Unit 1304: Financial Econometrics

1. Introduction to Financial Econometrics

• Definition and scope of financial econometrics
• Role of statistical methods in finance
• Applications: Market behavior analysis, financial decision making
• Data types and sources in financial econometrics

2. Time Series Analysis in Finance

• Characteristics of financial time series data
• Stationarity and unit root tests
• Autoregressive (AR), Moving Average (MA), and ARIMA models
• Volatility modeling basics
• Forecasting financial time series

3. Asset Pricing Models

• Overview of asset pricing theory
• Capital Asset Pricing Model (CAPM)
  • Assumptions and formula
  • Beta estimation and interpretation
• Arbitrage Pricing Theory (APT)
  • Factor models and applications
• Fama-French three-factor model
  • Size, value factors, and extensions
• Empirical testing of asset pricing models

4. Market Microstructure

• Definition and importance in financial markets
• Market participants and their roles
• Trading mechanisms and order types
• Price formation and bid-ask spreads
• Impact of high-frequency trading

5. Volatility Modeling

• Importance of volatility in finance
• ARCH and GARCH models
  • Model specification and estimation
  • Extensions: EGARCH, TGARCH
• Stochastic volatility models
• Implied volatility and its calculation
• Volatility forecasting and applications

6. Risk Management and Value at Risk (VaR)

• Fundamentals of financial risk management
• Value at Risk (VaR): Concepts and definitions
• Parametric, historical simulation, and Monte Carlo VaR models
• Stress testing methodologies
• Backtesting VaR models

7. Cointegration and Error Correction Models

• Concept of cointegration in financial time series
• Testing for cointegration
• Error Correction Models (ECM)
• Interpretation and application in finance

8. Panel Data Analysis in Finance

• Introduction to panel data and its advantages
• Fixed effects models
• Random effects models
• Pooled regression models
• Model selection criteria and diagnostics
• Applications in financial econometrics

9. Machine Learning in Financial Econometrics

• Overview of machine learning in finance
• Supervised learning techniques:
  • Neural networks
  • Support vector machines (SVM)
  • Random forests
• Predictive modeling and risk assessment
• Algorithmic trading strategies
• Challenges and considerations in application