Monte Carlo Methods in Finance Engineering

Take your first step towards advanced digital skills! This course is part of the RTU study course “Monte Carlo Method in Finance Engineering” and is designed for a self-paced learning experience to provide an insight into the topic and spark interest. The course is freely accessible; however, it does not offer a certificate upon completion.

The full-scale study course provides significant added value—it offers intensive practical work with digital tools and high-performance computing technologies, ensuring the development of advanced digital skills corresponding to levels 7–8 of the European Digital Competence Framework (DigComp).

If you wish to study in-depth and receive a certificate certifying the acquired DigComp competences, apply for the full study course as a guest learner through the RTU Lifelong Learning Department:

https://www.rtu.lv/lv/studijas/uznemsana/kursi-klausitajiem

talakizglitiba@rtu.lv

+371 67089439

Course Goal

This course examines the theoretical foundations and practical applications of the Monte Carlo method in financial engineering and risk management. Particular emphasis is placed on the relationship between Monte Carlo simulation techniques, the theory of stochastic differential equations, statistical inference, and computational methods. The course covers the fundamental principles of Monte Carlo simulation, methods of sample generation and analysis, and variance reduction techniques. It also addresses the simulation of autoregressive (AR) and generalized autoregressive conditional heteroskedasticity (GARCH) models, as well as diffusion approximations of discrete-time models. Further topics include simulation-based approaches to risk management and the Monte Carlo valuation of financial derivatives, including European, Asian, and American options. In addition, the course explores the application of Monte Carlo algorithms in the context of big data and their implementation on high-performance computing platforms.

Course learning outcomes

Able to independently develop simulation-based solutions in Python for financial instrument modeling and economic forecasting, and to analyze the obtained results (DigComp level 7).
Demonstrates understanding of stochastic differential equation modeling principles and their appropriate selection.
Able to design Monte Carlo algorithms for financial asset pricing (DigComp Level 8).
Possesses basic skills in using high-performance computing platforms, including Linux commands and scripts for data and program management, job execution, and result retrieval.