R in Finance and Economics PDF Download

Author: Abhay Kumar Singh
Publisher: World Scientific Publishing Company
ISBN: 9813144483
Category :
Languages : en
Pages : 264

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Book Description
This book provides an introduction to the statistical software R and its application with an empirical approach in finance and economics. It is specifically targeted towards undergraduate and graduate students. It provides beginner-level introduction to R using RStudio and reproducible research examples. It will enable students to use R for data cleaning, data visualization and quantitative model building using statistical methods like linear regression, econometrics (GARCH etc), Copulas, etc. Moreover, the book demonstrates latest research methods with applications featuring linear regression, quantile regression, panel regression, econometrics, dependence modelling, etc. using a range of data sets and examples. Request Inspection Copy

Author: Christian Kleiber
Publisher: Springer Science & Business Media
ISBN: 0387773185
Category : Business & Economics
Languages : en
Pages : 229

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Book Description
R is a language and environment for data analysis and graphics. It may be considered an implementation of S, an award-winning language initially - veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid-1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and also a variety of packages with canned routines. We believe that R has great potential in econometrics, both for research and for teaching. There are at least three reasons for this: (1) R is mostly platform independent and runs on Microsoft Windows, the Mac family of operating systems, and various ?avors of Unix/Linux, and also on some more exotic platforms. (2) R is free software that can be downloaded and installed at no cost from a family of mirror sites around the globe, the Comprehensive R Archive Network (CRAN); hence students can easily install it on their own machines. (3) R is open-source software, so that the full source code is available and can be inspected to understand what it really does, learn from it, and modify and extend it. We also like to think that platform independence and the open-source philosophy make R an ideal environment for reproducible econometric research.

Author: Jonathan K. Regenstein, Jr.
Publisher: CRC Press
ISBN: 1351052608
Category : Mathematics
Languages : en
Pages : 248

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Book Description
Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis is a unique introduction to data science for investment management that explores the three major R/finance coding paradigms, emphasizes data visualization, and explains how to build a cohesive suite of functioning Shiny applications. The full source code, asset price data and live Shiny applications are available at reproduciblefinance.com. The ideal reader works in finance or wants to work in finance and has a desire to learn R code and Shiny through simple, yet practical real-world examples. The book begins with the first step in data science: importing and wrangling data, which in the investment context means importing asset prices, converting to returns, and constructing a portfolio. The next section covers risk and tackles descriptive statistics such as standard deviation, skewness, kurtosis, and their rolling histories. The third section focuses on portfolio theory, analyzing the Sharpe Ratio, CAPM, and Fama French models. The book concludes with applications for finding individual asset contribution to risk and for running Monte Carlo simulations. For each of these tasks, the three major coding paradigms are explored and the work is wrapped into interactive Shiny dashboards.

Author: Eric Zivot
Publisher: Springer Science & Business Media
ISBN: 0387217630
Category : Business & Economics
Languages : en
Pages : 632

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Book Description
The field of financial econometrics has exploded over the last decade This book represents an integration of theory, methods, and examples using the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the practice of financial econometrics. This is the first book to show the power of S-PLUS for the analysis of time series data. It is written for researchers and practitioners in the finance industry, academic researchers in economics and finance, and advanced MBA and graduate students in economics and finance. Readers are assumed to have a basic knowledge of S-PLUS and a solid grounding in basic statistics and time series concepts. This Second Edition is updated to cover S+FinMetrics 2.0 and includes new chapters on copulas, nonlinear regime switching models, continuous-time financial models, generalized method of moments, semi-nonparametric conditional density models, and the efficient method of moments. Eric Zivot is an associate professor and Gary Waterman Distinguished Scholar in the Economics Department, and adjunct associate professor of finance in the Business School at the University of Washington. He regularly teaches courses on econometric theory, financial econometrics and time series econometrics, and is the recipient of the Henry T. Buechel Award for Outstanding Teaching. He is an associate editor of Studies in Nonlinear Dynamics and Econometrics. He has published papers in the leading econometrics journals, including Econometrica, Econometric Theory, the Journal of Business and Economic Statistics, Journal of Econometrics, and the Review of Economics and Statistics. Jiahui Wang is an employee of Ronin Capital LLC. He received a Ph.D. in Economics from the University of Washington in 1997. He has published in leading econometrics journals such as Econometrica and Journal of Business and Economic Statistics, and is the Principal Investigator of National Science Foundation SBIR grants. In 2002 Dr. Wang was selected as one of the "2000 Outstanding Scholars of the 21st Century" by International Biographical Centre.

Author: Robert J. Shiller
Publisher: Princeton University Press
ISBN: 140084617X
Category : Business & Economics
Languages : en
Pages : 312

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Book Description
Nobel Prize-winning economist explains why we need to reclaim finance for the common good The reputation of the financial industry could hardly be worse than it is today in the painful aftermath of the 2008 financial crisis. New York Times best-selling economist Robert Shiller is no apologist for the sins of finance—he is probably the only person to have predicted both the stock market bubble of 2000 and the real estate bubble that led up to the subprime mortgage meltdown. But in this important and timely book, Shiller argues that, rather than condemning finance, we need to reclaim it for the common good. He makes a powerful case for recognizing that finance, far from being a parasite on society, is one of the most powerful tools we have for solving our common problems and increasing the general well-being. We need more financial innovation—not less—and finance should play a larger role in helping society achieve its goals. Challenging the public and its leaders to rethink finance and its role in society, Shiller argues that finance should be defined not merely as the manipulation of money or the management of risk but as the stewardship of society's assets. He explains how people in financial careers—from CEO, investment manager, and banker to insurer, lawyer, and regulator—can and do manage, protect, and increase these assets. He describes how finance has historically contributed to the good of society through inventions such as insurance, mortgages, savings accounts, and pensions, and argues that we need to envision new ways to rechannel financial creativity to benefit society as a whole. Ultimately, Shiller shows how society can once again harness the power of finance for the greater good.

Author: Satya R. Chakravarty
Publisher: Anthem Press
ISBN: 1783083360
Category : Business & Economics
Languages : en
Pages : 314

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Book Description
“An Outline of Financial Economics” presents a systematic treatment of the theory and methodology of finance and economics. The book follows an analytical and geometric methodology, explaining technical terms and mathematical operations in clear, non-technical language, and providing intuitive explanations of the mathematical results. The text begins with a discussion of financial instruments, which form the basis of finance theory, and goes on to analyze bonds – which are regarded as fixed income securities – in a simple framework, and to discuss the valuation of stocks and cash flows in detail. Highly relevant topics such as attitudes toward risk, uncertainty, the financial structure of a firm, stochastic dominance, portfolio management, option pricing and conditions for non-arbitrage are analyzed explicitly. Because of its wide coverage and analytical, articulate and authoritative presentation, “An Outline of Financial Economics” will be an indispensable book for finance researchers and undergraduate and graduate students in fields such as economics, finance, econometrics, statistics and mathematics.

Author: Clifford S. Ang
Publisher: Springer Nature
ISBN: 3030641554
Category : Business & Economics
Languages : en
Pages : 465

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Book Description
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.

Author: Marcelo S Perlin
Publisher:
ISBN: 9781710627312
Category :
Languages : en
Pages : 494

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Book Description
book introduces the reader to the use of R and RStudio as a platform for analyzing financial and economic data. The book covers all necessary knowledge for using R, from its installation in your computer to the organization and development of scripts. For every chapter, the book presents practical and replicable examples of R code, providing context and facilitating the learning process. This is what you'll learn from this book: Using R and RStudio: In chapter 01 we will discuss the use of R as a programming platform designed to solve data-related problems in finance and economics. In chapter 02 we will explore basic commands and many functionalities of R and RStudio that will increase your productivity. Importing financial and economic data: In chapters 04 and 05 we will learn to import data from local files, such as an Excel spreadsheet, or the internet, using specialized packages that can download financial and economic data such as stock prices, economic indices, the US yield curve, corporate financial statements, and many others. Cleaning, structuring and analyzing the data with R: In chapters 06 and 07 we will concentrate our study on the ecosystem of basic and advanced classes of objects within R. We will learn to manipulate objects such as numeric vectors, dates and whole tables. In chapters 08 and 09 we'll study to use the programming tools to solve data-related problems such as cleaning and structuring messy data. In chapter 11 we will learn applications of the most common econometric models used in finance and economics including linear regression, generalized linear model, Arima model and others. Creating visual analysis of data: In chapter 10 we'll learn to use functions from package ggplot2 to create clever visualizations of our datasets, including the most popular applications in finance and economics, time series and statistical plots. Reporting your results: In chapter 12 we will see how to report our data analysis using specialized packages and the RMarkdown technology. Includes the topic of presenting and exporting tables, figure and models to a written report. Writing better and faster code: In the last chapter of the book we discuss best programming practices with R. We will look at how to profile code and search for bottlenecks, and improving execution time with caching strategies using package memoise, C++ code with Rcpp and parallel computing with furrr. All the material used in the book, including code examples separated by chapters, slides and exercises is publicly available on the Internet and distributed with a R package called afedR. It includes data files and several functions that can make it easier to run the examples of the book. If you plan to write some code as you read the book, this package will greatly help your journey. This book is recommended for researchers and students interested in learning how to use R. No prior knowledge of programming, finance or economics is required to take advantage of this book. After finishing, the reader will have enough knowledge to develop their own scripts autonomously, producing academic documents or data analysis for public and private institutions.

Author: Vikram Dayal
Publisher: Springer Nature
ISBN: 9811520356
Category : Mathematics
Languages : en
Pages : 323

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Book Description
This book provides a contemporary treatment of quantitative economics, with a focus on data science. The book introduces the reader to R and RStudio, and uses expert Hadley Wickham’s tidyverse package for different parts of the data analysis workflow. After a gentle introduction to R code, the reader’s R skills are gradually honed, with the help of “your turn” exercises. At the heart of data science is data, and the book equips the reader to import and wrangle data, (including network data). Very early on, the reader will begin using the popular ggplot2 package for visualizing data, even making basic maps. The use of R in understanding functions, simulating difference equations, and carrying out matrix operations is also covered. The book uses Monte Carlo simulation to understand probability and statistical inference, and the bootstrap is introduced. Causal inference is illuminated using simulation, data graphs, and R code for applications with real economic examples, covering experiments, matching, regression discontinuity, difference-in-difference, and instrumental variables. The interplay of growth related data and models is presented, before the book introduces the reader to time series data analysis with graphs, simulation, and examples. Lastly, two computationally intensive methods—generalized additive models and random forests (an important and versatile machine learning method)—are introduced intuitively with applications. The book will be of great interest to economists—students, teachers, and researchers alike—who want to learn R. It will help economics students gain an intuitive appreciation of applied economics and enjoy engaging with the material actively, while also equipping them with key data science skills.

Author: Florian Heiss
Publisher:
ISBN:
Category :
Languages : en
Pages : 380

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Book Description
Introduces the popular, powerful and free programming language and software package R Focus implementation of standard tools and methods used in econometrics Compatible with "Introductory Econometrics" by Jeffrey M. Wooldridge in terms of topics, organization, terminology and notation Companion website with full text, all code for download and other goodies: http: //urfie.net Also check out Using Python for Introductory Econometrics http: //upfie.net/ Praise "A very nice resource for those wanting to use R in their introductory econometrics courses." (Jeffrey M. Wooldridge) Using R for Introductory Econometrics is a fabulous modern resource. I know I'm going to be using it with my students, and I recommend it to anyone who wants to learn about econometrics and R at the same time." (David E. Giles in his blog "Econometrics Beat") Topics: A gentle introduction to R Simple and multiple regression in matrix form and using black box routines Inference in small samples and asymptotics Monte Carlo simulations Heteroscedasticity Time series regression Pooled cross-sections and panel data Instrumental variables and two-stage least squares Simultaneous equation models Limited dependent variables: binary, count data, censoring, truncation, and sample selection Formatted reports and research papers combining R with R Markdown or LaTeX