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## Datascience for Finance

### Objectives

The objective of the class is to provide students with:

• a set of powerful modern econometric methods (tools)
• a deep understanding and critical assessment of the presented methods
• the ability to independently apply the presented methods to empirical problems
• the foundations they need for other classes of the M.Sc. in Finance.

### Contents

1. Ordinary Least Squares (OLS)

• The Algebra of Least Squares
• Finite-sample properties of the OLS
• Asymptotic theory
• Hypothesis Testing
• Fundamental Concepts of Time Series Analysis
• Testing Conditional Homoskedasticity
• Estimation with Parameterized Conditional Heteroskedasticity
• Autocorrelation tests and AR models
• Forecasting

2. Maximum Likelihood (ML)

• Extremum Estimators
• Consistency
• Asymptotic Normality
• Hypothesis Testing
• Limited dependent variable models (Probit, Logit, Tobit)
• ARMA models
• Forecasting

Matlab programming: students learn to program in Matlab during the semester, and apply the presented methods to empirical problems.

### References

J. Hamilton (1994), “Time Series Analysis” (Princeton University Press).

F. Hayashi (2000), “Econometrics” (Princeton University Press).

W. Greene (2011), “Econometric Analysis” (7th edition, Prentice Hall).

### Evaluation

#### First attempt

Exam:
Written 3h00 hours
Documentation:
Not allowed
Calculator:
Allowed with restrictions
Evaluation:

Grade will be determined based on

• Final Exam (E)
• Assignments (A)
• Project (P)

according to the rule:

Final grade = 0.6*E + 0.2*A + 0.2*P.

Final exam (E) is a written closed book exam, non programmable calculator permitted.

#### Retake

Exam:
Written 3h00 hours
Documentation:
Not allowed
Calculator:
Allowed with restrictions
Evaluation:

Retake exam grade (RE) is the Final grade (i.e. assignments and project no longer count).

Retake exam (RE) is a written closed book exam, non programmable calculator permitted.

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