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
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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

LV-Beschreibung

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.

Beurteilungsschema

Note/Grade Benotungsschema

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)

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