Lecture Notes In Financial Modelling With Python
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Lecture Notes in Financial Modelling with Python
Lecture Notes in Financial Modelling with Python is an essential eBook that compiles a series of presentations by Fabio Dias, showcasing his approach to teaching financial modeling. Covering a wide range of foundational and advanced topics—including machine learning, portfolio selection, financial planning, panel data models, and value at risk (VaR)—this book is both a theoretical guide and practical resource. Each chapter is supported by code examples in Python, making it easy for readers to implement models and techniques on their own. Ideal for students, educators, and financial professionals, this eBook brings complex concepts to life, equipping readers with the tools and skills to tackle real-world financial challenges.
Lecture Notes In Entrepreneurial Finance For The Digital Economy
Author: Peter Joakim Westerholm
language: en
Publisher: World Scientific
Release Date: 2024-06-27
This book is intended to be used as a basis for developing courses in entrepreneurial finance. While many universities, particularly in the United States, have entrepreneurial finance on their curriculum, there is often a gap between the large selection of entrepreneurship courses and courses providing applicable hard skills in finance and accounting. Early-stage ventures cannot succeed without capital and careful management of cash flow for example. Entrepreneurs need skills, such as how to negotiate with investors, so that they don't end up giving up the control of their venture too early. This book aims to fill this gap by providing guidelines for how successful courses can be set up to train finance, accounting, and corporate strategy students for a career in the start-up and venture capital industry.
Derivatives Analytics with Python
Supercharge options analytics and hedging using the power of Python Derivatives Analytics with Python shows you how to implement market-consistent valuation and hedging approaches using advanced financial models, efficient numerical techniques, and the powerful capabilities of the Python programming language. This unique guide offers detailed explanations of all theory, methods, and processes, giving you the background and tools necessary to value stock index options from a sound foundation. You'll find and use self-contained Python scripts and modules and learn how to apply Python to advanced data and derivatives analytics as you benefit from the 5,000+ lines of code that are provided to help you reproduce the results and graphics presented. Coverage includes market data analysis, risk-neutral valuation, Monte Carlo simulation, model calibration, valuation, and dynamic hedging, with models that exhibit stochastic volatility, jump components, stochastic short rates, and more. The companion website features all code and IPython Notebooks for immediate execution and automation. Python is gaining ground in the derivatives analytics space, allowing institutions to quickly and efficiently deliver portfolio, trading, and risk management results. This book is the finance professional's guide to exploiting Python's capabilities for efficient and performing derivatives analytics. Reproduce major stylized facts of equity and options markets yourself Apply Fourier transform techniques and advanced Monte Carlo pricing Calibrate advanced option pricing models to market data Integrate advanced models and numeric methods to dynamically hedge options Recent developments in the Python ecosystem enable analysts to implement analytics tasks as performing as with C or C++, but using only about one-tenth of the code or even less. Derivatives Analytics with Python — Data Analysis, Models, Simulation, Calibration and Hedging shows you what you need to know to supercharge your derivatives and risk analytics efforts.