MATH0094 Market Risk and Portfolio Theory

MATH0094 Market Risk and Portfolio Theory

MATH0094 Market Risk and Portfolio Theory Year: 2021-2022 Code: MATH0094 Value: 15 UCL credits (= 7.5 ECTS) Term: 1 Structure: On Campus Assessment: In-class test with programming component (20%) and a fi- nal examination (80%). To pass the course, students must obtain an overall weighted mark of 50%. Pre-requisites: None Lecturers: Dr C Garcia Trillos Course description and objectives Risk is an intrinsic element in financial markets. Its quantitative modelling and understanding is a cornerstone of modern financial theory, as it is essential to many activities like choosing investment strategies, calculating capital require- ments and creating new financial products. This module aims to study quantitatively (by using several mathematical tools from probability, optimisation, linear algebra,...) the effects of market risk un- der some modelling assumptions. We will pay particular attention to the effects associated to decision making for investors and regulators. Important aspects related to the implementation of these concepts will be highlighted. Recommended texts K. Back, Asset pricing and portfolio choice theory. Oxford University Press, 2010. H. F¨ollmerand A. Schied, Stochastic finance: an introduction in discrete time. Walter de Gruyter, 2011. A. J. McNeil, R. Frey, and P. Embrechts, Quantitative risk management: Con- cepts, techniques and tools. Princeton university press, 2015. 1 Detailed syllabus 1. Mathematical modelling of financial markets in discrete time. Main as- sumptions and properties. 2. Utility functions: properties, absolute and relative risk aversion, examples, related concepts. 3. Portfolio choice: Consumption-investment problems. Performance mea- surement (RORAC) and efficient frontiers. 4. Risk, risk management and risk treatment. Risk measures. Notable ex- amples: value at risk, expected shortfall. Properties: coherent and convex risk measures. 5. Practical aspects: Factor models, risk measure estimation, backtesting. Implementation in Python. 2.

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