Checking date: 15/05/2024

Course: 2024/2025

Front-Office Algorithms
(16754)
Master in Financial Sector Technologies: FinTech (Plan: 461 - Estudio: 313)
EPI

Coordinating teacher: FERNANDEZ ARREGUI, SUSANA

Department assigned to the subject: Computer Science and Engineering Department

Type: Electives
ECTS Credits: 3.0 ECTS

Course:
Semester:

Requirements (Subjects that are assumed to be known)
Algorithms and Data Structures
Objectives
The objective of this course is, on one hand, to demonstrate the importance of programming and software engineering in the field of quantitative finance, and on the other hand, to expose students to the classic algorithms in this field. This will help guide students in acquiring the necessary tools and knowledge in this discipline.
Skills and learning outcomes
Description of contents: programme
1. Introduction to the financial calculus 2. Interest rates product valuation 3. Interest rates curve construction 4. Option valuation algorithms 5. Montecarlo simulation method 6. Sensitivities computation 7. Valuation adjustments on derivatives 8. Development of practices and projects
Learning activities and methodology
Learning activities AF1: Theory classes: Basic theoretical knowledge and skills will be presented in large groups. Attendance: 100% AF3: Theory - practice classes:Theory lessons and resolution of practical exercises. Attendance: 0%-100% AF4: Laboratory sessions: Small groups classes, in which problems proposed to the students are discussed and developed using the computer. Attendance: 0%-100% AF5: Tutorials: Tutorials in person (one-by-one) or videoconference. Attendance: 0%-100% AF2: e-Learning activities: forum about subjects, recorded-contents and other educational activities. Attendance: 0% AF7: Individual student's work: individual student's work to complete the rest of activities and to prepare the exams. Attendance: 0% Teaching methodologies: MD1: Theoretical lectures to develop the main concepts of the subject MD3: Practical cases and problems that students must solve individually or in small groups MD4: Oral presentations and discussions in class under teacher moderation MD5: Practical work individually or in small groups MD6: e-Learning activities For the practices and projects, students have to develop works on algorithms for front-office and risk measurement, such as discounted cash flow, plain vanilla products valuation, first-order sensitivities, etc. These implementations will be carried out using programming languages and techniques more frequently used in the quantitative financial sector, focusing on performance and software extensibility. As in other subjects, theoretical content can be delivered through online teaching systems such as recorded lectures or discussion forums, as well as traditional methods like individual or group projects. For more practical content, in-person attendance in labs can be combined with individual or group work outside the classroom through Remote Classroom, along with student monitoring and tutoring via forums and other discussion mechanisms. Other e-learning strategies will also be employed, such as self-assessment of completed work, all supported through Global Classroom. In cases where specific software with a license that is not easily accessible to students is required for a particular practice or lab, in-person attendance in those lab sessions will be emphasized, at the expense of other sessions that are more suitable for a blended learning approach.
Assessment System
• % end-of-term-examination 60
• % of continuous assessment (assigments, laboratory, practicals...) 40

Basic Bibliography
• John C. Hull. Options, Futures, and Other Derivatives. Person Prentice Hall.