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Financial Analytics with R

2016-10-06 Business & Economics
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Release : 2016-10-06
Genre : Business & Economics
Kind : eBook
Book Rating : 752/5 ( reviews)

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Book Synopsis Financial Analytics with R by : Mark J. Bennett

Download or read book Financial Analytics with R written by Mark J. Bennett. This book was released on 2016-10-06. Available in PDF, EPUB and Kindle. Book excerpt: Financial Analytics with R sharpens readers' skills in time-series, forecasting, portfolio selection, covariance clustering, prediction, and derivative securities.

An Introduction to Analysis of Financial Data with R

2014-08-21 Business & Economics
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Release : 2014-08-21
Genre : Business & Economics
Kind : eBook
Book Rating : 461/5 ( reviews)

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Book Synopsis An Introduction to Analysis of Financial Data with R by : Ruey S. Tsay

Download or read book An Introduction to Analysis of Financial Data with R written by Ruey S. Tsay. This book was released on 2014-08-21. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Analyzing Financial Data and Implementing Financial Models Using R

2021-06-23 Business & Economics
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Release : 2021-06-23
Genre : Business & Economics
Kind : eBook
Book Rating : 554/5 ( reviews)

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Book Synopsis Analyzing Financial Data and Implementing Financial Models Using R by : Clifford S. Ang

Download or read book Analyzing Financial Data and Implementing Financial Models Using R written by Clifford S. Ang. This book was released on 2021-06-23. Available in PDF, EPUB and Kindle. Book excerpt: This advanced undergraduate/graduate textbook teaches students in finance and economics how to use R to analyse financial data and implement financial models. It demonstrates how to take publically available data and manipulate, implement models and generate outputs typical for particular analyses. A wide spectrum of timely and practical issues in financial modelling are covered including return and risk measurement, portfolio management, option pricing and fixed income analysis. This new edition updates and expands upon the existing material providing updated examples and new chapters on equities, simulation and trading strategies, including machine learnings techniques. Select data sets are available online.

R for Business Analytics

2012-09-14 Business & Economics
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Release : 2012-09-14
Genre : Business & Economics
Kind : eBook
Book Rating : 423/5 ( reviews)

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Book Synopsis R for Business Analytics by : A Ohri

Download or read book R for Business Analytics written by A Ohri. This book was released on 2012-09-14. Available in PDF, EPUB and Kindle. Book excerpt: This book examines common tasks performed by business analysts and helps the reader navigate the wealth of information in R and its 4000 packages to create useful analytics applications. Includes interviews with corporate users of R, and easy-to-use examples.

Statistical Analysis of Financial Data in R

2013-12-13 Business & Economics
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Release : 2013-12-13
Genre : Business & Economics
Kind : eBook
Book Rating : 889/5 ( reviews)

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Book Synopsis Statistical Analysis of Financial Data in R by : René Carmona

Download or read book Statistical Analysis of Financial Data in R written by René Carmona. This book was released on 2013-12-13. Available in PDF, EPUB and Kindle. Book excerpt: Although there are many books on mathematical finance, few deal with the statistical aspects of modern data analysis as applied to financial problems. This textbook fills this gap by addressing some of the most challenging issues facing financial engineers. It shows how sophisticated mathematics and modern statistical techniques can be used in the solutions of concrete financial problems. Concerns of risk management are addressed by the study of extreme values, the fitting of distributions with heavy tails, the computation of values at risk (VaR), and other measures of risk. Principal component analysis (PCA), smoothing, and regression techniques are applied to the construction of yield and forward curves. Time series analysis is applied to the study of temperature options and nonparametric estimation. Nonlinear filtering is applied to Monte Carlo simulations, option pricing and earnings prediction. This textbook is intended for undergraduate students majoring in financial engineering, or graduate students in a Master in finance or MBA program. It is sprinkled with practical examples using market data, and each chapter ends with exercises. Practical examples are solved in the R computing environment. They illustrate problems occurring in the commodity, energy and weather markets, as well as the fixed income, equity and credit markets. The examples, experiments and problem sets are based on the library Rsafd developed for the purpose of the text. The book should help quantitative analysts learn and implement advanced statistical concepts. Also, it will be valuable for researchers wishing to gain experience with financial data, implement and test mathematical theories, and address practical issues that are often ignored or underestimated in academic curricula. This is the new, fully-revised edition to the book Statistical Analysis of Financial Data in S-Plus. René Carmona is the Paul M. Wythes '55 Professor of Engineering and Finance at Princeton University in the department of Operations Research and Financial Engineering, and Director of Graduate Studies of the Bendheim Center for Finance. His publications include over one hundred articles and eight books in probability and statistics. He was elected Fellow of the Institute of Mathematical Statistics in 1984, and of the Society for Industrial and Applied Mathematics in 2010. He is on the editorial board of several peer-reviewed journals and book series. Professor Carmona has developed computer programs for teaching statistics and research in signal analysis and financial engineering. He has worked for many years on energy, the commodity markets and more recently in environmental economics, and he is recognized as a leading researcher and expert in these areas.

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