems, and more. It starts off with basic concepts in Time Series, and switches to more advanced topics. It shows you how to set up Python from start, and goes through over 20 examples of applying both simple and advanced Time Series concepts with Python code.

Practical Time Series Analysis

Practical Time Series Analysis
  • Author : Dr. Avishek Pal,Dr. PKS Prakash
  • Publisher : Packt Publishing Ltd
  • Release Date : 2017-09-28
  • Total pages : 244
  • ISBN : 9781788294195
  • File Size : 20,9 Mb
  • Total Download : 982
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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.

Introduction to Modern Time Series Analysis

Introduction to Modern Time Series Analysis
  • Author : Gebhard Kirchgässner,Jürgen Wolters
  • Publisher : Springer Science & Business Media
  • Release Date : 2008-08-27
  • Total pages : 288
  • ISBN : 3540687351
  • File Size : 53,6 Mb
  • Total Download : 915
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This book presents modern developments in time series econometrics that are applied to macroeconomic and financial time series. It contains the most important approaches to analyze time series which may be stationary or nonstationary.

Analysis of Financial Time Series

Analysis of Financial Time Series
  • Author : Ruey S. Tsay
  • Publisher : John Wiley & Sons
  • Release Date : 2010-10-26
  • Total pages : 720
  • ISBN : 1118017099
  • File Size : 55,6 Mb
  • Total Download : 130
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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.

Quantitative Financial Risk Management

Quantitative Financial Risk Management
  • Author : Constantin Zopounidis,Emilios Galariotis
  • Publisher : John Wiley & Sons
  • Release Date : 2015-05-18
  • Total pages : 448
  • ISBN : 9781118738184
  • File Size : 24,7 Mb
  • Total Download : 388
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A Comprehensive Guide to Quantitative Financial Risk Management Written by an international team of experts in the field, Quantitative Financial Risk Management: Theory and Practice provides an invaluable guide to the most recent and innovative research on the topics of financial risk management, portfolio management, credit risk modeling, and worldwide financial markets. This comprehensive text reviews the tools and concepts of financial management that draw on the practices of economics, accounting, statistics, econometrics, mathematics, stochastic processes, and computer science and technology. Using the information found in Quantitative Financial Risk Management can help professionals to better manage, monitor, and measure risk, especially in today's uncertain world of globalization, market volatility, and geo-political crisis. Quantitative Financial Risk Management delivers the information, tools, techniques, and most current research in the critical field of risk management. This text offers an essential guide for quantitative analysts, financial professionals, and academic scholars.

Hands-on Time Series Analysis with Python

Hands-on Time Series Analysis with Python
  • Author : B V Vishwas,ASHISH PATEL
  • Publisher : Apress
  • Release Date : 2020-08-25
  • Total pages : 407
  • ISBN : 1484259912
  • File Size : 51,9 Mb
  • Total Download : 446
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Download Hands-on Time Series Analysis with Python in PDF, Epub, and Kindle

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