Sergej Obžigailov
MSc in Econometrics, University of Amsterdam
Articles by this Author
Modelling dependence of extreme events in energy markets using tail copulas
- By Sergej Obžigailov
- Published 12/13/2011
- Risk management
- Unrated
Co-Author 1: Stefan Jäschke (RWE Supply & Trading GmbH)
Co-Author 2: Karl Friedrich Siburg (Fakultät für Mathematik TU Dortmund)
Co-Author 3: Pavel A. Stoimenov (Fakultät Statistik TU Dortmund)
Abstract
This paper studies the dependence of extreme events in energy markets. Based on a large data set comprising quotes of crude oil and natural gas futures, large co-movements of commodity returns are estimated and modeled. To detect the presence of tail dependence a new method based on the concept of tail copulas which accounts for different scenarios of joint extreme outcomes is applied. Moreover, it is shown that the common practice to fit copulas to the data cannot capture the dynamics in the tail of the joint distribution and, therefore, is unsuitable for risk management purposes.
Co-Author 2: Karl Friedrich Siburg (Fakultät für Mathematik TU Dortmund)
Co-Author 3: Pavel A. Stoimenov (Fakultät Statistik TU Dortmund)
Abstract
This paper studies the dependence of extreme events in energy markets. Based on a large data set comprising quotes of crude oil and natural gas futures, large co-movements of commodity returns are estimated and modeled. To detect the presence of tail dependence a new method based on the concept of tail copulas which accounts for different scenarios of joint extreme outcomes is applied. Moreover, it is shown that the common practice to fit copulas to the data cannot capture the dynamics in the tail of the joint distribution and, therefore, is unsuitable for risk management purposes.
Valuation of Commodity-Based Swing Options
- By Sergej Obžigailov
- Published 12/13/2011
- Trading strategies
- Unrated
Co-author 1: Patrick Jaillet.
Co-author 2: Ehud I. Ronn.
Co-author 3: Stathis Tompaidis.
Abstract
In the energy markets, in particular the electricity and natural gas markets, many contracts incorporate exibility-of-delivery options, known as swing or take-or-pay options. Subject to daily as well as periodic constraints, these contracts permit the option holder to repeatedly exercise the right to receive greater or smaller amounts of energy.
We extract market information from forward prices and volatilities and build a pricing framework for swing options based on a one-factor mean-reverting stochastic process for energy prices which explicitly incorporates seasonal effects. We present a numerical scheme for the valuation of swing options calibrated for the case of natural gas.
Keywords
Swing option, take-or-pay option, mean-reverting stochastic process, seasonal effects in energy prices, natural gas
Link
http://web.mit.edu/jaillet/www/general/swing-last.pdf
Co-author 2: Ehud I. Ronn.
Co-author 3: Stathis Tompaidis.
Abstract
In the energy markets, in particular the electricity and natural gas markets, many contracts incorporate exibility-of-delivery options, known as swing or take-or-pay options. Subject to daily as well as periodic constraints, these contracts permit the option holder to repeatedly exercise the right to receive greater or smaller amounts of energy.
We extract market information from forward prices and volatilities and build a pricing framework for swing options based on a one-factor mean-reverting stochastic process for energy prices which explicitly incorporates seasonal effects. We present a numerical scheme for the valuation of swing options calibrated for the case of natural gas.
Keywords
Swing option, take-or-pay option, mean-reverting stochastic process, seasonal effects in energy prices, natural gas
Link
http://web.mit.edu/jaillet/www/general/swing-last.pdf
Modeling and Forecasting Demand for Natural Gas of Retail Consumers
- By Sergej Obžigailov
- Published 01/31/2012
- Forecasting
- Unrated
A thesis submitted for the degree of MSc of Econometrics. University of Amsterdam, August 2011. sergej.o [at] googlemail.com
Abstract
The thesis deals with fitting, modeling and forecasting of the retail consumer demand for natural gas. This is important and has a potential for numerous applications: gas portfolio risk management, keeping the storage system in balance, using it for valuation of swing contracts, gas consumption planning and other. Retail gas demand is weather sensitive and therefore can be better explained, compared to the demand from industries or power plants. A relationship between demand for the natural gas, weather and prices is of key focus. Such a relationship is studied by different tests and models for the demand of the natural gas, spot prices and weather are proposed. Econometric approaches are used to prove that spot or forward gas prices do not effect gas demand in a short-term horizon. Two models for residential natural gas demand are proposed. These are used to forecast demand for natural gas of residential consumers. Two weather models are considered and used to simulate gas demand. A comparison and discussion is provided. Data are obtained from three energy companies in the Netherlands and Belgium.
Abstract
The thesis deals with fitting, modeling and forecasting of the retail consumer demand for natural gas. This is important and has a potential for numerous applications: gas portfolio risk management, keeping the storage system in balance, using it for valuation of swing contracts, gas consumption planning and other. Retail gas demand is weather sensitive and therefore can be better explained, compared to the demand from industries or power plants. A relationship between demand for the natural gas, weather and prices is of key focus. Such a relationship is studied by different tests and models for the demand of the natural gas, spot prices and weather are proposed. Econometric approaches are used to prove that spot or forward gas prices do not effect gas demand in a short-term horizon. Two models for residential natural gas demand are proposed. These are used to forecast demand for natural gas of residential consumers. Two weather models are considered and used to simulate gas demand. A comparison and discussion is provided. Data are obtained from three energy companies in the Netherlands and Belgium.
