Forecasting realized volatility in Nord Pool electricity market
Lonka, Pekko Pietari (2020-04-27)
Lonka, Pekko Pietari
27.04.2020
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2020042722653
https://urn.fi/URN:NBN:fi-fe2020042722653
Tiivistelmä
Electricity markets, like other commodity markets, offer many opportunities for their participants, but their high volatility and the unstorable nature of electricity makes them quite different. Due to the high volatility, volatility forecasting would be even more useful in the electricity markets, but as they produce high frequency data, the traditional models for forecasting volatility often perform poorly. Realized volatility has emerged as one possible solution for this problem.
This study tests the possibility to use the realized volatility of daily spot prices in the Nord Pool electricity market to form forecasts about the next day’s realized volatility and the behavior of the daily and hourly spot prices during the next day. The Nord Pool electricity market is chosen due to its uniqueness among the European electricity markets, and due to the lack of existing studies about this area. The Nord Pool markets also strongly experience the effects of seasonality and the weather, which could affect the effectiveness of volatility forecasting. The hourly electricity spot price data used in this study is collected directly from Nord Pool.
Realized volatility and the heterogeneous autoregressive model of realized volatility, the HAR-RV model, have proven to be successful forecasting tools, even when using high frequency data. The HAR-RV model has been shown to be far more effective and accurate in high-frequency markets than the previously traditional volatility forecasting models. Realized volatility by itself makes it easier to view intraday prices, and it can be used to view the historical data inside the high-frequency data.
To test the forecasting power of realized volatility in the Nord Pool electricity markets, the effectiveness of the HAR-RV model is tested with OLS regression. The results of this model are then compared to the actual data, which is inspected more closely to find out possible patterns in the behavior of realized volatility and the hourly electricity prices. The results show that the HAR-RV model is able to accurately forecast the next day’s realized volatility, and these results can then be used to form predictions about the behavior of the next day’s electricity prices. As the model can be used to forecast over longer time periods as well, this gives ground for further study to be made from this area.
This study tests the possibility to use the realized volatility of daily spot prices in the Nord Pool electricity market to form forecasts about the next day’s realized volatility and the behavior of the daily and hourly spot prices during the next day. The Nord Pool electricity market is chosen due to its uniqueness among the European electricity markets, and due to the lack of existing studies about this area. The Nord Pool markets also strongly experience the effects of seasonality and the weather, which could affect the effectiveness of volatility forecasting. The hourly electricity spot price data used in this study is collected directly from Nord Pool.
Realized volatility and the heterogeneous autoregressive model of realized volatility, the HAR-RV model, have proven to be successful forecasting tools, even when using high frequency data. The HAR-RV model has been shown to be far more effective and accurate in high-frequency markets than the previously traditional volatility forecasting models. Realized volatility by itself makes it easier to view intraday prices, and it can be used to view the historical data inside the high-frequency data.
To test the forecasting power of realized volatility in the Nord Pool electricity markets, the effectiveness of the HAR-RV model is tested with OLS regression. The results of this model are then compared to the actual data, which is inspected more closely to find out possible patterns in the behavior of realized volatility and the hourly electricity prices. The results show that the HAR-RV model is able to accurately forecast the next day’s realized volatility, and these results can then be used to form predictions about the behavior of the next day’s electricity prices. As the model can be used to forecast over longer time periods as well, this gives ground for further study to be made from this area.