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Mathematical Models in Finance

General data

Course ID:	1000-1D11MMF
Erasmus code / ISCED:	11.924 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (0619) Information and Communication Technologies (ICTs), not elsewhere classified The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	Mathematical Models in Finance
Name in Polish:	Modele matematyczne w finansach
Organizational unit:	Faculty of Mathematics, Informatics, and Mechanics
Course groups:	Master seminars for Mathematics
ECTS credit allocation (and other scores):	6.00 Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load. view allocation of credits
Language:	English
Type of course:	Master's seminars
Prerequisites:	Introduction to Actuarial and Financial Mathematics 1000-135WMF Probability Theory II 1000-135RP2
Short description:	We will analyse various models of financial markets. We shall discuss not only theoretical properties of various models but also numerical aspects of pricing and hedging financial instruments.
Full description:	We will analyse various models of financial markets. We shall discuss not only theoretical properties of various models but also numerical aspects of pricing and hedging financial instruments. Every year a new topic is chosen as the main topic of the year.
Bibliography:	References will be given at the first meeting.
Learning outcomes:	Knowledge ans skills: 1. know selected financial models; 2. design a mathematical model from a description in an economic style paper; 3. prepare a presentation of variable mathematical involvement; 4. prepare a critical report on an analyzed financial model. Competence: 1. act and cooperate with experts in economy and finance;
Assessment methods and assessment criteria:	Active participation in the seminar, verbal presentation of selected papers, I year – supervisor and topic of a master thesis II year – submission of master thesis

Classes in period "Academic year 2023/24" (in progress)

Time span:	2023-10-01 - 2024-06-16	Selected timetable range: weekly course term Navigate to timetable MO TU W TH FR SEM-MGR
Type of class:	Second cycle diploma seminar, 60 hours more information
Coordinators:	Piotr Kowalczyk, Andrzej Palczewski
Group instructors:	Piotr Kowalczyk, Andrzej Palczewski
Students list:	(inaccessible to you)
Examination:	Pass/fail
Full description:	In the academic year 2023/24 the seminar will concentrate on the application of neural networks in computational finance. We will analyze the mathematical properties of neural algorithms used in the calibration of financial models and the pricing of contingent claims. The main topic of the seminar will be neural solutions of PDEs similar to the Black-Scholes equation.

Classes in period "Academic year 2024/25" (future)

Time span:	2024-10-01 - 2025-06-08	Selected timetable range: weekly course term Navigate to timetable
Type of class:	Second cycle diploma seminar, 60 hours more information
Coordinators:	Piotr Kowalczyk, Andrzej Palczewski
Group instructors:	Piotr Kowalczyk, Andrzej Palczewski
Students list:	(inaccessible to you)
Examination:	Pass/fail
Full description:	In the academic year 2023/24 the seminar will concentrate on the application of neural networks in computational finance. We will analyze the mathematical properties of neural algorithms used in the calibration of financial models and the pricing of contingent claims. The main topic of the seminar will be neural solutions of PDEs similar to the Black-Scholes equation.

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