Probability

Uncertainty is a part of life – be it stock price movement or a common natural phenomenon like the movement of a cyclone, tossing a coin, or even analysis of an engineering component. From sixteenth-century mathematicians to modern-day data scientists, everyone has tried and is continuously doing the same to decode “uncertainty” and give a stable shape to systems. Probability theory is the tool that helps them to do so. Despite having umpteen books, papers, and articles, this topic is still a gray area for many. This chapter introduces probability with examples in Python, and it is a must-have for a deep understanding of modeling financial instruments. A plethora of reference materials on this is already available both in online and physical form. You will get enough ideas on formulations and mathematical details from those. This chapter intends not to deluge you that much with the same stuff again but rather discuss interpretations of the different topics from the financial modeling perspective. Of course, theory and derivations are integral parts of our discussion, but we focus on discussing more why than what.