Department of Engineering
Dean:Dr. Ármann Gylfason
Email:ru@ru.is
Website:http://www.ru.is/tvd
TeachersView
MSc in Biomedical Engineering
Semesters:4
Years:2
ETCS:120
About majorÍ heilbrigðisverkfræði er verkfræðilegum aðferðum beitt til að fást við líffræðileg og læknisfræðileg viðfangsefni, s.s. uppgötvun og þróun nýrrar tækni og aðferða við greiningu og meðferð sjúkdóma. Hér sameinast ólíkar greinar eins og stærðfræði og eðlisfræði við sameindalíffræði og lífeðlisfræði í leit að lausnum heilsufarslegra viðfangsefna. MSc námið er mótað að kröfum Verkfræðingafélags Íslands um fullnaðarmenntun í verkfræði.
Learning OutcomesView
Education cycle2
Degree titleMSc in Biomedical Engineering
Legend
Mandatory course on majorTeaching language
Optional course on majorPrerequisites for course
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Vorönn/Spring 2024
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More infoSleepElectiveT-424-SLEEECTS 6
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More infoInternship in Engineering IElectiveT-706-INT1ECTS 6
More infoInternship in Engineering IIElectiveT-706-INT2ECTS 6
More infoSystems BiologyElectiveT-765-SYBIECTS 8
More infoProject Management and Strategic PlanningElectiveT-803-VERKECTS 8
More infoDerivatives and risk managementElectiveT-814-DERIECTS 8
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More infoPower System OperationElectiveT-867-POSYECTS 8
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Sumar/Summer 2024
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More infoExchange StudiesElectiveX-699-EXCHECTS 30
Haustönn/Fall 2024
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More infoMedical Modelling and ImagingCoreT-862-IMAGECTS 8
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Vorönn/Spring 2025
More infoSleepElectiveT-424-SLEEECTS 6
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More infoInternship in Engineering IElectiveT-706-INT1ECTS 6
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Year
1. yearPrint
SemesterSpring 2025
Level of course5. Second cycle, intermediate
Type of courseCore
PrerequisitesNo prerequisites.
ScheduleTaught for 12 weeks.
Lecturer
Luca De Gennaro Aquino
Content
Year of study:      4th year (1st year MSc).Semester:             Spring.
Level of course: 6. Second cycle, advanced.Type of course:   Core for MSc Financial Engineering, elective for other programs.Prerequisites:       Appropriate undergraduate degree.Probability and Stochastic Processes (T-606-PROB)Derivatives (T-503-AFLE)Applied Probability (T-811-PROB)Other recommended prerequisites: Risk Management (T-602-RISK)Data Mining and Machine Learning (T-809-DATA)Financial Engineering of the Firm (T-814-FINA)Schedule:             12 weeks of 2 x 2.5-hour (3-period) lectures per week.Supervisor:          Ralph Rudd.Lecturer:               Ralph Rudd.The core focus of this course is study and use of quantitative techniques in risk management.The approach is centered around the study of portfolio value change, where we consider general portfolios that can consist of a mixture of equity, fixed income, credit, and derivative instruments.
The risk of a portfolio is represented by its loss distribution, which is a function of its risk factors. In this course we examine how to model and manipulate this loss distribution. The modelling proceeds via modelling the risk factors themselves, and we investigate the properties of common risk factors in the real world. The manipulation of the loss distribution is done by modifying the portfolio constituents, usually through hedging.
To assess the risk of a portfolio, we look at summary measures of its loss distribution, primarily value-at-risk and expected shortfall. We examine the properties of these risk measures and investigate their shortcomings.
Learning outcome - Objectives
Learning Outcomes: After completing this course, students will have a good knowledge of how to identify, quantify and manage derivative and market risk. The learning outcome can be broken down into the following sub-outcomes:
  • Be able to define and explain the fundamental concepts used in the measurement and management of financial risk.
  • Be able to analyse and model changes in portfolio value and derive loss distributions.
  • Apply the loss distribution framework to different kinds of asset and liability portfolios.
  • Apply different quantitative approaches to measuring risk, with particular focus on risk measures that are calculated from loss distributions, like value-at-risk and expected shortfall.
  • Be able to build realistic models for risk management purposes by considering the empirical properties of fundamental risk factors and developing models that share these properties.
  • To model multivariate loss distributions with complex cross-dependencies.
  • Be able to aggregate and disaggregate risk across multi-instrument portfolios.
Course assessment
Assessment consists of theoretical and computational exams.
Reading material
No reading material found.
Teaching and learning activities
In-person, interactive lectures.
Language of instructionEnglish
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