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.

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 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.

A substantially updated new edition of the essential text on financial modeling, with revised material, new data, and implementations shown in Excel, R, and Python. Financial Modeling has become the gold-standard text in its field, an essential guide for students, researchers, and practitioners that provides the computational tools needed for modeling finance fundamentals. This fifth edition has been substantially updated but maintains the straightforward, hands-on approach, with an optimal mix of explanation and implementation, that made the previous editions so popular. Using detailed Excel spreadsheets, it explains basic and advanced models in the areas of corporate finance, portfolio management, options, and bonds. This new edition offers revised material on valuation, second-order and third-order Greeks for options, value at risk (VaR), Monte Carlo methods, and implementation in R. The examples and implementation use up-to-date and relevant data. Parts I to V cover corporate finance topics, bond and yield curve models, portfolio theory, options and derivatives, and Monte Carlo methods and their implementation in finance. Parts VI and VII treat technical topics, with part VI covering Excel and R issues and part VII (now on the book’s auxiliary website) covering Excel’s programming language, Visual Basic for Applications (VBA), and Python implementations. Knowledge of technical chapters on VBA and R is not necessary for understanding the material in the first five parts. The book is suitable for use in advanced finance classes that emphasize the need to combine modeling skills with a deeper knowledge of the underlying financial models.

Presents inference and simulation of stochastic process in the field of model calibration for financial times series modelled by continuous time processes and numerical option pricing. Introduces the bases of probability theory and goes on to explain how to model financial times series with continuous models, how to calibrate them from discrete data and further covers option pricing with one or more underlying assets based on these models. Analysis and implementation of models goes beyond the standard Black and Scholes framework and includes Markov switching models, Lévy models and other models with jumps (e.g. the telegraph process); Topics other than option pricing include: volatility and covariation estimation, change point analysis, asymptotic expansion and classification of financial time series from a statistical viewpoint. The book features problems with solutions and examples. All the examples and R code are available as an additional R package, therefore all the examples can be reproduced.

Financial Analytics with R sharpens readers' skills in time-series, forecasting, portfolio selection, covariance clustering, prediction, and derivative securities.

Financial Risk Modelling and Portfolio Optimization with R, 2nd Edition Bernhard Pfaff, Invesco Global Asset Allocation, Germany A must have text for risk modelling and portfolio optimization using R. This book introduces the latest techniques advocated for measuring financial market risk and portfolio optimization, and provides a plethora of R code examples that enable the reader to replicate the results featured throughout the book. This edition has been extensively revised to include new topics on risk surfaces and probabilistic utility optimization as well as an extended introduction to R language. Financial Risk Modelling and Portfolio Optimization with R: Demonstrates techniques in modelling financial risks and applying portfolio optimization techniques as well as recent advances in the field. Introduces stylized facts, loss function and risk measures, conditional and unconditional modelling of risk; extreme value theory, generalized hyperbolic distribution, volatility modelling and concepts for capturing dependencies. Explores portfolio risk concepts and optimization with risk constraints. Is accompanied by a supporting website featuring examples and case studies in R. Includes updated list of R packages for enabling the reader to replicate the results in the book. Graduate and postgraduate students in finance, economics, risk management as well as practitioners in finance and portfolio optimization will find this book beneficial. It also serves well as an accompanying text in computer-lab classes and is therefore suitable for self-study.

This is a programming book written by a finance professor. This book will be an ideal textbook for many quantitative finance courses, such as (next generation) financial modeling, portfolio theory, empirical research in finance, computational finance, and risk management. The book has three unique characteristics: (1) use free software; (2) combine programming with various finance theories, such as ratio analysis, CAPM, Fama-French 5-factor model, portfolio theory, options and futures, credit analysis, VaR (Value at Risk), and Monte Carlo Simulation; and (3) download and process publicly available financial and economic data from various sources, such as Yahoo! Finance, Google Finance, FRED (Federal Reserve Bank's Economic Data Library), SEC, and Prof. French's Data Library

Too often, finance courses stop short of making a connection between textbook finance and the problems of real-world business. "Financial Modeling" bridges this gap between theory and practice by providing a nuts-and-bolts guide to solving common financial problems with spreadsheets. The CD-ROM contains Excel* worksheets and solutions to end-of-chapter exercises. 634 illustrations.

Make informed business decisions with the beginner's guide to financial modeling using Microsoft Excel Financial Modeling in Excel For Dummies is your comprehensive guide to learning how to create informative, enlightening financial models today. Not a math whiz or an Excel power-user? No problem! All you need is a basic understanding of Excel to start building simple models with practical hands-on exercises and before you know it, you'll be modeling your way to optimized profits for your business in no time. Excel is powerful, user-friendly, and is most likely already installed on your computer—which is why it has so readily become the most popular financial modeling software. This book shows you how to harness Excel's capabilities to determine profitability, develop budgetary projections, model depreciation, project costs, value assets and more. You'll learn the fundamental best practices and know-how of financial modeling, and how to put them to work for your business and your clients. You'll learn the tools and techniques that bring insight out of the numbers, and make better business decisions based on quantitative evidence. You'll discover that financial modeling is an invaluable resource for your business, and you'll wonder why you've waited this long to learn how! Companies around the world use financial modeling for decision making, to steer strategy, and to develop solutions. This book walks you through the process with clear, expert guidance that assumes little prior knowledge. Learn the six crucial rules to follow when building a successful financial model Discover how to review and edit an inherited financial model and align it with your business and financial strategy Solve client problems, identify market projections, and develop business strategies based on scenario analysis Create valuable customized templates models that can become a source of competitive advantage From multinational corporations to the mom-and-pop corner store, there isn't a business around that wouldn't benefit from financial modeling. No need to buy expensive specialized software—the tools you need are right there in Excel. Financial Modeling in Excel For Dummies gets you up to speed quickly so you can start reaping the benefits today!