Finance – Comprehensive Resources for Learning Statistical Analysis of Financial Data

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I realize that the statistical analysis of financial data is a huge topic, but that is exactly why it is necessary for me to ask my question as I try to break into the world of financial analysis.

As at this point I know next to nothing about the subject, the results of my google searches are overwhelming. Many of the matches advocate learning specialized tools or the R programming language. While I will learn these when they are necessary, I'm first interested in books, articles or any other resources that explain modern methods of statistical analysis specifically for financial data. I assume there are a number of different wildly varied methods for analyzing data, so ideally I'm seeking an overview of the various methods that are practically applicable. I'd like something that utilizes real world examples that a beginner is capable of grasping but that aren't overly simplistic.

What are some good resources for learning bout the statistical analysis of financial data?

Best Answer

You might start with this series of lectures by Robert Shiller at Yale. He gives a good overview of the field.

My favorite books on the subject:

I strongly recommend starting with Statistics and Finance, by David Ruppert (the R code for the book is available). This is a great introduction and covers the basics of finance and statistics so it's appropriate as a first book.
Modeling Financial Time Series with S-Plus, by Eric Zivot
Analysis of Financial Time Series, by Ruey Tsay
Time Series Analysis, by Jonathan D. Cryer

Beyond that, you may want some general resources, and the "bible" of finance is Options, Futures, and Other Derivatives by John Hull.

Lastly, in terms of some good general books, you might start with these two:

Related Solutions

Solved – Several questions about statistical financial timeseries models from “machine-learning person”

Regarding question 1, time series do not deal mainly with random walks. Stationary time series have correlation structure that is modelled in for example ARMA models. Time series analysis also looks at periodic effects and trend (we call those time series nonstationary). Looking for patterns in data is not incompatible with statistics as long as there is recognition that there is a pattern + a random component and the random component must be considered in the analysis. Regarding question 2 I don't see why you call TAR a mix of machine learning and statistics. I see it as just a more complicated time series model that includes a threshold parameter an 2 AR models. I guess I also don't see a big distinction between machine learning and statistics. I view machine learning as part of statistical pattern recognition/classification which falls under the realm of multivariate analysis. It seems to me that TAR could easily be extended to putting a threshold on an ARMA model. I don't know if it has been tried or why it might not have been developed. Perhaps someone who works with these type of time series models can answer that question.

Solved – What statistical tools does a analyst (financial) require

Maybe you want to take a look in Statistics and Finance: An Introduction by David Ruppert. It covers a wide range of topics but none in detail. The author actually written this book with engineers in mind and it's very application-oriented.

Table of Content:

1 Introduction
2 Probability and Statistical Models
3 Returns
4 Time Series Models
5 Portfolio Theory
6 Regression
7 The Capital Asset Pricing Model
8 Options Pricing
9 Fixed Income Securities
10 Resampling
11 Value-At-Risk
12 GARCH Models
13 Nonparametric Regression and Splines
14 Behavioral Finance

Best Answer

Related Solutions

Solved – Several questions about statistical financial timeseries models from “machine-learning person”

Solved – What statistical tools does a analyst (financial) require

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