JSM 2015 Online Program

Online Program Home
My Program

Legend: Washington State Convention Center = CC, Sheraton Seattle = S, Grand Hyatt = GH and The Conference Center = TCC
* = applied session       ! = JSM meeting theme

Activity Details

CE_05C Sat, 8/8/2015, 8:30 AM - 5:00 PM S-Willow A
Statistical Analysis of Financial Data with R (ADDED FEE) — Professional Development Continuing Education Course
The analysis of financial and econometric data is typified by non-Gaussian multivariate observations that exhibit complex dependencies: heavy-tailed and skewed marginal distributions are commonly encountered; serial dependence, such as auto-correlation and conditional heteroscedasticity, appear in time-ordered sequences; and nonlinear, higher-order, and tail dependence are widespread. This course will introduce statistical methods for the analysis of financial data. Examples and case studies will illustrate the application of these methods using R and numerous contributed packages. The first half of the course will include assessing departures from normality, modeling univariate and multivariate data, copula models, and tail dependence. The second half will provide an introduction to univariate and multivariate time series modeling, including autoregressive moving average (ARMA), generalized autoregressive conditional heteroscedastic (GARCH), and stochastic volatility (SV) models. Prerequisites are knowledge of calculus, vectors, and matrices, as well as probability models, mathematical statistics, and regression at the level typical of third- or fourth-year undergraduates in statistics, mathematics, engineering, and related disciplines. Prior experience using R is helpful, but not necessary.
Instructor(s): David S. Matteson, Cornell University, David Ruppert, Cornell University

For program information, contact the JSM Registration Department or phone (888) 231-3473.

For Professional Development information, contact the Education Department.

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

2015 JSM Online Program Home