An introduction to analysis of financial data with R.
type: Item
instance of: scholarly article
zbMATH Open document ID: 1279.91003
author: Ruey S. Tsay
publication date: 2012-08-21T00:00:00Z
Mathematics Subject Classification ID: 91-01
Mathematics Subject Classification ID: 91G70
Mathematics Subject Classification ID: 91-04
Mathematics Subject Classification ID: 91B84
Mathematics Subject Classification ID: 62M10
Mathematics Subject Classification ID: 62P05
zbMATH DE Number: 6069716
zbMATH Keywords: financial data
zbMATH Keywords: time series
zbMATH Keywords: volatility
zbMATH Keywords: forecasting
zbMATH Keywords: AR
zbMATH Keywords: MA
zbMATH Keywords: ARMA
zbMATH Keywords: GARCH models
zbMATH Keywords: value at risk
zbMATH Keywords: expected shortfall
zbMATH Keywords: high-frequency data
zbMATH Keywords: market microstructure
zbMATH Keywords: quantile regression
describes a project that uses: R
describes a project that uses: zoo
describes a project that uses: TTR
describes a project that uses: xts
describes a project that uses: quantmod
describes a project that uses: RMetrics
describes a project that uses: fGarch
describes a project that uses: evir
describes a project that uses: fPortfolio
describes a project that uses: CRAN
describes a project that uses: fBasics
MaRDI profile type: Publication
title: An introduction to analysis of financial data with R.
published in: Wiley Series in Probability and Statistics
review text: This book is thought as an introductory textbook for financial time series analysis. The idea of the author was to make the lecture as user-friendly as possible. The concepts of economic and statistical theory are presented in a simple and concise manner. The lecture focuses on practical aspects of financial data analysis. The objectives of the book expressed by the author are: to provide basic knowledge of financial time series, to introduce statistical tools for analyzing financial data, and to gain experience in financial applications. The book consists of seven chapters.NEWLINENEWLINEChapter 1 presents some basic concepts dealing with financial data (asset returns, bond yields and prices, implied volatility). Next, the R program is introduced and first applications of its packages are used to present some stylized facts about financial data and ways to visualize the data.NEWLINENEWLINEChapter 2 introduces basics of linear time series analysis. The importance of weak stationarity is pointed. Next the author discusses correlation and autocorrelation and the notion of linear time series. The autoregressive (AR), moving average (MA) and ARMA models are presented in detail. Some attention is paid to unit-root nonstationarity and long memory. The exponential smoothing as a method of forecasting is presented. Seasonal models and regression models with time series errors are briefly discussed. The last section of the chapter deals with model comparison and averaging.NEWLINENEWLINEChapter 3 includes three case studies of linear time series. An extensive analysis of weekly regular gasoline price, global temperature anomalies and the US monthly unemployment rates is presented. This chapter is especially useful for beginners in the subject, because it shows step by step the process of modeling the data.NEWLINENEWLINEChapter 4 addresses the problem of volatility measures and models. After describing the general structure of volatility models, the GARCH family is discussed qu…
reviewed by: Małgorzata Doman
description: scientific article; zbMATH DE number 6069716