DRE 4017 Numerical Methods in Finance and Economics
DRE 4017 Numerical Methods in Finance and Economics
This is a course in the basic tools of numerical analysis that can be used to address analytically intractable problems in finance and economics. A large class of problems cannot be analyzed with analytical tools, and numerical methods are increasingly expanding the questions we can address.
Numerical methods are vital to all types of applied financial and economic research. The generality with which the techniques will be presented in this course will make them applicable to a wide range of fields, including econometrics, corporate finance, asset pricing, resource economics, labor economics, economic theory, international trade, macroeconomics, finance, game theory, public finance, contract theory and others.
In order to learn how to use computational tools in an informed and intelligent way, this course endeavors to explain not only when and how to use various numerical algorithms but also how and why they work; in other words, the course opens up the “black boxes” and provide the students with a versatile toolbox for their own research.
- To learn elementary computer programming in on one of the following languages Python, Julia, R, Matlab, Fortran or C++.
- To learn different elementary methods for solving basic problems: differentiation, root finding, optimization, approximation, integration.
- To understand to evaluate the trade off between accuracy, speed of convergence and ease of programming.
- To learn to combine elementary methods to solve functional problems in finance and economics.
- To master basic computational tools to do quantitative research finance and economics.
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The course has three main parts:
- Elementary numerical methods on R^n
- Functional equation problems
- Solving individual choice models, heterogeneity and aggregation.
- Advanced topics (aggregate shocks, continuous time methods, default models,etc).
Computer-based tools will be used extensively, in addition to Matlab and R-Studio/R-Project: Python, Julia, C++, or Fortran.
Enrollment in a PhD programme is a general requirement for participation in PhD courses at BI Norwegian Business School.
External candidates are kindly asked to attach confirmation of enrollment in a PhD programme when signing up for a course. Other candidates may be allowed to sit in on courses by approval of the course leader. Sitting in on a course does not permit registration for the course, handing in exams or gaining credits for the course. Course certificates or confirmation letters will not be issued for sitting in on courses.
Assessments |
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Exam category: Submission Form of assessment: Written submission Weight: 15 Grouping: Individual Duration: 2 Week(s) Comment: Take-home problem set. Exam code: DRE40171 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
Exam category: Submission Form of assessment: Written submission Weight: 15 Grouping: Individual Duration: 2 Week(s) Comment: Take-home problem set. Exam code: DRE40171 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
Exam category: Submission Form of assessment: Written submission Weight: 15 Grouping: Individual Duration: 2 Week(s) Comment: Take-home problem set. Exam code: DRE40171 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
Exam category: Submission Form of assessment: Written submission Weight: 15 Grouping: Individual Duration: 2 Week(s) Comment: Take-home problem set. Exam code: DRE40171 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
Exam category: Submission Form of assessment: Written submission Weight: 40 Grouping: Individual Duration: 1 Month(s) Comment: Final Project. Exam code: DRE40171 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
Activity | Duration | Comment |
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Teaching | 30 Hour(s) | |
Student's own work with learning resources | 75 Hour(s) | Autonomous student learning (including exam preparation). |
Group work / Assignments | 75 Hour(s) | Specified learning activities (including reading). |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.