Courses Master Display 2022-2023
|Course Description||To PDF|
|Course title||Quantitative Techniques for Financial Economics|
For more information: email@example.com
|Language of instruction||English|
The objectives of the course are to provide student in the Financial Economics master programme with a solid knowledge of stochastic models and econometric techniques used in the analysis of financial markets. The students should be able to read and assess the current literature on stochastic models and econometric methods used in security pricing and empirical finance and to apply the acquired techniques in practice.
PLEASE NOTE THAT THE INFORMATION ABOUT THE TEACHING AND ASSESSMENT METHOD(S) USED IN THIS COURSE IS WITH RESERVATION. A RE-EMERGENCE OF THE CORONAVIRUS AND NEW COUNTERMEASURES BY THE DUTCH GOVERNMENT MIGHT FORCE COORDINATORS TO CHANGE THE TEACHING AND ASSESSMENT METHODS USED. THE MOST UP-TO-DATE INFORMATION ABOUT THE TEACHING/ASSESSMENT METHOD(S) WILL BE AVAILABLE IN THE COURSE SYLLABUS.
The course consists of two parts.
Part A focuses on advanced econometric techniques for modelling financial time series. Topics that are covered include volatility models. Empirical applications will provide students with practical experience in analysing financial time series.
In part B, we cover and discuss the theoretical concepts and probability models underlying the pricing, construction, and hedging of (derivative) securities. Key concepts such as arbitrage pricing and risk-neutral valuation are introduced in a formal way and their implementation and use by market practitioners will be discussed.
Hull, J.C., Options, Futures and Other Derivatives, Prentice Hall, 8th edition, 2011 (or more recent edition).
Other literature will be provided or listed on the course webpage.
Solid background in finance and in statistics/econometrics (on the level of a quantitatively oriented economics/finance bachelor). Required concepts from mathematics/statistics are a.o. random variables, probability distributions, statistical tests, regression analysis, ordinary and partil derivatives, exponential function. Basic knowledge in programming (e.g. in R, MATLAB, or VBA) is suggested (introductory material for R will be provided on the course webpage before the start of the course).
An advanced level of English.
|Teaching methods (indicative; course manual is definitive)||PBL / Presentation / Lecture / Assignment|
|Assessment methods (indicative; course manual is definitive)||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||