Modelling Seasonalities of HPFCs Using a Parametric Approach
(2019) FMSM01 20191Mathematical Statistics
- Abstract
- Electricity differs from other commodities in that it cannot be stored. This non-storability characteristic results in traditional pricing methods for commodities not being applicable for electricity. An alternative pricing method is therefore needed and the solution is the Hourly Price Forward Curve (HPFC). The HPFC essentially gives the prices as of today for the delivery of electricity at each hour in the future. It is generally constructed in two steps. The first step involves estimating the shape vector, which is a vector of hourly weights reflecting all seasonalities and forward-looking information in the electricity spot price. The second step calibrates the shape vector to futures products in order to make it arbitrage free.
In... (More) - Electricity differs from other commodities in that it cannot be stored. This non-storability characteristic results in traditional pricing methods for commodities not being applicable for electricity. An alternative pricing method is therefore needed and the solution is the Hourly Price Forward Curve (HPFC). The HPFC essentially gives the prices as of today for the delivery of electricity at each hour in the future. It is generally constructed in two steps. The first step involves estimating the shape vector, which is a vector of hourly weights reflecting all seasonalities and forward-looking information in the electricity spot price. The second step calibrates the shape vector to futures products in order to make it arbitrage free.
In this thesis, we have exclusively studied the first step of the HPFC construction. Specifically, we have modelled the month-to-hour ratios of the shape vector, i.e. a vector of hourly weights normalized for every month. This paper aims to explore the possibilities of modelling the month-to-hour ratios using parametric models with external inputs. This is done by implementing regression models using polynomial and Fourier basis functions, which is there after further developed with the addition of a Kalmanfilter and regularization techniques. The study covers the Nord Pool electricity market and it is conducted using data from E.ON and Nord Pool. It is concluded that parametric models with external inputs are well-suited for constructing a shape vector. It is successful in modelling the intra-yearly, intra-weekly and intra-daily seasonalities and shows robustness against extremities. However, difficulties arose in adequately modelling the summer months and the morning levels. (Less) - Popular Abstract
- The Nordic electricity market is currently changing with an expansion of renewables and an increased exchange with surrounding countries. This together with more established price drivers such as weather and business activity, creates distinct seasonal patterns of electricity prices. These price movements can from time to time be very volatile and therefore many companies feel the need to hedge their future purchases. Due to electricity being non-storable, the only way of hedging is via financial derivatives. However, the contracts on the market are traded with certain specific delivery periods, which creates a demand for non-standard products. Furthermore these customized contracts cannot be priced with conventional valuation methods,... (More)
- The Nordic electricity market is currently changing with an expansion of renewables and an increased exchange with surrounding countries. This together with more established price drivers such as weather and business activity, creates distinct seasonal patterns of electricity prices. These price movements can from time to time be very volatile and therefore many companies feel the need to hedge their future purchases. Due to electricity being non-storable, the only way of hedging is via financial derivatives. However, the contracts on the market are traded with certain specific delivery periods, which creates a demand for non-standard products. Furthermore these customized contracts cannot be priced with conventional valuation methods, once again due to the non-storability of electricity. The model solving this problem is the Hourly Price Forward Curve. This forward curve breaks down the prices of market-traded contracts into an hourly granularity, making it possible to valuate customized contracts for non-standard time periods. The construction of the curve can be divided into two steps. Firstly, a shape vector is contructed describing the seasonal pattern at an hourly granularity and secondly, the constructed shape vector is calibrated against the market prices of standard electricity futures contracts. This thesis has focused on the first step of the curve construction, modelling the shape vector. More specifically, each month was broken down into hourly ratios reflecting each hour’s weight against the others. This was done by applying a statistical approach using regression models to forecast the shape vector. Moreover, in order to take into account the effects of known spot price drivers, the possibility of including external fundamental data was investigated. These were the hydrological reservoir balance, total system consumption, net exchange with other markets, installed wind capacity and lastly, temperature deviation.
This data is somewhat easy to forecast and therefore interesting to include in an HPFC model to more accurately estimate the seasonal patterns. It was found that this approach was well-suited for modelling the seasonal patterns of an HPFC, and among the predictors, consumption was found the most influential one. Nevertheless, further investigative work needs to be done, where a logical first step would be to combine this approach with a price calibration to construct a complete forward curve. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8986622
- author
- Rastegar, Reza and Svantesson, Lucas
- supervisor
- organization
- course
- FMSM01 20191
- year
- 2019
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Power Markets, Hourly Price Forward Curves, Seasonality, Electricity Spot Price
- language
- English
- id
- 8986622
- date added to LUP
- 2019-06-20 12:51:50
- date last changed
- 2020-03-11 10:07:04
@misc{8986622,
abstract = {{Electricity differs from other commodities in that it cannot be stored. This non-storability characteristic results in traditional pricing methods for commodities not being applicable for electricity. An alternative pricing method is therefore needed and the solution is the Hourly Price Forward Curve (HPFC). The HPFC essentially gives the prices as of today for the delivery of electricity at each hour in the future. It is generally constructed in two steps. The first step involves estimating the shape vector, which is a vector of hourly weights reflecting all seasonalities and forward-looking information in the electricity spot price. The second step calibrates the shape vector to futures products in order to make it arbitrage free.
In this thesis, we have exclusively studied the first step of the HPFC construction. Specifically, we have modelled the month-to-hour ratios of the shape vector, i.e. a vector of hourly weights normalized for every month. This paper aims to explore the possibilities of modelling the month-to-hour ratios using parametric models with external inputs. This is done by implementing regression models using polynomial and Fourier basis functions, which is there after further developed with the addition of a Kalmanfilter and regularization techniques. The study covers the Nord Pool electricity market and it is conducted using data from E.ON and Nord Pool. It is concluded that parametric models with external inputs are well-suited for constructing a shape vector. It is successful in modelling the intra-yearly, intra-weekly and intra-daily seasonalities and shows robustness against extremities. However, difficulties arose in adequately modelling the summer months and the morning levels.}},
author = {{Rastegar, Reza and Svantesson, Lucas}},
language = {{eng}},
note = {{Student Paper}},
title = {{Modelling Seasonalities of HPFCs Using a Parametric Approach}},
year = {{2019}},
}