The 2016 Methods Summer Programme is now closed. Details for the 2017 Programme will be available soon.
The implementation of sound quantitative risk models is a vital task for all financial institutions, and this trend has accelerated in recent years after the last financial crisis. This course provides a self-contained introduction to both theoretical and practical implementation of various techniques in risk management. We draw on diverse quantitative disciplines, from probability to statistics, from actuarial science to quantitative finance. Main topics include: risk factor models, risk measures and their statistical estimation, multivariate factor models, dimensional reduction techniques, copulas, measure of dependence on extreme events. We work with real financial data and aim to provide hands-on experience on practical applications.
Course Suitability
This course is designed for postgraduates and professionals who are interest in analytical techniques in risk management and who have some background in probability and statistics.
Course benefits
After completing this course students will:
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Gain knowledge of important analytical techniques to measure risk in financial market.
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Develop skills to implement these techniques on real financial data.
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Gain experience transferring these techniques to other applications.
Pre-requisites
At least one semester of calculus, and at least one semester of probability and statistics. Some understanding in financial markets.
This course is a self-contained introduction to probabilistic and statistical methods in risk management. It aims to provide hands-on experience implementing these methods.
The course consists of five daily lectures of three hours each, supported by four afternoon computer-based practical classes lasting an hour and a half, which will allow course participants to implement the lecture material in R.
This course starts with risk factor models and loss distributions, illustrated via various examples in stock, derivative, and bond portfolios. Notion of coherent risk measures are introduced. Value at risk and its statistical estimation are presented. Multivariate factor models are introduced and analysed: covariance and correlation estimates, multivariate normal distributions and their testing, dimensional reduction techniques. The theory of copulas is introduced: meta distributions, tail dependence, fitting copulas to data, measure of dependence on extreme events. The extreme value theory is briefly introduced at the end.
All theoretical materials will be implemented on R using real financial and insurance data.
Main text
Lecture slides and necessary materials will be provided.
Indicative reading:
A.McNeil, R.Frey, P.Embrechts, Quantitative Risk Management: Concepts, Techniques, Tools; Princeton Series in Finance
Software used
R, no previous experience is expected.
Assessment
A 2-hour final examination will take place on the Friday afternoon.
Dr. Hao Xing is an associate professor in Department of Statistics. His research mainly focuses on applying probabilistic and statistical methods to control and optimization problem rising from economics and finance.
Please note: A full timetable will be provided at registration on Monday 15 August. The below timetable contains approximate hours only.
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3
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3
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3
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3
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3
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1.5
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1.5
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1.5
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1.5
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Exam
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