This is a non-fiction, advanced finance reference designed to teach robust financial modeling using practical, spreadsheet-based methods. The central aim is to equip students, researchers, and practitioners with computational tools for understanding finance fundamentals. The tone is practical and precise, with a clear path from concepts to working models that you can adapt in real-world scenarios.
The content is organized around hands-on tasks and real-world data. Detailed Excel spreadsheets accompany explanations, and the topics span corporate finance, bonds and yield curves, portfolio theory, options and derivatives, and Monte Carlo methods. The edition revises valuation approaches, adds coverage of second-order and third-order Greeks for options, value at risk (VaR), and Monte Carlo techniques, with implementations in R and Python. While the first five parts establish core modeling skills, the later sections expand into Excel and R issues and Python implementations via an auxiliary website. Knowledge of VBA or R is not required to grasp the main material.
Readers move through the material by building models step by step, experimenting with inputs, and reproducing results from up-to-date data. The experience is hands-on, with templates you can adapt, code you can study, and clear explanations that make complex concepts approachable. Itโs well-suited for advanced finance courses and for professionals who want to combine modeling practice with a solid understanding of underlying theory.
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Key content elements: Corporate finance models, bond and yield curve models, portfolio theory, options and derivatives, Monte Carlo methods, VaR, and Greeks; Excel-based templates and reproducible workflows.
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Learning outcomes: Build and customize financial models, interpret valuations and risk metrics, apply Monte Carlo simulations, analyze sensitivity and scenario impacts.
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Illustration and writing style: Clear, step-by-step explanations paired with practical spreadsheets and code examples.
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Interactive features: DIY templates in Excel, sample R and Python code, and an auxiliary website with additional exercises and VBA/Python implementations.
After reading, you gain practical modeling capabilities you can apply to valuation, risk management, and decision support. Youโll approach problems with a confident method, understand how changes in inputs affect outcomes, and carry a durable, data-driven mindset into research or professional work.
