Modulhandbuch (Module manual)

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M.184.4451 Financial Econometrics and Quantitative Risk Management
(Financial Econometrics and Quantitative Risk Management)

Koordinator (coordinator):	Prof. Dr. Yuanhua Feng
Ansprechpartner (contact):	Dominik Schulz (dominik.schulz[at]upb.de)
Credits:	5 ECTS
Workload:	150 Std (h)
Semesterturnus (semester cycle):	SoSe
Studiensemester (study semester):	1-4
Dauer in Semestern (duration in semesters):	1

Lehrveranstaltungen (courses):
	Nummer / Name (number / title)	Art (type)	Kontaktzeit (contact time)	Selbststudium (self-study)	Status (P/WP) (status)	Gruppengröße (group size)
a)	K.184.44511 / Financial Econometrics and Quantitative Risk Management (Vorlesung)				P
b)	K.184.44512 / Financial Econometrics and Quantitative Risk Management (Übung)				P
Wahlmöglichkeiten innerhalb des Moduls (Options within the module):
Keine

Empfohlene Voraussetzungen (prerequisites):
W1471 Basic Principles of Statistics I W1472 Basic Principles of Statistics II W2474 Einführung in die Ökonometrie or W4479 Econometrics

Inhalte (short description):
This module will introduce the students to time series analysis, financial econometrics and their applications. The course consists of three parts: Part I – Introduction to time series analysis; Part II: Introduction to financial econometrics; Part III: Introduction to multivariate time series. Main topics of Part I are: basic concepts of time series, weak and strong stationarity, well known operators, AR (autoregressive), MA (moving average), ARMA, ARIMA (autoregressive integrated moving average) and RW (random walk) processes, properties of those processes, estimation, model selection and forecasting using the selected model, additive model for time series with trend and seasonality, smoothing of such time series. Part II deals with the following topics: properties of financial time series, ARCH (autoregressive conditional heteroskedasticity), GARCH (generalized ARCH), estimation and application of GARCH, VaR (value at risk) and CVaR (conditional VaR), different extensions of GARCH, ACD (autoregressive conditional duration) for modeling high-frequency data, semiparametrisc GARCH models with trend in volatility. In Part III VAR (vector AR) processes and MGARCH (multivariate GARCH) models will be introduced briefly.

Inhalte (short description):

This module will introduce the students to time series analysis, financial econometrics and their applications. The course consists of three parts: Part I – Introduction to time series analysis; Part II: Introduction to financial econometrics; Part III: Introduction to multivariate time series.

Main topics of Part I are: basic concepts of time series, weak and strong stationarity, well known operators, AR (autoregressive), MA (moving average), ARMA, ARIMA (autoregressive integrated moving average) and RW (random walk) processes, properties of those processes, estimation, model selection and forecasting using the selected model, additive model for time series with trend and seasonality, smoothing of such time series.

Part II deals with the following topics: properties of financial time series, ARCH (autoregressive conditional heteroskedasticity), GARCH (generalized ARCH), estimation and application of GARCH, VaR (value at risk) and CVaR (conditional VaR), different extensions of GARCH, ACD (autoregressive conditional duration) for modeling high-frequency data, semiparametrisc GARCH models with trend in volatility.

In Part III VAR (vector AR) processes and MGARCH (multivariate GARCH) models will be introduced briefly.

Lernergebnisse (learning outcomes):

Fachkompetenz Wissen (professional expertise):
Studierende... know the most important elements of time series analysis. know economic time series and data resources. know financial econometrics and its related related applications. know the effect of dependent observations, asymptotic analysis, approximate least squares, unconditional and conditional maximum-likelihood, quasi maximum linkelihood.
Fachkompetenz Fertigkeit (practical professional and academic skills):
Studierende... can use time series models, models for financial data, model estimation and model selection, forecasting, smoothing techniques, analysis and forecasting of business cycles. test theory empirically and through simulation.
Personale Kompetenz / Sozial (individual competences / social skills):
Studierende... work in groups. present and discuss solutions to exercises.
Personale Kompetenz / Selbstständigkeit (individual competences / ability to perform autonomously):
Studierende... receive training for further learning in Econometrics and Statistics. learn skills to deal with large and complex data sets.

Prüfungsleistungen (examinations)
Art der Modulprüfung (type of modul examination): Modulteilprüfungen
	Art der Prüfung (type of examination)	Umfang (extent)	Gewichtung (weighting)
a)	Projektarbeit	approx. 10 pages	40.00 %
b)	Klausur	90 minutes	60.00 %

Studienleistung / qualifizierte Teilnahme (module participation requirements)
Nein

Voraussetzungen für die Teilnahme an Prüfungen (formal requirements for participating in examinations)
Keine

Voraussetzungen für die Vergabe von Credits (formal requirements for granting credit points)
Die Vergabe der Credits erfolgt, wenn die Modulnote mindestens „ausreichend“ ist

Gewichtung für Gesamtnote (calculation of overall grade)
Das Modul wird mit der Anzahl seiner Credits gewichtet (Faktor: 1)

Verwendung des Moduls in den Studiengängen (The module can be selected in the following degree programmes)
M.Sc. International Business Studies, M.Sc. Betriebswirtschaftslehre, M.Sc. International Economics and Management, M.Sc. Management Information Systems, M.Sc. Wirtschaftsinformatik, M.Sc. Wirtschaftspädagogik, M. Ed. Wirtschaftspädagogik

Umfang QT (participation requirements):

Lernmaterialien, Literaturangaben (learning material, literature):

Teilnehmerbegrenzung (participant limit):
Keine

Sonstige Hinweise (additional information):
Unterrichtssprachen: English

Sonstige Hinweise (additional information):

Unterrichtssprachen: English

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