Introduction to Stochastic Calculus | QuantStart (2024)

Stochastic calculus is the area of mathematics that deals with processes containing a stochastic component and thus allows the modeling of random systems. Many stochastic processes are based on functions which are continuous, but nowhere differentiable. This rules out differential equations that require the use of derivative terms, since they are unable to be defined on non-smooth functions. Instead, a theory of integration is required where integral equations do not need the direct definition of derivative terms. In quantitative finance, the theory is known as Ito Calculus.

The main use of stochastic calculus in finance is through modeling the random motion of an asset price in the Black-Scholes model. The physical process of Brownian motion (in particular, a geometric Brownian motion) is used as a model of asset prices, via the Weiner Process. This process is represented by a stochastic differential equation, which despite its name is in fact an integral equation.

The Binomial Model provides one means of deriving the Black-Scholes equation. A fundamental tool of stochastic calculus, known as Ito's Lemma allows us to derive it in an alternative manner. Ito's Lemma is a stochastic analogue of the chain rule of ordinary calculus. 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. The derivative of a random variable has both a deterministic component and a random component, which is normally distributed.

In the subsequent articles, we will utilise the theory of stochastic calculus to derive the Black-Scholes formula for a contingent claim. For this we need to assume that our asset price will never be negative. A vanilla equity, such as a stock, always has this property. A standard Brownian motion cannot be used as a model here, since there is a non-zero probability of the price becoming negative. A geometric Brownian motion is used instead, where the logarithm of the stock price has stochastic behaviour.

We will form a stochastic differential equation for this asset price movement and solve it to provide the path of the stock price. In order to price our contingent claim, we will note that the price of the claim depends upon the asset price and that by clever construction of a portfolio of claims and assets, we will eliminate the stochastic components by cancellation. We can then finally use a no-arbitrage argument to price a European call option via the derived Black-Scholes equation.

Related Articles

Introduction to Stochastic Calculus | QuantStart (2024)

FAQs

What is stochastic calculus in simple terms? ›

Stochastic calculus is a branch of mathematics that operates on stochastic processes. It allows a consistent theory of integration to be defined for integrals of stochastic processes with respect to stochastic processes. This field was created and started by the Japanese mathematician Kiyosi Itô during World War II.

How difficult is stochastic calculus? ›

Stochastic calculus is genuinely hard from a mathematical perspective, but it's routinely applied in finance by people with no serious understanding of the subject. Two ways to look at it: PURE: If you look at stochastic calculus from a pure math perspective, then yes, it is quite difficult.

Is stochastic calculus still used? ›

Stochastic calculus is widely used in quantitative finance as a means of modelling random asset prices.

What is the prerequisite for stochastic calculus? ›

Stochastic Calculus assumes a prior, calculus-based course in probability. For example: you must be comfortable working with probability densities, integrating to get means and variances, computing conditional probabilities, etc.

What level is stochastic calculus? ›

Stochastic calculus is not something that you would encounter in an elementary calculus sequence. It is typically a graduate level course for applied mathematics and statistics majors, and requires a firm grasp of real analysis, differential equations, and probability theory, among other subjects.

What is stochastic calculus useful for? ›

Stochastic calculus is the mathematics used for modeling financial options. It is used to model investor behavior and asset pricing. It has also found applications in fields such as control theory and mathematical biology.

Who is the father of stochastic calculus? ›

Professor Kiyosi Ito is well known as the creator of the modern theory of stochastic analysis. Although Ito first proposed his theory, now known as Ito's stochastic analysis or Ito's stochastic calculus, about fifty years ago, its value in both pure and applied mathematics is becoming greater and greater.

What calculus did Einstein use? ›

Note that while Newtonian physics (as in classical physics) often used 3-dimensional, multi-variable calculus, with x, y, z variables and x, y, z planes for instance, some Einsteinian physics required multi-variable calculus -at a level of 5-dimensions.

Do hedge funds use stochastic calculus? ›

Despite the abundance of stochastic models for stocks, commodities and market indices, relatively few stochastic models have been developed for hedge funds. That is not entirely surprising since hedge funds are not too trans- parent; there are only a few sources of data, with infrequent voluntary reporting.

What are the three stochastic methods? ›

In this chapter we discuss three classes of stochastic methods: two-phase methods, random search methods and random function methods, as well as applicable stopping rules.

Is stochastic calculus used in quantum mechanics? ›

This is done in a general setting with Brownian motion and Quantum mechanics as special limits, where one obtains respectively the heat equation and the Schrödinger equation. The derivation heavily relies on tools from Lagrangian mechanics and stochastic calculus.

Is stochastic calculus used in data science? ›

Stochastic processes are indispensable in the realm of data science, especially in fields where uncertainty is a defining feature. The ability to model randomness and make probabilistic predictions is crucial for navigating the complexities of the natural and social worlds.

Is stochastic process math or statistics? ›

Stochastic processes are widely used as mathematical models of systems and phenomena that appear to vary in a random manner. Examples include the growth of a bacterial population, an electrical current fluctuating due to thermal noise, or the movement of a gas molecule.

What is a simple example of stochastic? ›

Simply put, a stochastic process is any mathematical process that can be modeled with a family of random variables. A coin toss is a great example because of its simplicity.

What is an example of stochastic? ›

Examples include the growth of some population, the emission of radioactive particles, or the movements of financial markets. There are many types of stochastic processes with applications in various fields outside of mathematics, including the physical sciences, social sciences, finance, and engineering.

What is the difference between stochastic process and calculus? ›

Calculus deals with deterministic equations, like y = x. Stochastic calculus deals with stochastic processes, like random walks. In a simple random walk, y starts at zero when x=0. A fair coin is flipped and y goes to either +1 or -1 when x = 1.

Top Articles
Latest Posts
Article information

Author: Edwin Metz

Last Updated:

Views: 6378

Rating: 4.8 / 5 (78 voted)

Reviews: 85% of readers found this page helpful

Author information

Name: Edwin Metz

Birthday: 1997-04-16

Address: 51593 Leanne Light, Kuphalmouth, DE 50012-5183

Phone: +639107620957

Job: Corporate Banking Technician

Hobby: Reading, scrapbook, role-playing games, Fishing, Fishing, Scuba diving, Beekeeping

Introduction: My name is Edwin Metz, I am a fair, energetic, helpful, brave, outstanding, nice, helpful person who loves writing and wants to share my knowledge and understanding with you.