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Computational Finance and Risk Management (minor)
Basic information
Code:
SCI3170
Extent:
20–25
ECTS
Curriculum:
2024–2026
Level:
Advanced studies
Language of learning:
English
Theme:
Global business dynamics
Target group:
All Aalto students
Teacher in charge:
Mikko Jääskeläinen
Ruth Kaila
Ahti Salo
Administrative contact:
Tarja Timonen
Organising department:
Department of Industrial Engineering and Management
School:
School of Science
Prerequisites:
The minor requires basic knowledge in engineering mathematics and probability theory. Bachelor-level minor SCI3034 Systems Sciences is recommended.
Quotas and restrictions:
No quotas for the minor, but some elective courses may have space for a limited number of students. The minor is available for master’s level students.
Application process:
Open for all students of Aalto University. It is recommended that the student discuss the choice of courses with Ruth Kaila.
Content and structure of the minor
About the minor
Welcome to the Minor in Computational Finance and Risk Management, where principles of managing financial assets intersect with the study of uncertainty. In the landscape of the finance, understanding and effectively mitigating risks is paramount. This minor provides you with a specialized and comprehensive exploration of the strategies and tools employed to identify, analyze, and manage financial risks.
From market volatility to credit uncertainties, financial risk management is a critical discipline ensuring the resilience and success of businesses and organizations worldwide. The curriculum will engage you with topics ranging from mathematical finance and stochastic modeling to derivative pricing and asset management. The program emphasizes the development of programming skills and the understanding of quantitative analysis tools, ensuring that the students are well-prepared to tackle the complexities of risk in today’s financial environment.
You might be interested in Fintech and exploring the power of technology in reshaping traditional financial services. In an era defined by digital disruption and rapid advancements, the minor offers students a unique opportunity to understand, harness, and drive the digital evolution of finance. Whether aspiring to pursue a career in risk analysis, investment banking, asset management, or financial technology, the Minor in Computational Finance and Risk Management equips you with the expertise needed to thrive in a world
where traditional financial practices are continually challenged by technological advancements. Join us in exploring the convergence of finance and engineering in tomorrow’s global financial system.
After completing the minor, student can:
- Understand foundational concepts in finance and financial risk
management, including asset pricing, risk management, portfolio theory, and
derivatives. - Apply quantitative methods, such as mathematical modeling and
statistical analysis, to assess financial risk and make informed investment
decisions. - Understand and critically evaluate various financial instruments, markets,
and investment strategies within the context of risk management. - Demonstrate proficiency in utilizing computational tools and techniques to
analyze financial data and solve complex financial problems. - Develop programming skills in languages commonly used in finance, such
as Python, R, or MATLAB, to implement financial models and algorithms. - Collaborate in interdisciplinary teams to solve real-world financial
problems. Communicate effectively about complex financial concepts and
analyses to diverse stakeholders including investors, managers, and regulators.
The minor consists of 15–20 credits of compulsory core courses and 0-10 credits of elective courses from two subject areas (i) systems and operations research, (ii) computational methods.
Content
| Code | Course name | ECTS | Period |
|---|---|---|---|
| TU-E2211 | Financial Risk Management with Derivatives 1 D | 5 | I-II |
Choose 2-3 of the following courses |
|||
| TU-E2221 | Financial Risk Management with Derivatives 2 | 5 | III-IV |
| TU-E2231 | Machine Learning in Financial Risk Management | 5 | III-IV |
| MS-E2114 | Investment Science | 5 | I-II |
Optional courses 0-10 ECTSThe credit requirement is fullfilled by completing a sufficient number of courses from the following. Any courses except those from the subject area of the student's major can be selected. |
|||
| Mathematical methods (Dept of Mathematics and Systems Analysis) | |||
| MS-E2121 | Linear Optimization | 5 | III-IV |
| MS-E1600 | Probability Theory | 5 | I |
| MS-E1604 | Brownian Motion and Stochastic Analysis* | 5 | IV |
| MS-E2112 | Multivariate Statistical Analysis | 5 | III-IV |
| MS-E2160 | Stochastic Programming and Robust Optimization | 5 | I-II |
| MS-E2117 | Riskianalyysi | 5 | III-IV |
| MS-E2177 | Seminar on Case Studies in Operation Research | 5-7 | III-V |
| Computational Methods (Dept of Computer Science) | |||
| CS-E4715 | Supervised Machine Learning | 5 | I-II |
| CS-E4825 | Probabilistic Machine Learning | 5 | III-IV |
| CS-E4890 | Deep Learning | 5 | III-IV |
| CS-E4891 | Deep Generative Models | 5 | IV-V |
| CS-E5710 | Bayeasian Data Analysis | 5 | I-II |
| ELEC-C9420 | Introduction to Quantum Technology | 5 | II |
* No teaching 2025-2026
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