About the course
Quantum Computing for Finance is an advanced course in the emerging field of quantum computing for finance. This introductory course will develop a foundation in quantitative finance to step into the emerging multidisciplinary field of Quantum computing for finance.
Learning objectives
This course is designed for all those learners or quantum enthusiasts who wish to develop their skills in quantitative finance and start a career in the finance industry. This course will teach you:
- the basics of derivative products,
- the Black-Scholes-Merton model for pricing vanilla derivatives, and
- how to compute the price of exotic options using computational and modelling techniques.
The computational and modelling techniques detailed in the course through the concepts of linear algebra, multiple statistical tools, and python programming language will help you develop the fundamental concepts required for an understanding of quantum algorithms and more advanced concepts in computational finance. This course is designed for all those who wish to develop their skills and start a career in quantitative finance.
What skills you will learn
- Apply analytical solutions like the Black-Scholes-Merton model and implement numerical techniques to solve complex pricing problems for exotic derivatives
- Approach financial challenges with a computational mindset, structuring problems to be solved via algorithms and simulations.
- Gain hands-on experience writing efficient Python code to implement pricing algorithms, perform Monte Carlo simulations, and model financial systems.
- Build, test, and refine computational models for pricing and risk assessment, focusing on robustness, accuracy, and scalability.
- Acquire foundational knowledge and technical skills necessary for understanding quantum algorithms and exploring their application in financial modelling.
Course Structure
- Introduction to derivative products
- The price of derivative products
- Risk-neutral pricing
- Fundamental theorem of asset pricing
- Pricing options with binomial model
- Introduction to stochastic calculus
- Introduction to the Black-Scholes-Merton model
- The Black-Scholes formula for a European call
- The Black-Scholes partial differential equation
- The Greeks
- Introduction to hedging
- Volatility modelling
- The Monte-Carlo method part 1
- The Monte-Carlo method part 2
- The finite difference method
- The Euler scheme
- Summary
Course Prerequisites
A general understanding of mathematics and financial terms.
Duration
The estimated duration to complete this course is approximately 12 hours.
Assessment
Pass all the quizzes at the end of each lesson by scoring 80% or more to complete the course and earn the certificate upon completion.
Who Should Enrol
This course is ideal for students, researchers, and professionals who are eager to delve into the field of quantitative finance and computational techniques. Whether you are a beginner looking to build a strong foundation in finance or an experienced practitioner aiming to enhance your skills, this course offers valuable insights and practical knowledge. It is particularly suited for those interested in careers in trading, risk management, financial analysis, and quantitative research.
Why Enrol
Enrolling in the “Introduction to Quantitative and Computational Finance” course is essential for understanding quantum algorithms for finance. The course covers fundamental concepts in quantitative finance, such as derivative products and the Black-Scholes-Merton model, which are crucial for grasping financial complexities. It emphasises computational techniques and modelling, using linear algebra and statistical methods, directly preparing you for the mathematical rigor of quantum computing. By mastering Python programming and data analysis, you’ll develop the skills needed to implement quantum algorithms. This course bridges classical computational finance with quantum computing, providing the foundational knowledge to explore and innovate in quantum finance.
