MSc, PhD, Dr Sc.
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I teach MSO4110 "Risk Measurement" and MSO4160 "Portfolios and Risk" modules for the MSc "Financial Mathematics" program at the School of Science and Technology.
Forecasting the magnitude of the next financial crisis is demanding, yet it is a task of utmost importance for every financial risk manager or data analyst.
For instance, on the “Black Monday” October 19, 1987, the markets fell by more than 20% in one day. Would it be possible to possible to forecast the magnitude of the crash using data available on the eve of the crash?
The answer is “Yes”!
At MSO4110 module students learn advanced methods of evaluating measures of financial risk from dependent heavy-tailed data. In particular, we study measures of risk called Value-at-Risk (VaR) and Expected Shortfall (ES) or CVaR.
Traditional measures of risk are static – they barely change with the inflow of new information and hence are only suitable for long-term investment decisions. MSO4110 introduces students to a dynamic measure of risk, m_{TA}, that changes actively as data changes. Measure m_{TA} appears more suitable for forecasting trends changes and short-term risks.
The module equips students with tools for accurate evaluation of the scale of the possible future market crashes. It provides an introduction to dynamic risk measurement and related statistical techniques (see chapter 10 of the recommended textbook S.Novak (2011) Extreme value methods with applications to finance. London: Chapman & Hall/CRC Press. ISBN 9781439835746).
Prerequisites: some mathematical skills, knowledge of undergraduate statistics.
Areas of Expertise:
In particular:
Particular results:
Possible areas of PhD supervision:
* Inference on heavy-tailed distributions (Statistics)
* Accuracy of normal and compound Poisson approximation (Probability Theory)