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## Time Series

• Teacher(s):
• Course given in: English
• ECTS Credits: 3 credits
• Schedule: Spring Semester 2019-2020, 2.0h. course (weekly average)
•  sessions
•  course website
• Related programme: Master of Science (MSc) in Actuarial Science

[warning] This course syllabus is currently edited by the professor in charge. Please come back in a few days. --- For your information only, here is the old syllabus :

### Objectives

Important examples of time series include monthly number of claims filed from an insurance portfolio, cash-flow data over time, monthly climate data (temperature, snowfall, rainfall), the number of sun spots observed over time, etc. Insurance and finance applications aim at explaining the observed time series in order to understand the underlying phenomenon and eventually build models for future predictions. Typically, time series data contain information on trends, business cycle, sesonality and are subject to residual errors.

This course shall discuss both probabilistic and statistical aspects of stationary and non-stationary times series including the multivarite setup and extreme-value models. R will be the main platform for the illustration of various concepts and the statistical analysis of real/simulated time series data.

### Contents

• Introduction to time series data
• ARMA Models: Basics
• Probabilistic Background
• ARMA Models: Revisited
• Financial Time Series
• Estimation & Prediction
• Non-stationary & Multivariate Time Series

The course will be accompanied by exercises and diverse R programming tasks.

### References

Brockwell, P.J., Davis, R.A. (2002) Introduction to time series and forecasting. Second Edition, Springer
Ruppert, D., Matteson, D.S. (2015) Statistics and data analysis for financial engineering, with R examples. Second Edition, Springer
Shumway, R.H., Stoffer, D.S. (2011) Time series analysis and its applications, with R examples. Third Edition, Springer
Embrechts, P., Klüpelberg, C., and Mikosch, T. (1997) Modeling extremal events for finance and insurance. Springer

### Pre-requisites

Basics in probability

### Evaluation

#### First attempt

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#### Retake

Exam:
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