Statistical Models and Methods for Financial Data

Course Description:

This course will cover the statistical models and methods that are relevant to financial data analysis. These include modeling and estimation of heavy tailed distributions, modeling and inference with multivariate copulas, linear and non-linear time series analysis (e.g., GARCH and its variations), and statistical portfolio modeling and analysis. Time permitting, optional topics include stochastic volatility models. Exam- ples and data from financial applications will be used to motivate and illustrate the methods.

Lecture 01， 02

Overview of R and Financial Data

Lecture 03

Probability Models/Distributions and Intro to Value-at-Risk (VaR)/Expected Shortfall

Lecture 04

Univariate Descriptive Statistics,Density Estimation

Lecture 05

Semi-parametric estimation of VaR Peak over threshold (PoT) methods

Lecture 06

Multivariate Distributions: Basics, EDA, and Models/Estimation

Lecture 07

Portfolio Theory - Classical and CAPM-type

Lecture 08

Introduction to Time Series

Lecture 09

Introduction to ARCH/GARCH

Lecture 10

Advanced Topics in GARCH Time Series Analysis

Lecture 11

Cointegration and Statistical Arbitrage