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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 Book
Author : Massimo Guidolin,Manuela Pedio
Publisher : Academic Press
Release : 2018-05-29
ISBN : 0128134100
Language : En, Es, Fr & De

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Book Description :

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 Book
Author : Ngai Hang Chan
Publisher : Wiley-Interscience
Release : 2002
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

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 Book
Author : William W. S. Wei
Publisher : Wiley
Release : 2019-02-26
ISBN : 1119502853
Language : En, Es, Fr & De

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Book Description :

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 Book
Author : J. Michael Steele
Publisher : Springer Science & Business Media
Release : 2012-12-06
ISBN : 1468493051
Language : En, Es, Fr & De

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Book Description :

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 Book
Author : Ruey S. Tsay
Publisher : John Wiley & Sons
Release : 2014-08-21
ISBN : 1119013461
Language : En, Es, Fr & De

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Book Description :

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.

The Essentials of Machine Learning in Finance and Accounting

The Essentials of Machine Learning in Finance and Accounting Book
Author : Mohammad Zoynul Abedin,M. Kabir Hassan,Petr Hajek,Mohammed Mohi Uddin
Publisher : Routledge
Release : 2021-06-21
ISBN : 1000394123
Language : En, Es, Fr & De

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Book Description :

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.

Practical Time Series Analysis

Practical Time Series Analysis Book
Author : Dr. Avishek Pal,Dr. PKS Prakash
Publisher : Packt Publishing Ltd
Release : 2017-09-28
ISBN : 178829419X
Language : En, Es, Fr & De

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Book Description :

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.

Handbook of Financial Time Series

Handbook of Financial Time Series Book
Author : Torben Gustav Andersen,Richard A. Davis,Jens-Peter Kreiß,Thomas V. Mikosch
Publisher : Springer Science & Business Media
Release : 2009-04-21
ISBN : 3540712976
Language : En, Es, Fr & De

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Book Description :

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.

Oracle Essentials

Oracle Essentials Book
Author : Rick Greenwald,Robert Stackowiak,Jonathan Stern
Publisher : "O'Reilly Media, Inc."
Release : 2004-02-11
ISBN : 0596552351
Language : En, Es, Fr & De

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Book Description :

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.

SAS for Forecasting Time Series Third Edition

SAS for Forecasting Time Series  Third Edition Book
Author : John C. Brocklebank, Ph.D.,David A. Dickey, Ph.D.,Bong Choi
Publisher : SAS Institute
Release : 2018-03-14
ISBN : 1629605441
Language : En, Es, Fr & De

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Book Description :

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.

Financial Economics and Econometrics

Financial Economics and Econometrics Book
Author : Nikiforos T. Laopodis
Publisher : Routledge
Release : 2021-12-15
ISBN : 1000506088
Language : En, Es, Fr & De

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Book Description :

Financial Economics and Econometrics provides an overview of the core topics in theoretical and empirical finance, with an emphasis on applications and interpreting results. Structured in five parts, the book covers financial data and univariate models; asset returns; interest rates, yields and spreads; volatility and correlation; and corporate finance and policy. Each chapter begins with a theory in financial economics, followed by econometric methodologies which have been used to explore the theory. Next, the chapter presents empirical evidence and discusses seminal papers on the topic. Boxes offer insights on how an idea can be applied to other disciplines such as management, marketing and medicine, showing the relevance of the material beyond finance. Readers are supported with plenty of worked examples and intuitive explanations throughout the book, while key takeaways, ‘test your knowledge’ and ‘test your intuition’ features at the end of each chapter also aid student learning. Digital supplements including PowerPoint slides, computer codes supplements, an Instructor’s Manual and Solutions Manual are available for instructors. This textbook is suitable for upper-level undergraduate and graduate courses on financial economics, financial econometrics, empirical finance and related quantitative areas.

Statistical Analysis of Financial Data

Statistical Analysis of Financial Data Book
Author : James Gentle
Publisher : CRC Press
Release : 2020-03-12
ISBN : 042993923X
Language : En, Es, Fr & De

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Book Description :

Statistical Analysis of Financial Data covers the use of statistical analysis and the methods of data science to model and analyze financial data. The first chapter is an overview of financial markets, describing the market operations and using exploratory data analysis to illustrate the nature of financial data. The software used to obtain the data for the examples in the first chapter and for all computations and to produce the graphs is R. However discussion of R is deferred to an appendix to the first chapter, where the basics of R, especially those most relevant in financial applications, are presented and illustrated. The appendix also describes how to use R to obtain current financial data from the internet. Chapter 2 describes the methods of exploratory data analysis, especially graphical methods, and illustrates them on real financial data. Chapter 3 covers probability distributions useful in financial analysis, especially heavy-tailed distributions, and describes methods of computer simulation of financial data. Chapter 4 covers basic methods of statistical inference, especially the use of linear models in analysis, and Chapter 5 describes methods of time series with special emphasis on models and methods applicable to analysis of financial data. Features * Covers statistical methods for analyzing models appropriate for financial data, especially models with outliers or heavy-tailed distributions. * Describes both the basics of R and advanced techniques useful in financial data analysis. * Driven by real, current financial data, not just stale data deposited on some static website. * Includes a large number of exercises, many requiring the use of open-source software to acquire real financial data from the internet and to analyze it.

Introductory Econometrics for Finance

Introductory Econometrics for Finance Book
Author : Chris Brooks
Publisher : Cambridge University Press
Release : 2008-05-22
ISBN : 1139472305
Language : En, Es, Fr & De

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Book Description :

This best-selling textbook addresses the need for an introduction to econometrics specifically written for finance students. Key features: • Thoroughly revised and updated, including two new chapters on panel data and limited dependent variable models • Problem-solving approach assumes no prior knowledge of econometrics emphasising intuition rather than formulae, giving students the skills and confidence to estimate and interpret models • Detailed examples and case studies from finance show students how techniques are applied in real research • Sample instructions and output from the popular computer package EViews enable students to implement models themselves and understand how to interpret results • Gives advice on planning and executing a project in empirical finance, preparing students for using econometrics in practice • Covers important modern topics such as time-series forecasting, volatility modelling, switching models and simulation methods • Thoroughly class-tested in leading finance schools. Bundle with EViews student version 6 available. Please contact us for more details.

Machine Learning in Finance

Machine Learning in Finance Book
Author : Matthew F. Dixon,Igor Halperin,Paul Bilokon
Publisher : Springer Nature
Release : 2020-07-01
ISBN : 3030410684
Language : En, Es, Fr & De

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Book Description :

This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.

Recent Advances in Time Series Forecasting

Recent Advances in Time Series Forecasting Book
Author : Dinesh C.S. Bisht,Mangey Ram
Publisher : CRC Press
Release : 2021-09-08
ISBN : 1000433846
Language : En, Es, Fr & De

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Book Description :

Future predictions are always a topic of interest. Precise estimates are crucial in many activities as forecasting errors can lead to big financial loss. The sequential analysis of data and information gathered from past to present is call time series analysis. This book covers the recent advancements in time series forecasting. The book includes theoretical as well as recent applications of time series analysis. It focuses on the recent techniques used, discusses a combination of methodology and applications, presents traditional and advanced tools, new applications, and identifies the gaps in knowledge in engineering applications. This book is aimed at scientists, researchers, postgraduate students and engineers in the areas of supply chain management, production, inventory planning, and statistical quality control.

Data Science for Economics and Finance

Data Science for Economics and Finance Book
Author : Sergio Consoli,Diego Reforgiato Recupero,Michaela Saisana
Publisher : Springer Nature
Release : 2021
ISBN : 3030668916
Language : En, Es, Fr & De

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Book Description :

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.

Essentials of Stochastic Finance

Essentials of Stochastic Finance Book
Author : Albert N. Shiryaev
Publisher : World Scientific
Release : 1999
ISBN : 9810236050
Language : En, Es, Fr & De

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Book Description :

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

Theory and Applications of Time Series Analysis

Theory and Applications of Time Series Analysis Book
Author : Olga Valenzuela,Fernando Rojas,Luis Javier Herrera,Héctor Pomares,Ignacio Rojas
Publisher : Springer Nature
Release : 2020-11-20
ISBN : 3030562190
Language : En, Es, Fr & De

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Book Description :

This book presents a selection of peer-reviewed contributions on the latest advances in time series analysis, presented at the International Conference on Time Series and Forecasting (ITISE 2019), held in Granada, Spain, on September 25-27, 2019. The first two parts of the book present theoretical contributions on statistical and advanced mathematical methods, and on econometric models, financial forecasting and risk analysis. The remaining four parts include practical contributions on time series analysis in energy; complex/big data time series and forecasting; time series analysis with computational intelligence; and time series analysis and prediction for other real-world problems. Given this mix of topics, readers will acquire a more comprehensive perspective on the field of time series analysis and forecasting. The ITISE conference series provides a forum for scientists, engineers, educators and students to discuss the latest advances and implementations in the foundations, theory, models and applications of time series analysis and forecasting. It focuses on interdisciplinary research encompassing computer science, mathematics, statistics and econometrics.

R Programming and Its Applications in Financial Mathematics

R Programming and Its Applications in Financial Mathematics Book
Author : Shuichi Ohsaki,Jori Ruppert-Felsot,Daisuke Yoshikawa
Publisher : CRC Press
Release : 2018-01-31
ISBN : 1351649868
Language : En, Es, Fr & De

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Book Description :

This book provides an introduction to R programming and a summary of financial mathematics. It is not always easy for graduate students to grasp an overview of the theory of finance in an abstract form. For newcomers to the finance industry, it is not always obvious how to apply the abstract theory to the real financial data they encounter. Introducing finance theory alongside numerical applications makes it easier to grasp the subject. Popular programming languages like C++, which are used in many financial applications are meant for general-purpose requirements. They are good for implementing large-scale distributed systems for simultaneously valuing many financial contracts, but they are not as suitable for small-scale ad-hoc analysis or exploration of financial data. The R programming language overcomes this problem. R can be used for numerical applications including statistical analysis, time series analysis, numerical methods for pricing financial contracts, etc. This book provides an overview of financial mathematics with numerous examples numerically illustrated using the R programming language.

Discrete Time Approximations and Limit Theorems

Discrete Time Approximations and Limit Theorems Book
Author : Yuliya Mishura,Kostiantyn Ralchenko
Publisher : Walter de Gruyter GmbH & Co KG
Release : 2021-10-25
ISBN : 3110654245
Language : En, Es, Fr & De

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Book Description :

Financial market modeling is a prime example of a real-life application of probability theory and stochastics. This authoritative book discusses the discrete-time approximation and other qualitative properties of models of financial markets, like the Black-Scholes model and its generalizations, offering in this way rigorous insights on one of the most interesting applications of mathematics nowadays.