The goal of this course is to build a foundation in statistical tools for the analysis of financial data. In case studies statistical software will be used to study the basic characteristics of financial data, estimate, and test financial models. Students will build models for stock prices, interest rates and other quantities and learn how to predict future outcomes.
Learn How To:
- Estimate linear models with regression analysis to understand if a financial variable can be explained by other economic data. Interpret the results
- Extent linear models to structural breaks, endogenous regressors and binary outcomes
- Estimate non-linear models with maximum likelihood and interpret the results
- Understand which models can describe the data by analyzing characteristics of financial time series including kernel density estimation
- Assess uncertainty of your estimators with resampling and bootstrapping
- Estimate stochastic models for time-series (ARMA), which allows you to forecast future outcomes of financial data.
- Make predictions based on cointegration, which can be used for arbitrage trading in stocks.
- Estimate models of time-varying volatility (GARCH) and predict future volatilities of financial assets.
- Test factor models for pricing stocks and estimate new latent factors with principal component analysis.
- Analyze “big financial data” using penalized regressions.
- Professor Markus Pelger
June 18-22, 2016