Financial Econometrics

Graduate Program (& Advanced Certificate) Status

Course Level: 
Master’s
Campus: 
Budapest
Course Open to: 
Students on-site
Academic Year: 
2019-2020
Term: 
Winter
US Credits: 
2
ECTS Credits: 
4
Course Description: 

This course introduces students to econometric methods useful for those aiming to work in finance. Specifically, we will learn methods of predicting returns and volatility of financial assets, and for computing the level of risk that an investor faces in her portfolio. Some background in undergraduate econometrics is recommended, e.g., in hypothesis testing and linear regressions. Key terms to be covered include: log returns, volatility, autocorrelation, AR, MA, ARMA, ARCH, GARCH, and Value-at-Risk.

Learning Outcomes: 

In completing this course, a student will be able to… 

  • Forecast returns and evaluate his/her confidence in the forecast; 
  • Predict volatility and incorporate volatility predictions into return forecasts; 
  • Choose between possible models of return-volatility prediction based on data; 
  • Compute Value-at-Risk (VaR) and interpret his/her VaR estimates; 
  • Explain the limitations of VaR as a measure of risk; and 
  • Apply methods learned in class to real-world financial data using R.
Assessment: 

Take-home Exam 40% 

Take-home assignment 30% 

In-class assignments 20% 

Class attendance 10%

Prerequisites: 

None; Data Analysis 1 & 2, and Investments 1 are recommended

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