Statistical analysis of financial data : with examples in R / James E. Gentle.

Includes bibliographical references and index.

Financial time series -- Financial assets and markets -- Frequency distributions of returns -- Volatility -- Market dynamics -- Stylized facts about financial data -- Data reduction -- The empirical cumulative distribution function -- Nonparametric probability density estimation -- Graphical methods in exploratory analysis -- Random variables and probability distributions -- Some useful probability distributions -- Simulating observations of a random variables -- Models -- Criteria and methods for statistical modeling -- Optimization in statistical modeling; least squares and maximum likelihood -- Statistical inference -- Models of relationship among variables -- Assessing the adequacy of models -- Basic linear operations -- Analysis of discrete time series models -- Autoregressive and moving average models -- Conditional heteroscedasticity -- Unit root and cointegration

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.