Book Image

Financial Modeling Using Quantum Computing

By : Anshul Saxena, Javier Mancilla, Iraitz Montalban, Christophe Pere
5 (1)
Book Image

Financial Modeling Using Quantum Computing

5 (1)
By: Anshul Saxena, Javier Mancilla, Iraitz Montalban, Christophe Pere

Overview of this book

Quantum computing has the potential to revolutionize the computing paradigm. By integrating quantum algorithms with artificial intelligence and machine learning, we can harness the power of qubits to deliver comprehensive and optimized solutions for intricate financial problems. This book offers step-by-step guidance on using various quantum algorithm frameworks within a Python environment, enabling you to tackle business challenges in finance. With the use of contrasting solutions from well-known Python libraries with quantum algorithms, you’ll discover the advantages of the quantum approach. Focusing on clarity, the authors expertly present complex quantum algorithms in a straightforward, yet comprehensive way. Throughout the book, you'll become adept at working with simple programs illustrating quantum computing principles. Gradually, you'll progress to more sophisticated programs and algorithms that harness the full power of quantum computing. By the end of this book, you’ll be able to design, implement and run your own quantum computing programs to turbocharge your financial modelling.
Table of Contents (16 chapters)
1
Part 1: Basic Applications of Quantum Computing in Finance
5
Part 2: Advanced Applications of Quantum Computing in Finance
10
Part 3: Upcoming Quantum Scenario

Machine learning

Machine learning in derivative pricing employs complex algorithms to predict future derivative prices, drawing from a vast dataset of historical trading data. By modeling market dynamics and identifying patterns, it provides more accurate price forecasts than traditional models. This not only reduces financial risk but also optimizes trading strategies. Furthermore, it provides insights into market behavior, assisting in the development of more resilient financial systems.

Geometric Brownian motion

We must model the underlying equities before estimating the price of derivative instruments based on their value. The geometric Brownian motion (GBM), also called the Wiener process, is the method often uses to model the stochastic process of a Brownian motion, driving the future values of an asset. It helps create trajectories that the asset price of the underlying stock may take in the future.

A stochastic or random process, here defined as the time-dependent...