FINM334, Statistical Risk Management

Spring 2008

The Final Exam will be in KPTC (Kersten) 106.

For the final exam you can bring midterm sheet plus one new double paged sheet.

Exams
Literature
Preliminary Scedule
Computing
Instructors
Times and places
Lecture Notes
Homework
Data files

Exams

Midterm will be on Monday 28 April 6pm-8pm. Place will be announced later. Students are allowed to bring one standard page of handwritten information, double paged. Problems will be based on Lectures 1-4 plus Homeworks 1-3.

For those students that cannot make it to the midterm the final exam will count 85%.

The final exam will be on Monday 9 June 6pm-9pm at KPTC (Kersten) 106. Students are allowed to bring their midterm sheet plus one new standard page of handwritten information, double paged. Problems are based on Lectures 1-7 plus Homeworks 1-7.

Literature

Notes are prepared every week and can be downloaded from this site. Useful additional literature is

Recommended Literature

A.J. McNeil, R. Frey and E. Embrechts. Quantitative Risk Management. Princeton Series in Finance, 2005

Y. Malevergne and D. Sornette, Extreme Financial Risks, from Dependence to Risk Management. Springer, 2006. To manuscript

E. Jondeau, S-H Poon and M. Rockinger. Financial Modeling under Non-Gaussian Distributions. Springer, 2007. To manuscript

Of these, my notes will rely most heavily on the book by McNeil and al. So if your budget just allows you to buy one book, this is the one.

Preliminary Scedule

Lecture 1: Geometric Brownian motion, univariate statistical distributions, basic notions of risk management, q-q plots, generation of random variables.

Lecture 2: Multivariate normal distribution, maximum likelihood estimators, delta method, goodness of fit tests, optimization methods.

Lecture 3: Extreme value theory.

Lecture 4: Time series. ARMA models. Nonstationary time series.

Lecture 5: ARCH and GARCH time series. Random volatility models. Applications to risk management.

Lecture 6: Multivariate statistical distributions, multivariate extreme value theory

Lecture 7: Copulas

Lecture 8: Measures of association, multivariate risk management.

Computing

Examples are based on the programming language R, and so are answers to homeworks. If you want, you can use other languages, but then on your own responsibility. A very useful book with R (and Splus) is

W.N. Venables and B.D. Ripley. Modern Applied Statistics with S. Springer

Instructors
Lecturer: Jostein Paulsen Email: jostein@galton.uchicago.edu

TA:
Martin Hunting,hunting@uchicago.edu
Nick Longo,nmichalo@math.uchicago.edu
Eric Patterson,eric@math.uchicago.edu

TA: Stamford
Ramesh Kadambi Ramesh.Kadambi@ubs.com
Times and places