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Learn to build robust, scalable financial models to position yourself as an expert in computational finance. At a time when the financial industry demands an increasingly complex and accurate mode, this book ensures you stay ahead of the curve by leveraging the latest advancements in programming to develop faster, more reliable, and maintainable financial software.
To begin, you ll explore key features of C++23, object-oriented programming, and template-based design patterns critical for building reusable financial components. From there, dive into a range of numerical methods, including Monte Carlo simulations, binomial and trinomial trees, and finite difference schemes. Special attention is given to practical implementation details. Every chapter is designed to guide you step by step in transforming mathematical models into efficient, production-level C++ code. You will also learn to handle exotic derivatives, stochastic volatility, and jump-diffusion models, bridging the gap between theory and practice.
In the end, you ll be equipped with the technical foundation and practical tools needed to design, implement, and analyze complex financial products. You will also be well-prepared to tackle the advanced interest rate and credit derivatives covered in further depth in De La Rosa s Advanced Quantitative Finance with Modern C++.
What You Will Learn:
- Master modern C++23 syntax and features, including object-oriented and generic programming.
- Design flexible option payoff hierarchies for code reuse.
- Apply advanced numerical techniques such as Monte Carlo, binomial/trinomial trees, and finite difference methods.
- Calculate and interpret option sensitivities (Greeks).
- Model and price exotic options, including stochastic volatility and jump-diffusion models.
- Integrate mathematical finance concepts into production-quality C++ code.
Who This Book is for: Quantitative analysts, financial engineers, researchers, and advanced developers who seek to deepen their knowledge of derivative pricing and computational finance using modern C++. Also suited for graduate students in quantitative finance or applied mathematics who want to complement their theoretical studies with robust coding skills.
Inhaltsverzeichnis
1. Introduction to Modern C++23.- 2. Components of an Object-Oriented C++ Program.- 3. Option Payoff Hierarchies in C++.- 4. Generic Programming and Template Classes in C++.- 5. Introduction to the Standard Template Library (STL) in C++23.- 6. Function Objects in C++.- 7. Matrix Classes for Quantitative Finance.- 8. Numerical Linear Algebra in C++.- 9. Black Scholes and Pricing Fundamentals.- 10. Calculating the Greeks.- 11. European Options with Monte Carlo Simulation.- 12. Binomial and Trinomial Trees.- 13. Finite Difference Methods.- 14. Asian/Path-Dependent Options with Monte Carlo.- 15. Exotic Options.- 16. Implied Volatility.- 17. Stochastic Volatility.- 18. Geometric Brownian Motion (GBM) and Jump-Diffusion Models.
Über den Autor / die Autorin
Aaron De la Rosa is a Senior Quantitative Analyst and Data Scientist with a strong background in programming, finance, and quantitative analysis. He holds an MSc in Finance and has extensive experience as a Senior Data Scientist. Aaron is proficient in Python, R, C++, and Matlab, and specializes in portfolio optimization, machine learning, deep learning, and algorithmic trading.
As a Quantitative Developer, he has expertise in market and credit risk, sentiment analysis, web scraping, natural language processing, and large language models. Aaron possesses comprehensive financial, quantitative, and modeling expertise, along with strong problem-solving abilities, excellent analytical skills, and broad financial experience.
He is a highly skilled, motivated, competent, and certified Quant with over eight years of experience in quantitative analysis and statistical modeling. Aaron is capable of providing accurate forecasts, optimizing investment portfolios, and developing projects using his knowledge of various programming languages.
Zusammenfassung
Learn to build robust, scalable financial models to position yourself as an expert in computational finance. At a time when the financial industry demands an increasingly complex and accurate mode, this book ensures you stay ahead of the curve by leveraging the latest advancements in programming to develop faster, more reliable, and maintainable financial software.
To begin, you’ll explore key features of C++23, object-oriented programming, and template-based design patterns critical for building reusable financial components. From there, dive into a range of numerical methods, including Monte Carlo simulations, binomial and trinomial trees, and finite difference schemes. Special attention is given to practical implementation details. Every chapter is designed to guide you step by step in transforming mathematical models into efficient, production-level C++ code. You will also learn to handle exotic derivatives, stochastic volatility, and jump-diffusion models, bridging the gap between theory and practice.
In the end, you’ll be equipped with the technical foundation and practical tools needed to design, implement, and analyze complex financial products. You will also be well-prepared to tackle the advanced interest rate and credit derivatives covered in further depth in De La Rosa’s Advanced Quantitative Finance with Modern C++.
What You Will Learn:
- Master modern C++23 syntax and features, including object-oriented and generic programming.
- Design flexible option payoff hierarchies for code reuse.
- Apply advanced numerical techniques such as Monte Carlo, binomial/trinomial trees, and finite difference methods.
- Calculate and interpret option sensitivities (Greeks).
- Model and price exotic options, including stochastic volatility and jump-diffusion models.
- Integrate mathematical finance concepts into production-quality C++ code.
Who This Book is for: Quantitative analysts, financial engineers, researchers, and advanced developers who seek to deepen their knowledge of derivative pricing and computational finance using modern C++. Also suited for graduate students in quantitative finance or applied mathematics who want to complement their theoretical studies with robust coding skills.
