STAT3170 Stochastic Methods of Energy Markets (5 cr)
Cooperation network course
Network: Vaasa Higher Education Consortium
Network: Cross-institutional studies in advanced courses in mathematics and statistics
This course is offered through the Network for Advanced Studies in Mathematics. These studies are available for the following degree students:
- Bachelor's Degree Programme in Mathematics
- Master's Degree Programme in Mathematics
- Bachelor's Degree Programme in Mathematics (Subject Teacher)
- Master's Degree Programme in Mathematics (Subject Teacher)
- Bachelor's Degree Programme in Mathematics, Chemistry or Physics Subject Teacher Education and Primary Teacher Education (Specialication in Mathematics)
- Master's Degree Programme in Mathematics, Chemistry or Physics Subject Teacher Education and Primary Teacher Education (Specialication in Mathematics)
- Doctoral Programme in Mathematics and Statistics
- Doctoral Programme in Mathematics and Science (Specialication in Mathematics)
Description
5.1
1. Introduction to Energy Markets.
1.1 Stylized facts in energy markets,
1.2 Basic contracts (day-ahead prices, forward prices, swaps).
1.3 Derivative contracts for physical assets: calls/puts, Asians, spreads, power
plants, swings, storages, weather derivatives.
2. Forward Models: Brownian Motion
2.1. Brief recap on Ito calculus.
2.2. Black-Scholes-Merton and Black 76 formulas.
2.3. Brownian motion and geometric Brownian notion. Multi- factor forward
models.
3. Spot Models: Ornstein-Uhlenbeck Processes
5.1. Ornstein-Uhlenbeck processes, one-factor spot models.
5.2. Multi-factor spot models, regime-switching models.
4. Some Notes on Lévy and Jump-diffusion Processes
4.1. Jump-diffusion processes, e.g. compound Poisson, Kou model.
4.2. Pure jump processes, e.g. Gamma, IG, VG, and NIG.
5. Pricing Contracts in practice
5.1. Monte Carlo methods in Python.
5.2. Pricing spread options.
5.3. Pricing weather derivatives
4. Pricing Storages
Learning outcomes
The liberalization of energy markets in many regions has led to new electricity
and gas markets with increasing trading volumes. Producers have started trad-
ing energy, and particularly after the 2007/2008 financial crisis, they are now
subject to regulations similar to those in the banking industry. Consequently,
the modeling of physical assets, like hydro power plants or gas-fired plants, is
addressed using real option approaches from financial mathematics.
Energy and commodity markets exhibit unique characteristics and are based
on different fundamentals than pure financial markets. Energy and commodity
prices often depend on physical constraints and are seen as volatile and erratic.
These aspects require robust and complex mathematical modeling by risk man-
agement units and trading desks.
This course provides students with a solid foundation in stochastic methods
for energy markets, equipping them with both theoretical and computational
skills suited for careers as quantitative analysts, risk managers, or traders in
commodity trading houses, energy companies, banks, insurance firms, and con-
sulting companies.
Description of prerequisites
(Recommended)
• Probability
• Stochastic Analysis
• Basic Programming in Python