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Financial Modeling Using Quantum Computing

You're reading from   Financial Modeling Using Quantum Computing Design and manage quantum machine learning solutions for financial analysis and decision making

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Product type Paperback
Published in May 2023
Publisher Packt
ISBN-13 9781804618424
Length 292 pages
Edition 1st Edition
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Concepts
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Authors (4):
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Iraitz Montalban Iraitz Montalban
Author Profile Icon Iraitz Montalban
Iraitz Montalban
Anshul Saxena Anshul Saxena
Author Profile Icon Anshul Saxena
Anshul Saxena
Javier Mancilla Javier Mancilla
Author Profile Icon Javier Mancilla
Javier Mancilla
Christophe Pere Christophe Pere
Author Profile Icon Christophe Pere
Christophe Pere
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Toc

Table of Contents (16) Chapters Close

Preface 1. Part 1: Basic Applications of Quantum Computing in Finance
2. Chapter 1: Quantum Computing Paradigm FREE CHAPTER 3. Chapter 2: Quantum Machine Learning Algorithms and Their Ecosystem 4. Chapter 3: Quantum Finance Landscape 5. Part 2: Advanced Applications of Quantum Computing in Finance
6. Chapter 4: Derivative Valuation 7. Chapter 5: Portfolio Management 8. Chapter 6: Credit Risk Analytics 9. Chapter 7: Implementation in Quantum Clouds 10. Part 3: Upcoming Quantum Scenario
11. Chapter 8: Simulators and HPC’s Role in the NISQ Era 12. Chapter 9: NISQ Quantum Hardware Roadmap 13. Chapter 10: Business Implementation 14. Index 15. Other Books You May Enjoy

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...

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