Winter 2020 Course description: Econometric and statistical techniques commonly used in quantitative finance. Use of estimation application software in exercises to estimate volatility, correlations, stability, regressions, and statistical inference using financial time series. Topic 1: Time series properties of stock market returns and prices
Class intro: Forecasting and Finance
The random walk hypothesis
Stationarity
Time-varying volatility and General Least Squares
Robust standard errors and OLS
Topic 2: Time-dependence and predictability
ARMA models
The likelihood function, exact and conditional likelihood estimation
Predictive regressions, autocorrelation robust standard errors
The Campbell-Shiller decomposition
Present value restrictions
Multivariate analysis: Vector Autoregression (VAR) models, the Kalman Filter
Topic 3: Heteroscedasticity
Time-varying volatility in the data
Realized Variance
ARCH and GARCH models, application to Value-at-Risk
Topic 4: Time series properties of the cross-section of stock returns
Single- and multifactor models
Economic factors: Models and data exploration
Statistical factors: Principal Components Analysis
Fama-MacBeth regressions and characteristics-based factors