Code Samples of statistics work in R
This repo contains a collection of source code files completing all of the laboratory exercises in the textbook Statistics and Data Analysis for Financial Engineering with R examples Second Edition by David Ruppert and David S. Matteson. This textbook was used for a graduate level course of statistics I took while attending my undergraduate studies at Cornell. The course referenced the textbook, but did not assign all of the laboratory exercises and so I did not complete them all in the course. Recently as a way to practice R and showcase my R skills I decided to complete all of the laboratory exercises. The answers to the questions asked are included in the source code files as comments.
For copyright reasons I cannot upload the textbook itself. I can provide the data if requested.
Explanation of Labs:
Chapter 2: Data Analysis of Stock Returns
Chapter 3: Yield to Maturity calculations
Chapter 4: Exploratory Data Analysis of European Stock Indices
Chapter 5: Modeling Univariate Distributions with Earnings Data
Chapter 6: Resampling with Stock Returns
Chapter 7: Multivariate Statistic Models with Equity Returns (multivariate t-Distributions, matrix calculations, etc)
Chapter 8: Copulas (including simulation, calibration, rank correlations, etc)
Chapter 9: Linear Regression on U.S. Macroeeconomic variables (including Variance Inflation Factors (VIFs), collinearity, ANOVA tables)
Chapter 10: Troubleshooting Regression with Population Data
Chapter 11: Advanced Regression (Nonlinear regression, robust regression, binary regression)
Chapter 12: Time Series Models (ARMA) with T-Bill data
Chapter 13: Time Series Models (Seasonal ARIMA)
Chapter 14: GARCH models with T-bill rates, inflation rates, and GDP data
Chapter 15: Cointegration analysis of Midcap stocks (including vector error corrected models)
Chapter 16: Portfolio Selection (Efficient Frontier)
Chapter 17: Capital Asset Pricing Model (CAPM) with S&P 500 data
Chapter 18: Principal Component Analysis
Chapter 19: Risk Management, Univariate Value at Risk and Expected Shortfall
Chapter 20: Bayesian Data Analysis and Markov Chain Monte Carlo (to fit a t-Distribution).