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Abstract:
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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 non-linear, 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 the freely available software language 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. The prerequisites are knowledge of calculus, vectors, and matrices; 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.
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