Stochastic Calculus for Finance I - by Steven Shreve (Hardcover) (2024)

Book Synopsis

This book evolved from the first ten years of the Carnegie Mellon professional Master's program in Computational Finance. The contents of the book have been used successfully with students whose mathematics background consists of calculus and calculus-based probability. The author does not assume familiarity with advanced mathematical concepts from measure-theoretic probability, but rather develops the necessary tools from this subject informally within the text. Many classroom-tested examples, exercises, and intuitive arguments are presented throughout the book.

From the Back Cover

Stochastic Calculus for Finance evolved from the first ten years of the Carnegie Mellon Professional Master's program in Computational Finance. The content of this book has been used successfully with students whose mathematics background consists of calculus and calculus-based probability. The text gives both precise statements of results, plausibility arguments, and even some proofs, but more importantly intuitive explanations developed and refine through classroom experience with this material are provided. The book includes a self-contained treatment of the probability theory needed for stchastic calculus, including Brownian motion and its properties. Advanced topics include foreign exchange models, forward measures, and jump-diffusion processes.

This book is being published in two volumes. The first volume presents the binomial asset-pricing model primarily as a vehicle for introducing in the simple setting the concepts needed for the continuous-time theory in the second volume.

Chapter summaries and detailed illustrations are included. Classroom tested exercises conclude every chapter. Some of these extend the theory and others are drawn from practical problems in quantitative finance.

Advanced undergraduates and Masters level students in mathematical finance and financial engineering will find this book useful.

Steven E. Shreve is Co-Founder of the Carnegie Mellon MS Program in Computational Finance and winner of the Carnegie Mellon Doherty Prize for sustained contributions to education.

Review Quotes

From the reviews of the first edition:

Steven Shrevea (TM)s comprehensive two-volume Stochastic Calculus for Finance may well be the last word, at least for a while, in the flood of Mastera (TM)s level books.... a detailed and authoritative reference for "quantsa (formerly known as "rocket scientistsa ). The books are derived from lecture notes that have been available on the Web for years and that have developed a huge cult following among students, instructors, and practitioners. The key ideaspresented in these works involve the mathematical theory of securities pricing based upon the ideas of classical finance.

...the beauty of mathematics is partly in the fact that it is self-contained and allows us to explore the logical implications of our hypotheses. The material of this volume of Shrevesa (TM)s text is a wonderful display of the use of mathematical probability to derive a large set of results from a small set of assumptions.

In summary, this is a well-written text that treats the key classical models of finance through an applied probability approach. It is accessible to a broad audience and has been developed after years of teaching the subject. It should serve as an excellent introduction for anyone studyin the mathematics of the classical theory of finance.

-- SIAM, 2005

"This is the first of the two-volume series evolving from the authora (TM)s mathematics courses in M.Sc. Computational Finance program at Carnegie Mellon University (USA). The content of this book is organized such as to give the reader precise statements of results, plausibility arguments, mathematical proofs and, more importantly, the intuitive explanations of the financial andeconomic phenomena. Each chapter concludes with summary of the discussed matter, bibliographic notes, and a set of really useful exercises." (Neculai Curteanu, Zentralblatt MATH, Vol. 1068, 2005)

From the reviews of the first edition:

Steven Shreve??'s comprehensive two-volume Stochastic Calculus for Finance may well be the last word, at least for a while, in the flood of Master??'s level books.... a detailed and authoritative reference for "quants??? (formerly known as "rocket scientists???). The books are derived from lecture notes that have been available on the Web for years and that have developed a huge cult following among students, instructors, and practitioners. The key ideaspresented in these works involve the mathematical theory of securities pricing based upon the ideas of classical finance.

...the beauty of mathematics is partly in the fact that it is self-contained and allows us to explore the logical implications of our hypotheses. The material of this volume of Shreves??'s text is a wonderful display of the use of mathematical probability to derive a large set of results from a small set of assumptions.

In summary, this is a well-written text that treats the key classical models of finance through an applied probability approach. It is accessible to a broad audience and has been developed after years of teaching the subject. It should serve as an excellent introduction for anyone studyin the mathematics of the classical theory of finance.

-- SIAM, 2005

"This is the first of the two-volume series evolving from the author??'s mathematics courses in M.Sc. Computational Finance program at Carnegie Mellon University (USA). The content of this book is organized such as to give the reader precise statements of results, plausibility arguments, mathematical proofs and, more importantly, the intuitive explanations of the financial and economic phenomena. Each chapter concludes with summary of the discussed matter, bibliographic notes, and a set of really useful exercises." (Neculai Curteanu, Zentralblatt MATH, Vol. 1068, 2005)

Steven Shreves comprehensive two-volume Stochastic Calculus for Finance may well be the last word, at least for a while, in the flood of Masters level books.... a detailed and authoritative reference for quants (formerly known as rocket scientists). The books are derived from lecture notes that have been available on the Web for years and that have developed a huge cult following among students, instructors, and practitioners. The key ideaspresented in these works involve the mathematical theory of securities pricing based upon the ideas of classical finance.

...the beauty of mathematics is partly in the fact that it is self-contained and allows us to explore the logical implications of our hypotheses. The material of this volume of Shrevess text is a wonderful display of the use of mathematical probability to derive a large set of results from a small set of assumptions.

In summary, this is a well-written text that treats the key classical models of finance through an applied probability approach. It is accessible to a broad audience and has been developed after years of teaching the subject. It should serve as an excellent introduction for anyone studyin the mathematics of the classical theory of finance.

-- SIAM, 2005

Stochastic Calculus for Finance I - by  Steven Shreve (Hardcover) (2024)

FAQs

Is stochastic calculus still useful? ›

Stochastic calculus will continue to be an important tool for modeling financial markets: Even though stochastic calculus has its limitations, it is still a valuable tool for modeling and analyzing financial markets.

What level is stochastic calculus? ›

Stochastic calculus is usually taught as the third semester of a probability sequence in math PhD programs. That makes it a third year course, as you can't take probability until you've had a year of measure theory and integration.

What is the application of stochastic calculus in finance? ›

The primary use of stochastic calculus in finance is for modeling the random motion of an asset price in the Black–Scholes model. The physical process of Brownian motion (specifically geometric Brownian motion) is used to model asset prices via the Weiner process.

What is the difference between stochastic calculus and 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.

Is stochastic calculus for finance hard? ›

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.

Do hedge funds use stochastic calculus? ›

Consider an investment bank that wants to price an option or a hedge fund wanting to implement an optimal trading strategy. These tasks require understanding how asset prices move, the risks involved, and a mathematical model to capture these movements - enter Stochastic Calculus.

What is an example of a stochastic process in finance? ›

There are many different types of stochastic processes that are used in finance, but I will focus on few of them.
  • Brownian Motion.
  • Geometric Brownian Motion.
  • Jump Process.
  • Ornstein-Uhlenbeck Process.
  • GARCH Model.
  • Jump-diffusion Model.
  • Cox-Ingersoll-Ross Model.
  • Levy Process.
Jul 25, 2023

Who invented stochastic calculus? ›

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.

Do actuaries use stochastic processes? ›

Stochastic models are particularly useful in forecasting, in which the actuary produces estimates of results in future years, not just a current year valuation.

What are the 4 types of calculus? ›

The main concepts of calculus are :
  • Limits.
  • Differential calculus (Differentiation).
  • Integral calculus (Integration).
  • Multivariable calculus (Function theory).

What math is calculus most similar to? ›

Algebra and Calculus though belong to different branches of math, they are inseparably related to each other. Looking into algebra vs calculus, applying basic algebraic formulas and equations, we can find solutions to many of our day-to-day problems.

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.

Do traders use stochastic calculus? ›

Calculus plays a significant role in the financial market. From stochastic calculus to algorithmic trading and the Greeks, calculus is used to make predictions and optimize trading decisions. The Golden Ratio is embedded in the stock market and is used to identify trends and make informed decisions.

Why use stochastic instead of random? ›

Random is used to indicate no biasing or no way of knowing. Stochastic is used to describe a process. These 'definitions' are general usage. There is no rule that prohibits interchanging the usage.

Is stochastic calculus useful in machine learning? ›

The combination of stochastic calculus and machine learning allows the model to capture the intricate dynamics of volatility, leading to more accurate pricing predictions. Another practical application is risk assessment.

Are stochastic processes useful? ›

Since then, stochastic processes have become a common tool for mathematicians, physicists, engineers, and the field of application of this theory ranges from the modeling of stock pricing, to a rational option pricing theory, to differential geometry.

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