![]() A geometric Brownian motion is used instead, where the logarithm of the stock price has stochastic behaviour. A standard Brownian motion cannot be used as a model here, since there is a non-zero probability of the price becoming negative. A vanilla equity, such as a stock, always has this property. For this we need to assume that our asset price will never be negative. In the subsequent articles, we will utilise the theory of stochastic calculus to derive the Black-Scholes formula for a contingent claim. The derivative of a random variable has both a deterministic component and a random component, which is normally distributed. The fundamental difference between stochastic calculus and ordinary calculus is that stochastic calculus allows the derivative to have a random component determined by a Brownian motion. Ito's Lemma is a stochastic analogue of the chain rule of ordinary calculus. A fundamental tool of stochastic calculus, known as Ito's Lemma allows us to derive it in an alternative manner. The Binomial Model provides one means of deriving the Black-Scholes equation. This process is represented by a stochastic differential equation, which despite its name is in fact an integral equation. The physical process of Brownian motion (in particular, a geometric Brownian motion) is used as a model of asset prices, via the Weiner Process. The main use of stochastic calculus in finance is through modeling the random motion of an asset price in the Black-Scholes model. In quantitative finance, the theory is known as Ito Calculus. Instead, a theory of integration is required where integral equations do not need the direct definition of derivative terms. This rules out differential equations that require the use of derivative terms, since they are unable to be defined on non-smooth functions. Many stochastic processes are based on functions which are continuous, but nowhere differentiable. For mathematicians, this book can be used as a first text on stochastic calculus or as a companion to more rigorous texts by a way of examples and exercises.Stochastic calculus is the area of mathematics that deals with processes containing a stochastic component and thus allows the modeling of random systems. ![]() The book covers models in mathematical finance, biology and engineering. Using such structure, the text will provide a mathematically literate reader with rapid introduction to the subject and its advanced applications. It contains many solved examples and exercises making it suitable for self study.In the book many of the concepts are introduced through worked-out examples, eventually leading to a complete, rigorous statement of the general result, and either a complete proof, a partial proof or a reference. It is also suitable for researchers to gain working knowledge of the subject. It may be used as a textbook by graduate and advanced undergraduate students in stochastic processes, financial mathematics and engineering. Not everything is proved, but enough proofs are given to make it a mathematically rigorous exposition.This book aims to present the theory of stochastic calculus and its applications to an audience which possesses only a basic knowledge of calculus and probability. In biology, it is applied to populations' models, and in engineering it is applied to filter signal from noise. In finance, the stochastic calculus is applied to pricing options by no arbitrage. It also gives its main applications in finance, biology and engineering. This book presents a concise and rigorous treatment of stochastic calculus.
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