Advanced Quantitative Finance with Modern C++: Interest Rate Modeling and Advanced Derivatives
暫譯: 現代 C++ 高級量化金融：利率模型與高級衍生品
de la Rosa, Aaron
商品描述
From the elegance of the Black-Scholes equation to the complexity of multi-factor interest rate models and hybrid derivatives, this book is your comprehensive guide to quantitative finance, complete with 15+ advanced C++ projects using QuantLib and Boost.
You'll move seamlessly from mathematical foundations to real-world implementation, building a professional-grade toolkit for pricing, risk analysis, and calibration. Inside, you will learn core option pricing methods, master single-and multi-factor interest rate models, and construct and calibrate trees and lattices for advanced derivatives. You will also explore cutting edge products: exotic multi-asset options, hybrid derivatives, credit instruments, and cross-currency swaps.
Packed with practical source code, step-by-step calibrations, and performance-tuned Boost integration, this book bridges the gap between academic finance and production-grade quant development. Whether you're a quant developer, financial engineer, or an advanced student, you'll gain the skills to design, implement, and deploy derivatives pricing models ready for the trading floor.
What You Will Learn
- Understand the mathematics behind Black-Scholes, Vasicek, Hull-White, CIR, BDT, Black-Karasinski, and other core models.
- Apply finite difference schemes, trinomial trees, and Monte Carlo simulations for derivative pricing.
- Build and value swaps, swaptions, FRAs, bonds, callable/convertible debt, and multi-curve term structures.
- Implement barrier, multi-asset, hybrid, and structured products in C++.
- Model credit default swaps, cross-currency swaps, and total return structures.
- Use QuantLib and Boost to create production-grade pricing engines and calibration tools.
- Employ Gaussian models, market models, and global optimizers for fitting market data.
- Integrate code into professional workflows, ensuring speed, accuracy, and maintainability.
Who This Book is for:
Quantitative developers, financial engineers, traders, analysts, and graduates students using C++, QuantLib, Boost, and robust tools to price, hedge, and manage risk for complex financial instruments--and for software engineers aiming to bridge theory and industry practice in quantitative finance.
Optional prerequisite: Mastering Quantitative Finance with Modern C++: Foundations, Derivatives, and Computational Methods, for readers who want to build a solid foundation before tackling the advanced models and projects in this book.
商品描述(中文翻譯)
從 Black-Scholes 方程的優雅到多因子利率模型和混合衍生品的複雜性，本書是您量化金融的全面指南，包含 15 個以上使用 QuantLib 和 Boost 的高級 C++ 專案。
您將無縫地從數學基礎過渡到實際應用，建立一個專業級的工具包，用於定價、風險分析和校準。在書中，您將學習核心選擇權定價方法，掌握單因子和多因子利率模型，並為高級衍生品構建和校準樹狀圖和格子。您還將探索尖端產品：異國多資產選擇權、混合衍生品、信用工具和跨貨幣掉期。
本書充滿實用的源代碼、逐步的校準和性能優化的 Boost 整合，彌合了學術金融與生產級量化開發之間的鴻溝。無論您是量化開發者、金融工程師還是高級學生，您都將獲得設計、實施和部署準備好進入交易市場的衍生品定價模型的技能。
您將學到的內容：
- 理解 Black-Scholes、Vasicek、Hull-White、CIR、BDT、Black-Karasinski 和其他核心模型背後的數學。
- 應用有限差分法、三項樹和蒙地卡羅模擬進行衍生品定價。
- 建立和評估掉期、掉期選擇權、FRAs、債券、可贖回/可轉換債務和多曲線期限結構。
- 在 C++ 中實現障礙、多資產、混合和結構性產品。
- 建模信用違約掉期、跨貨幣掉期和總回報結構。
- 使用 QuantLib 和 Boost 創建生產級定價引擎和校準工具。
- 使用高斯模型、市場模型和全局優化器來擬合市場數據。
- 將代碼整合到專業工作流程中，確保速度、準確性和可維護性。
本書適合對象：
量化開發者、金融工程師、交易員、分析師以及使用 C++、QuantLib、Boost 和穩健工具來定價、對沖和管理複雜金融工具風險的研究生——以及旨在將理論與量化金融的行業實踐相結合的軟體工程師。
可選的先修知識：掌握現代 C++ 的量化金融：基礎、衍生品和計算方法，適合希望在處理本書中的高級模型和專案之前建立堅實基礎的讀者。
作者簡介
Aaron De la Rosa is a Fixed Income Quantitative Researcher and C++ Quant Developer specializing in the design and implementation of advanced models for derivative pricing and risk management. With a strong focus on option valuation, particularly exotic and path-dependent instruments, Aaron bridges the gap between theoretical finance and real-world application through high-performance C++ development.
He has extensive experience leveraging QuantLib, the industry-standard open-source library for quantitative finance, to build scalable and production-level solutions in fixed income, structured products, and derivative pricing. His work spans the full spectrum of financial engineering--from modeling stochastic processes and volatility surfaces to constructing efficient numerical solvers such as finite difference methods, Monte Carlo simulations, and lattice-based trees.
Aaron's passion lies in translating complex financial mathematics into robust, maintainable C++ code. His contributions are guided by modern software engineering principles, with an emphasis on clean architecture, reusable components, and computational efficiency. His expertise is not only technical but also deeply grounded in financial theory, enabling him to craft solutions that are both mathematically sound and software-engineered for performance.
When he's not developing quantitative models or enhancing pricing frameworks, Aaron actively contributes to the financial developer community and explores new frontiers in interest rate modeling, credit derivatives, and modern C++ design.C++ design.
作者簡介(中文翻譯)
Aaron De la Rosa 是一位固定收益量化研究員及 C++ 量化開發者，專注於衍生品定價和風險管理的先進模型設計與實施。Aaron 對選擇權評價有著強烈的關注，特別是異型及路徑依賴工具，他透過高效能的 C++ 開發，架起了理論金融與實務應用之間的橋樑。
他擁有豐富的經驗，利用QuantLib，這個業界標準的開源量化金融庫，來構建可擴展且具生產級別的固定收益、結構性產品和衍生品定價解決方案。他的工作涵蓋了金融工程的全範疇——從建模隨機過程和波動率曲面，到構建高效的數值解法，如有限差分法、蒙地卡羅模擬和基於格子的樹。
Aaron 的熱情在於將複雜的金融數學轉化為穩健且可維護的 C++ 代碼。他的貢獻受到現代軟體工程原則的指導，強調乾淨的架構、可重用的組件和計算效率。他的專業不僅技術性強，還深深根植於金融理論，使他能夠設計出數學上合理且在性能上經過軟體工程優化的解決方案。
當他不在開發量化模型或增強定價框架時，Aaron 積極貢獻於金融開發者社群，並探索利率建模、信用衍生品和現代 C++ 設計的新領域。
