snowrr/empirical-method-in-finance

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

Empirical-Method-in-Finance

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