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Online course materials for MATH67101Stochastic Calculus
Unit code: | MATH67101 |
Credit Rating: | 15 |
Unit level: | Level 6 |
Teaching period(s): | Semester 1 |
Offered by | School of Mathematics |
Available as a free choice unit?: | N |
Requisites
NoneAdditional Requirements
Students are not permitted to take, for credit, MATH47101 in an undergraduate programme and then MATH67101 in a postgraduate programme at the University of Manchester, as the courses are identical.
Aims
The course unit aims to provide the basic knowledge necessary to pursue further studies/applications where stochastic calculus plays a fundamental role (e.g. Financial Mathematics).
Overview
The stochastic integral (Ito's integral) with respect to a continuous semimartingale is introduced and its properties are studied. The fundamental theorem of stochastic calculus (Ito's formula) is proved and its utility is demonstrated by various examples. Stochastic differential equations driven by a Wiener process are studied.
Learning outcomes
On successful completion of this course unit students will:
- understand the concept of the stochastic integral (Ito's integral);
- be able to apply Ito's formula to smooth functions of continuous semimartingales;
- know basic facts and theorems of stochastic calculus;
- understand the concept of the stochastic differential equation driven by a Wiener process.
Assessment Further Information
End of semester examination: three hours weighting 100%
Syllabus
- The Wiener process (standard Brownian motion): Review of various constructions. Basic properties and theorems. Brownian paths are of unbounded variation. [6 lectures]
- The Ito's integral with respect to a Wiener process: Definition and basic properties. Continuous local martingales. The quadratic variation process. The Kunita-Watanabe inequality. Continuous semimartingales. The Ito's integral with respect to a continuous semimartingale: Definition and basic properties. Stochastic dominated convergence theorem. [10]
- The Ito's formula: Statement and proof. Integration by parts formula. The Levy characterization theorem. The Cameron-Martin-Girsanov theorem (change of measure). The Dambis-Dubins-Schwarz theorem (change of time). [10]
- The Ito's-Clark theorem. The martingale representation theorem. Optimal prediction of the maximum process. [4]
- Stochastic differential equations. Examples: Brownian motion with drift, geometric Brownian motion, Bessel process, squared Bessel process, the Ornstein-Uhlenbeck process, branching diffusion, Brownian bridge. The existence and uniqueness of solutions in the case of Lipschitz coefficients. [6]
Recommended reading
- Rogers, L. C. G. and Williams, D., Diffusions, Markov Processes and Martingales, Vol. 1 & 2, Cambridge University Press 2000.
- Revuz, D. and Yor, M., Continuous Martingales and Brownian Motion, Springer 1999.
- Karatzas, I. and Shreve, S. E., Brownian Motion and Stochastic Calculus, Springer 1991.
- Durrett, R., Stochastic Calculus, CRC Press LCL 1996.
Feedback methods
Feedback tutorials will provide an opportunity for students' work to be discussed and provide feedback on their understanding. Coursework or in-class tests (where applicable) also provide an opportunity for students to receive feedback. Students can also get feedback on their understanding directly from the lecturer, for example during the lecturer's office hour.
Study hours
- Lectures - 33 hours
- Tutorials - 11 hours
- Independent study hours - 106 hours
Teaching staff
Goran Peskir - Unit coordinator