312.170 (18W) Financial Data Analysis
Überblick
- Lehrende/r
- LV-Titel englisch Financial Data Analysis
- LV-Art Vorlesung
- Semesterstunde/n 2.0
- ECTS-Anrechnungspunkte 4.0
- Anmeldungen 3
- Organisationseinheit
- Unterrichtssprache Englisch
- LV-Beginn 04.10.2018
Zeit und Ort
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 inkl. Einsatz von eLearning-Tools
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 BenotungsschemaPosition 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)
-
Modeling-Analysis - Optimization of discrete, continuous and stochastic systems (
0.0h XX / 0.0 ECTS)
-
Fach: Modeling-Analysis-Optimization of discrete, continuous and stochastic systems
(Pflichtfach)
- 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)
-
5.3 Financial Data Analysis (
2.0h VO / 4.0 ECTS)
-
Fach: Applied Statistics
(Wahlfach)
- 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)
-
Lehrveranstaltungen aus den Vertiefungsfächern (
0.0h XX / 12.0 ECTS)
-
Fach: Applied Mathematics
(Wahlfach)
- 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)
-
Finanzstatistik (
3.0h VU / 5.0 ECTS)
-
Fach: Statistik
(Pflichtfach)
- 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)
-
Studienleistungen gem. § 3 Abs. 2a des Curriculums (
16.0h XX / 32.0 ECTS)
-
Fach: Studienleistungen gem. § 3 Abs. 2a des Curriculums
(Pflichtfach)