Introduction to Financial Econometrics

Instructor: Dr. Kamini Rai

Overview:

• Definition and introduction to econometrics and financial econometrics.
• Practical applications of financial econometrics.
• Tools and techniques used in financial econometrics.

Understanding Econometrics

• Econometrics:
  • Application of mathematics and statistics to economics.
  • Develops statistical methods for estimating economic relationships or testing economic theories.
  • Examples:
    • Impact of price changes on demand.
    • How consumption patterns are influenced by income changes.
  • Common applications include forecasting macroeconomic variables like interest rates, inflation, GDP, etc.

Introduction to Financial Econometrics

• Financial Econometrics:
  • Application of statistical and mathematical tools in finance.
  • Focuses on financial market data, differing from other econometric tools.
  • Analyzes and values financial assets, primarily those traded in financial markets.

Areas of Study:

• Capital markets (primary and secondary).
• Corporate finance, public finance, corporate governance.
• Banks and financial institutions use it for forecasting.
• Hedge funds, insurance companies, trading companies to manage risk.

Applications:

• Valuation of Securities:
  • Includes models like CAPM (Capital Asset Pricing Model) and Arbitrage Pricing Model.
• Forecasting and Prediction:
  • Use of time series, panel data.
  • Applied in security price forecasting and price index prediction.
• Risk Management and Volatility Modeling:
  • Essential to quantify risk and predict uncertainty.
• Portfolio Management:
  • Identifying optimal asset allocation for maximum returns.
• Market Efficiency:
  • Testing the Efficient Market Hypothesis.

Models and Tools in Financial Econometrics

• Regression Models:
  • Used to find relationships between dependent and independent variables.
  • Includes panel data, linear, and multinomial regression models.
• Logit and Probit Models:
  • Binary variable regressions.
• Time Series Modeling:
  • Stationary and non-stationary time series analysis.
  • ARIMA (AutoRegressive Integrated Moving Average) for stock price prediction.
• ARCH and GARCH Models:
  • Analyze risk and volatility patterns in stock prices.
• Cointegration and Error Correction Models:
  • Establish correlations between variables.

Recommended Books

• "Financial Econometrics" by Damodar Gujarati.
• "Financial Econometrics" by Wooldridge.
• Many other resources available for further study.

Software Tools

• Open Source:
  • R, Gretel, Python.
• Paid Software:
  • EViews, Stata.
• Additional: C++ can also be used for financial modeling.

Contact Information:

• Email: [email protected]

Note:

• This lecture is an introductory part of financial econometrics. More detailed topics will be covered in upcoming sessions or videos.