Search result: Catalogue data in Autumn Semester 2016
|Computational Science and Engineering Master|
|Fields of Specialization|
|401-3913-01L||Mathematical Foundations for Finance||W||4 credits||3V + 2U||E. W. Farkas, M. Schweizer|
|Abstract||First introduction to main modelling ideas and mathematical tools from mathematical finance|
|Objective||This course gives a first introduction to the main modelling ideas and mathematical tools from mathematical finance. It aims at a double audience: mathematicians who want to learn the modelling ideas and concepts for finance, and non-mathematicians who need an introduction to the main tools from stochastics used in mathematical finance. The main emphasis will be on ideas, but important results will be given with (sometimes partial) proofs.|
|Content||Topics to be covered include|
- financial market models in finite discrete time
- absence of arbitrage and martingale measures
- valuation and hedging in complete markets
- basics about Brownian motion
- stochastic integration
- stochastic calculus: Itô's formula, Girsanov transformation, Itô's representation theorem
- Black-Scholes formula
|Lecture notes||Lecture notes will be sold at the beginning of the course.|
|Literature||Lecture notes will be sold at the beginning of the course. Additional (background) references are given there.|
|Prerequisites / Notice||Prerequisites: Results and facts from probability theory as in the book "Probability Essentials" by J. Jacod and P. Protter will be used freely. Especially participants without a direct mathematics background are strongly advised to familiarise themselves with those tools before (or very quickly during) the course. (A possible alternative to the above English textbook are the (German) lecture notes for the standard course "Wahrscheinlichkeitstheorie".)|
For those who are not sure about their background, we suggest to look at the exercises in Chapters 8, 9, 22-25, 28 of the Jacod/Protter book. If these pose problems, you will have a hard time during the course. So be prepared.
|401-4657-00L||Numerical Analysis of Stochastic Ordinary Differential Equations |
Alternative course title: "Computational Methods for Quantitative Finance: Monte Carlo and Sampling Methods"
|W||6 credits||3V + 1U||A. Jentzen|
|Abstract||Course on numerical approximations of stochastic ordinary differential equations driven by Wiener processes. These equations have several applications, for example in financial option valuation. This course also contains an introduction to random number generation and Monte Carlo methods for random variables.|
|Objective||The aim of this course is to enable the students to carry out simulations and their mathematical convergence analysis for stochastic models originating from applications such as mathematical finance. For this the course teaches a decent knowledge of the different numerical methods, their underlying ideas, convergence properties and implementation issues.|
|Content||Generation of random numbers|
Monte Carlo methods for the numerical integration of random variables
Stochastic processes and Brownian motion
Stochastic ordinary differential equations (SODEs)
Numerical approximations of SODEs
Multilevel Monte Carlo methods for SODEs
Applications to computational finance: Option valuation
|Lecture notes||Lecture Notes are available in the lecture homepage (please follow the link in the Learning materials section).|
|Literature||P. Glassermann: |
Monte Carlo Methods in Financial Engineering.
Springer-Verlag, New York, 2004.
P. E. Kloeden and E. Platen:
Numerical Solution of Stochastic Differential Equations.
Springer-Verlag, Berlin, 1992.
|Prerequisites / Notice||Prerequisites:|
Mandatory: Probability and measure theory,
basic numerical analysis and
basics of MATLAB programming.
a) mandatory courses:
Probability Theory I.
b) recommended courses:
Start of lectures: Wednesday, September 21, 2016
For more details, please follow the link in the Learning materials section.
|401-8905-00L||Financial Engineering (University of Zurich)|
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH.
UZH Module Code: MFOEC103
Mind the enrolment deadlines at UZH:
|W||4.5 credits||3G||University lecturers|
|Abstract||This lecture is intended for students who would like to learn more on equity derivatives modelling and pricing.|
|Objective||Quantitative models for European option pricing (including stochastic|
volatility and jump models), volatility and variance derivatives,
American and exotic options.
|Content||After introducing fundamental|
concepts of mathematical finance including no-arbitrage, portfolio
replication and risk-neutral measure, we will present the main models
that can be used for pricing and hedging European options e.g. Black-
Scholes model, stochastic and jump-diffusion models, and highlight their
assumptions and limitations. We will cover several types of derivatives
such as European and American options, Barrier options and Variance-
Swaps. Basic knowledge in probability theory and stochastic calculus is
required. Besides attending class, we strongly encourage students to
stay informed on financial matters, especially by reading daily
financial newspapers such as the Financial Times or the Wall Street
|Prerequisites / Notice||Basic knowledge of probability theory and stochastic calculus.|
|401-5820-00L||Seminar in Computational Finance for CSE||W||4 credits||2S||J. Teichmann|
|Content||We aim to comprehend recent and exciting research on the nature of|
stochastic volatility: an extensive econometric research  lead to new in-
sights on stochastic volatility, in particular that very rough fractional pro-
cesses of Hurst index about 0.1 actually provide very attractive models. Also
from the point of view of pricing  and microfoundations  these models
are very convincing.
More precisely each student is expected to work on one specified task
consisting of a theoretical part and an implementation with financial data,
whose results should be presented in a 45 minutes presentation.
|Literature|| C. Bayer, P. Friz, and J. Gatheral. Pricing under rough volatility.|
Quantitative Finance , 16(6):887-904, 2016.
 F. M. Euch, Omar El and M. Rosenbaum. The microstructural founda-
tions of leverage effect and rough volatility. arXiv:1609.05177 , 2016.
 O. E. Euch and M. Rosenbaum. The characteristic function of rough
Heston models. arXiv:1609.02108 , 2016.
 J. Gatheral, T. Jaisson, and M. Rosenbaum. Volatility is rough.
arXiv:1410.3394 , 2014.
|Prerequisites / Notice||Requirements: sound understanding of stochastic concepts and of con-|
cepts of mathematical Finance, ability to implement econometric or simula-
tion routines in MATLAB.
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