Financial Statistics and Econometrics

This course focuses on financial statistics and econometrics, designed for postgraduate students. The aim is to equip students with the skills needed to analyze and model financial data using advanced econometric and time series methods. As the lead instructor, I guided students in applying these techniques to real-world financial datasets.

Course Content:

1. Introduction to Financial Data:
   • Characteristics of financial data and how to acquire financial data.
   • Overview of 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 and tests on 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 and Kalman filter techniques.
   • Estimation of stochastic volatility (SV) models using Gibbs sampling.
5. Asset Pricing Models:
   • Review of CAPM theory, empirical testing of the Capital Asset Pricing Model (CAPM).
   • Multifactor asset pricing models (e.g., Arbitrage Pricing Theory).
6. Present-Value Relations:
   • The relationship between stock prices, dividends, and returns.
7. Derivative Pricing:
   • Introduction to derivative pricing models, such as Brownian motion, the Black-Scholes model, and the martingale approach.

Teaching Methodology:

The course combines theoretical instruction with practical applications. Students were provided with the opportunity to analyze real financial data and apply econometric models using R programming.

Hours: 40 hours