Financial Econometrics
This module covers the core econometric tools used in empirical finance research and practice. The unifying theme is that financial time series have distinctive statistical properties — fat tails, volatility clustering, non-normality — that standard econometric tools were not designed for. We build the methods that respect these properties.
Module Overview¶
Week | Topic | Key Methods |
|---|---|---|
1–2 | Returns and Stylized Facts | Log-returns, ACF/PACF, moment analysis |
3–5 | Volatility Modeling | ARCH, GARCH, GJR-GARCH, EGARCH |
6–7 | Multivariate Volatility | DCC-GARCH, BEKK |
8–9 | Realized Volatility | Realized variance, HAR model |
10 | Spillover Analysis | Diebold-Yilmaz connectedness |
Software¶
All code in this module uses Python. The main libraries are:
numpy,pandas— data manipulationarch— GARCH estimationstatsmodels— regression, ACF, diagnosticsmatplotlib,seaborn— visualization
You can install everything with:
pip install numpy pandas arch statsmodels matplotlib seaborn yfinance