Courses Master Display 2019-2020

Course Description To PDF
Course title Mathematical Finance
Course code EBC4121
ECTS credits 6,5
Assessment None
Period
Period Start End Mon Tue Wed Thu Fri
4 3-2-2020 3-4-2020 X X
Level Advanced
Coordinator Eric Beutner
For more information: e.beutner@maastrichtuniversity.nl
Language of instruction English
Goals
The principal aim of this course is to provide students with an appreciation and understanding of how the application of mathematics, particularly stochastic mathematics, to the field of finance may be used to illuminate this field and model its randomness, resulting in greater understanding and quantification of investment returns and security prices.
Description
The aim of the course is to provide students with an appreciation and understanding of the main ideas and concepts of mathematical finance. The core of mathematical finance concerns questions of pricing and hedging of financial derivatives such as options whose value depend on that of an underlying risky asset. We will discuss the general principles of continuous-time financial markets where the investor can buy and sell d+1 assets. As a special case we will consider the Black-Scholes model for a financial market. We will further point out the link between the no-arbitrage condition and certain probability measures, the so called equivalent martingale measures. In complete markets as well as in incomplete markets these measures allow to price financial derivatives in an arbitrage-free way. Moreover, we will consider probabilistic models for bond markets and apply the theory of equivalent martingale measures to the pricing of fixed income securities. Finally, we will address the issue of estimating the parameters of the probabilistic models from historical data.
Literature
Bingham, N.H., Kiesel, R. (2004). Risk-Neutral Valuation: Pricing and Hedging of Financial Derivatives, 2nd edition, Springer, London Berlin Heidelberg.
Prerequisites
Students should have knowledge of stochastic processes, in particular Brownian motion, geometric Brownian motion and the underlying stochastic differential equations. Moreover, students should be familiar with the Ito integral and the Ito formula. Knowledge of the Girsanov transformation is helpful, but not required.
Teaching methods PBL / Presentation / Lecture / Assignment
Assessment methods Participation / Written Exam
Evaluation in previous academic year For the complete evaluation of this course please click "here"
This course belongs to the following programmes / specialisations
Master Econometrics and Operations Research - Actuarial Sciences Compulsory Course(s)
Master Econometrics and Operations Research - Econometrics Elective Course(s)
Master Econometrics and Operations Research - Mathematical Economics Elective Course(s)
Master Econometrics and Operations Research - No specialisation Elective Course(s)
Master Econometrics and Operations Research - Operations Research Elective Course(s)
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