105.091 Stochastic analysis in financial and actuarial mathematics 2
This course is in all assigned curricula part of the STEOP.
This course is in at least 1 assigned curriculum part of the STEOP.

2020S, VO, 2.0h, 4.0EC
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Course evaluation


  • Semester hours: 2.0
  • Credits: 4.0
  • Type: VO Lecture

Learning outcomes

After successful completion of the course, students are able to ...

  • explain and apply the chain rule, integration by parts, and convergence theorems for stochastic integrals (w.r.t. continuous semimartingales),
  • formulate and use Ito's multidimensional formula, Tanaka's formula, lto's local formula and Ito's formula for holomophic functions, present selected applications,
  • introduce the stochastic exponential and the stochastic logarithm, explain basic properties and characterisations, 
  • use Lévy's characterization of Brownian motion, 
  • formulate Girsanov's theorem and apply it to adjust the drift of Brownian motion by a measure change, 
  • explain Doob's upcrossing inequality and derive Doob's convergence theorems for submartingales,
  • explain and apply the predictable integral representation theorem for Brownian local martingales, 
  • check and derive conclusions from Kazamaki's and Novikov's criterion,
  • describe and apply the ideas and methods used to prove tha main theorems of the course.

Subject of course

Chain rule and convergence theorems for stochastic integrals (with respect to continuous semimartingales), integration by parts, multi-dimensional Ito formula with applications, Tanaka's formula, local Ito formula and Ito formula for holomorphic functions, stochastic exponential of continuous semimartingales, stochastic logarithm, Lévy's characterization of standard Brownian motion, Girsanov's theorem, change of drift using Girsanov's theorem, Doob's upcrossing inequality, Doob's convergence theorems for submartingales, representation of Brownian local martingales, Kazamaki's and Novikov's criterion

Teaching methods

The basic contents and concepts are presented by the head of the LVA and illustrated and discussed with the help of examples.

Mode of examination




Course dates

Thu09:00 - 11:0005.03.2020 - 12.03.2020Sem.R. DA grün 06A .
Thu11:00 - 12:0005.03.2020 - 12.03.2020Sem.R. DA grün 06A .
Stochastic analysis in financial and actuarial mathematics 2 - Single appointments
Thu05.03.202009:00 - 11:00Sem.R. DA grün 06A .
Thu05.03.202011:00 - 12:00Sem.R. DA grün 06A .
Thu12.03.202009:00 - 11:00Sem.R. DA grün 06A .
Thu12.03.202011:00 - 12:00Sem.R. DA grün 06A .

Examination modalities

The performance is assessed by an oral examination at the end of the semester.

Course registration

Not necessary



Registered students (to part 1 of the course) have access to an English script in electronic format with numerous references. The script will be updated on a continuing basis.

Additional literature:
Olav Kallenberg: Foundations of Modern Probability. 2. Edition, Springer-Verlag, 2002, ISBN 0-387-953113-2.
Daniel Revuz and Marc Yor: Continuous Martingales and Brownian Motion, 3. Edition, Springer-Verlag, 1999, ISBN 3-540-64325-7.
Ioannis Karatzas und Steven E. Shreve: Brownian Motion and Stochastic Calculus. 2. Edition, Springer-Verlag, ISBN 0-38797-655-8.
Bernt Øksendal: Stochastic Differential Equations: An Introduction with Applications. 6. Edition, Springer-Verlag, 2007, ISBN 978-3-54004-758-2.

David Williams: Probability with Martingales. Cambridge University Press, 1991, ISBN 0-521-40605-6.
Heinz Bauer: Maß- und Integrationstheorie. 2. Edition, De Gruyter, 1992, ISBN 3-11013-626-0.
Heinz Bauer: Wahrscheinlichkeitstheorie. 5. Edition, De Gruyter, 2002, ISBN 3-11017-236-4.

Preceding courses

Accompanying courses

Continuative courses


if required in English