Financial Statistics and Econometrics

This course focuses on financial statistics and econometrics, aimed at developing students’ ability to analyze and model financial data using both traditional econometric methods and time series models. As the lead instructor, I guided students through topics such as asset pricing models, market efficiency, volatility modeling (e.g., ARCH/GARCH), and the pricing of financial derivatives.

Course Content:

1. Introduction to Financial Data:
   • Characteristics of financial data and sources of data.
   • Introduction to time series models for financial data analysis.
2. Market Returns:
   • Market Efficiency: The theory of efficient markets.
   • ARMA Models: Autoregressive moving average models.
   • VARMA Models: Vector autoregressive moving average models.
   • Event Studies: Analysis of abnormal returns, testing abnormal returns.
3. Predicting Market Volatility (Risk):
   • Introduction to ARCH family models (e.g., ARCH, GARCH, EGARCH).
4. Stochastic Volatility Models:
   • Introduction to state space models, Kalman filter, and Stochastic Volatility (SV) models.
   • Estimation of SV models using Gibbs sampling.
5. Asset Pricing Models:
   • Overview of CAPM theory, empirical testing of CAPM, and multifactor asset pricing models (e.g., Arbitrage Pricing Theory).
6. Present-Value Relations:
   • The relationship between stock prices, dividends, and returns.
7. Derivative Pricing:
   • Models for pricing financial derivatives, including Brownian motion, the Black-Scholes and Merton models, and the martingale approach.

Teaching Methodology:

The course is designed to combine theoretical learning with practical application. Students worked with real financial data and applied the methods using R programming.

Hours: 40 hours