Options and Black-Scholes Model

After covering stocks in the book's second part, this chapter delves into one of the key driving factors of the financial market: options. With the short introduction discussed in Chapter 2 , as of now, you must know that options are not direct assets but derivatives on top of underlying assets. Readers of this book who do regular share market trading should be well accustomed to the useability aspects of options. But what may not be known is the underlying statistical theory, or more precisely the background of some cryptic-looking formulae they might regularly use to trade in options. As said in Chapter 3 , estimating the premium paid in option trading is our fundamental problem. In this process, we will learn the Black-Scholes model, a powerful tool that can help you understand and predict option prices and estimate Greeks (different rates on option value), which are crucial for risk management. We will also explore the design of Python components, a practical and efficient way to perform these computations. What I said about probability theory in Chapter 2 also holds here, i.e., countless books have been written on option modeling and Black-Scholes theory, and many available resources are lying everywhere in the forms of books, online tutorials, videos, and whatnot. But I always felt something needed to be added; some are heavy on theory with no hands-on items, or a pile of dry Jupyter Notebooks implementing some mysterious formulae with no theoretical background explained. In this chapter, I will try to construct a bridge between these two extreme borders, providing a theoretical background as well as practical implications. Let’s begin this journey!