Courses

This course is designed for students and researchers who want to develop professional skills in modern quantitative finance. It is offered to interested students who have had some experience with probability, statistics and software applications but have not had advanced courses in mathematical finance. Although the course assumes only a modest background it moves quickly between different fields of applications and in the end, the participant can expect to have theoretical and computational tools that are deep enough and rich enough to be relied on throughout future professional careers. The compulsory textbook is readable for the graduate student in financial engineering as well as for the inexperienced newcomer to quantitative finance who wants to get a grip on modern statistical tools in financial data analysis. The experienced reader with a bright knowledge of mathematical finance will probably skip some sections but will hopefully enjoy the various computational tools of the presented techniques. A graduate student might think that some of the econometric techniques are well known. The mathematics of risk management and volatility dynamics will certainly introduce him into the rich realm of quantitative financial data analysis. The computer inexperienced user of this course is softly introduced into the interactive course concept and will certainly enjoy the various practical examples. The textbook is an e-book which is designed as an interactive document: a stream of text and information with various hints and links to additional tools and features. The course "Advanced Methods in Quantitative Finance" consists of four parts: Preliminaries, Value at Risk, Credit Risk and Implied Volatility. The first part of the course is a quick refresher of the most important concepts needed for this course. In the second part we treat the Approximation of the Value at Risk in conditional Gaussian Models, show how the VaR can be calculated using copulae and we discuss techniques of risk assessment beyond VaR. We then quantify the risk of yield spread changes via historical simulations. The third part deals with an analysis of rating migration probabilities. The forth part is devoted to the analysis of implied volatilities and their dynamics. We start with an analysis of the implied volatility surface and show how common PCA can be applied to model the dynamics of the surface. In the next two chapters we estimate the risk neutral state price density from observed option prices and the corresponding implied volatilities. We then calculate implied binomial trees to estimate the SPD, and present a method based on a local polynomial estimation of the implied volatility and its derivatives. The proposed methods are used to develop trading strategies based on the comparison of the historical SPD and the one implied by option prices.

Literature:
www.quantlet.de

Exam: Oral Exam

The aim of applied microeconometrics is to analyze individual behavior on the basis of micro data (cross-section and panel data) of individuals, households, and firms.
The standard linear regression model is generally not applicable to micro data due to the non-metric measurement and censoring of dependent variables at the individual level, selectivity and incomplete observability of endogenous variables, and the dependence of individual observations over time. The empirical methods most frequently applied in empirical microeconomics are surveyed and several applications in empirical microeconomics are presented. Students learn how to apply these methods using micro data and the software package STATA.

Please see the attached link for further information and dates of the tutorial (Tuesday, 12pm - 2pm):
http://www.fu-berlin.de/vv/de/modul?id=71958&sm=314889

Literature:
M. Verbeek, A Guide to Modern Econometrics (4 ed.), Wiley, 2012.
A. C. Cameron and P. K. Trivedi, Microeconometrics. Methods and Applications, Cambridge University Press, 2005
W. H. Greene, Econometric Analysis (7 ed.), Pearson, 2012, Chapters 11 and 17-19.
J. M. Wooldridge, Econometric Analysis of Cross Section and Panel Data, MIT Press, 2 ed. 2010
Selected journal articles on empirical applications.

Exam: Written final exam; research paper

The WZB reading group takes places each Monday from 4pm-5 pm.

Exam: Two presentations and/or discussions
Location: to be announced

Please contact Philipp Albert (philipp.albert@wzb.eu), if you are interested in joining the reading group.

Course objectives:

Covers statistical methods relevant for the analysis of data based on structural economic models
• Discuss advantages and limitations of structural econometric models. Give students an understanding of why and when adding structure is important.
• Focus on discrete choice methods for cross section and panel data
• Provide insights into strategy (especially, identification) in important papers in structural Labour, Public & IO literature. Give a feel of how one may go about establishing a structural model.
• Establish basic estimation techniques & numerical methods such as simulation, numerical integration and discretisation; coding best-practice using Matlab, such as loops vs. vectorisation, readability vs. speed, and sustainable coding for several projects.
• The aim is to equip students with skills allowing them to carry out independent empirical research

Course organization:

• Part I is taught by Daniel Kemptner, Part II by Peter Haan, Luke Haywood, and Hannes Ullrich.
• Credit points: 12 ECTS. 5 sessions in Part I, 8 sessions in Part II. Both parts must be completed to gain credits.
• Prerequisites: skills in advanced econometric methods (Master or Ph.D. level)
• All sessions in this course take place at DIW.
• First session: 20.4.2017
• Final session: 20.7.2017 (Exam)