MVE220 / MSA400 Financial risk Spring 25
This course covers some modern techniques and concepts for measuring risk in a financial setting. It requires no prior knowledge of finance, but is quite mathematical.
This page contains the program of the course: lectures and projects. Other information, such as learning outcomes, teachers, literature and examination, are in a separate course PM.
Literature
Stuart Coles, Introduction to Statistical Modeling of Extreme Values. Available in STORE or here. We will mainly cover 2.6.3-4, 2.6.7, 2.7, 3.1-4, 4. Everything related to profile likelihood is omitted. We focus on the versions of the theorems and proofs that are in the slides, but the examples and concepts that are covered in Coles are important to understand.
In the Markov Theory slides, we focus on the ones with blue titles.
Carl Lindberg, Lecture notes:
Comment on Structural Credit Risk 20231211.pdf
ExtremeValueTheory 20250314.pd
Fisher Information 20250314.pd
Risk Project Slides
CDOs and the financial crisis.pdf
Dot-Com.pdf Elin Johansson Emelie Lemann .pdf
Long Term Capital Management.pdf
Tulipmania_ Beyond the Bloom.pdf
Old exams
Program
The schedule of the course is in TimeEdit.
Lectures
| Week | Topic | Content |
|---|---|---|
| 1 | Introduction to Financial Risk, Extreme value theory | Derivation of the extreme value distributions and the Generalized Pareto Distribution |
| 2 | Statistical Modeling: Classical EVT with Statistics, Threshold models, Value-at-Risk, Expected shortfall | Distributions, independence, Fisher Information |
| 3 | Week 2 continued, CHARM days, Risk projects intro | General EV distributions with statistics |
| 4 | EVT statistics continued | Generalized Pareto distribution, Peaks over Threshold |
| 5 | Insurance risk, structural Credit risk | Cramér-Lundberg model, structural credit risk models |
| 6 | Markov Theory, Credit risk | Basic properties, hitting times, Strong Markov property, invariant distributions, Markov based credit risk models. |
| 7 | Project supervision and presentations | |
| 8 | Project supervision |
Recommended exercises
Learn all the derivations and examples covered in the lectures.
Statistical project
All students will, in groups of 2, apply the GEV and PoT models to financial data, e g from a stock or an equity index, along the lines of the example statistical analysis in Coles. In addition, you should make use of autocorrelation plots of the data and the absolute values of the data, as well as plots of the data in order of observation to assess whether the data can be viewed as iid. (You should make sure to have at least 50 data points). The students should in the PoT case calculate VaR and Expected Shortfall for their time series, given an investment of 1 million "moneys" (I e, use SEK if you analyze Swedish stocks, USD if you analyze American stocks, and so on..). I will supervise these projects during weeks 6-8. Each group will present their projects to me in class. The projects are MANDATORY, but they are also quite relevant to the exam. The projects may be done in a programming language, or tool, of your choosing.
Presentation risk project
Groups of (ideally) 2 people will some time during study weeks 7-8 present a topic related to financial risk (such as the Madoff scandal, Enron, LTCM, the Financial Crisis) during a 10 minute presentation. The presenting groups will hand in 1-2 pages (ChatGPT-tools are allowed, but check your facts!) The best presentations will be rewarded in the sense that I will choose exam questions from their presentations, so doing the project can be regarded as studying for the exam. The presentation and handing in of a report is MANDATORY. Let me know what event you are studying to avoid several groups choosing the same one.
Course summary:
| Date | Details | Due |
|---|---|---|