# 312.170 (18W) Financial Data Analysis

## Wintersemester 2018/19

Kein Anmeldezeitraum angegeben.

Erster Termin der LV
04.10.2018 12:15 - 13:45 , HS 10
... keine weiteren Termine bekannt

## Überblick

Lehrende/r
LV-Titel englisch
Financial Data Analysis
LV-Art
Vorlesung
Semesterstunde/n
2.0
ECTS-Anrechungspunkte
4.0
Anmeldungen
3
Organisationseinheit
Unterrichtssprache
Englisch
LV-Beginn
04.10.2018

## Intendierte Lernergebnisse

􏰀 The students should be able to analyse, whether a dataset of financial returns comes from Gaussian random variables.

The students should have a basic knowledge on risk measures.

The students should know the most common model used for financial time series.

􏰀 The students should understand several applications of extreme value theory in finance and insurance.

􏰀 The students should be able to work with multivariate models and how to deal with dependence.􏰀

The students should be able to fit a price process model to data.

The students should be able to simulate price processes and understand the basic concepts of Monte Carlo simulation.

## Lehrmethodik

Lecture with practical examples
Combination of slides and blackboard

## Inhalt/e

􏰀 Return distribution
􏰀 Risk measures
Financial time series
􏰀 Extreme value theory
􏰀 Multivariante models and dependence􏰀
Model calibration
Stochastic simulation methods

## Erwartete Vorkenntnisse

Knowledge on stochastics and stochastic processes.

## Literatur

A. J. McNeil, R. Frey, P. Embrechts.
Quantitative Risk Management.
Publisher: Princeton Series in Finance.

R. Carmona
Statistical Analysis of Financial Data in R.
Publisher: Springer.

P. Glassermann.
Monte Carlo Methods in Financial Engineering.
Publisher: Springer.

## Prüfungsinformationen

### Prüfungsmethode/n

There will be a final exam, presumably at the end of January.
The point scheme is as follows:
100-87 p. -> 1
86-75 p. -> 2
74-62 p. -> 3
61-50 p. -> 4
49-0 p. -> 5

### Prüfungsinhalt/e

Everything that is covered in the lecture.
If additional reading is required for the lecture, this will be clearly announced in the lecture.

### Beurteilungskriterien/-maßstäbe

The mark depends only on the number of points the student achieves at the final exam.

## Position im Curriculum

• Doktoratsprogramm Modeling-Analysis-Optimization of discrete, continuous and stochastic systems (SKZ: ---, Version: 16W.1)
• Fach: Modeling-Analysis-Optimization of discrete, continuous and stochastic systems (Pflichtfach)
• Modeling-Analysis - Optimization of discrete, continuous and stochastic systems ( 0.0h XX / 0.0 ECTS)
• 312.170 Financial Data Analysis (2.0h VO / 4.0 ECTS)
• Masterstudium Mathematics (SKZ: 401, Version: 18W.1)
• Fach: Applied Statistics (Wahlfach)
• 5.3 Financial Data Analysis ( 2.0h VO / 4.0 ECTS)
• 312.170 Financial Data Analysis (2.0h VO / 4.0 ECTS)
• Masterstudium Mathematics (SKZ: 401, Version: 18W.1)
• Fach: Applied Mathematics (Wahlfach)
• Lehrveranstaltungen aus den Vertiefungsfächern ( 0.0h XX / 12.0 ECTS)
• 312.170 Financial Data Analysis (2.0h VO / 4.0 ECTS)
• Masterstudium Technische Mathematik (SKZ: 401, Version: 13W.1)
• Fach: Statistik (Pflichtfach)
• Finanzstatistik ( 3.0h VU / 5.0 ECTS)
• 312.170 Financial Data Analysis (2.0h VO / 4.0 ECTS)
• Doktoratsstudium Doktoratsstudium der Technischen Wissenschaften (SKZ: 786, Version: 12W.4)
• Fach: Studienleistungen gem. § 3 Abs. 2a des Curriculums (Pflichtfach)
• Studienleistungen gem. § 3 Abs. 2a des Curriculums ( 16.0h XX / 32.0 ECTS)
• 312.170 Financial Data Analysis (2.0h VO / 4.0 ECTS)

## Gleichwertige Lehrveranstaltungen im Sinne der Prüfungsantrittszählung

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