Renewable energy and electricity prices: indirect empirical evidence from hydro power

Many countries have introduced policies to stimulate the production of electricity in a sustainable or renewable way. Theoretical and simulation studies provide evidence that the introduction of renewable energy promotion policies lead to lower electricity prices as sustainable energy supply as wind and solar have very low or even zero marginal costs. Empirical support for this result is relatively scarce. The motivation for this study is to provide additional empirical evidence on how the growth of low marginal costs renewable energy supply such as wind and solar influences power prices. We do so indirectly studying Nord Pool market prices where hydro power is the dominant supply source. We argue that the marginal costs of hydro production varies depending on reservoir levels that determine hydro production capacity. ..

Using Machine Learning to Profit on the Risk Premium of the Nordic Electricity Futures

This study investigates the use of several trading strategies, based on Machine Learning methods, to profit on the risk premium of the Nordic electricity base-load week futures. The information set is only composed by financial data from January 02, 2006 to November 15, 2017. The results point out that the Support Vector Machine is the best method, but, most importantly, they highlight that all individual models are valuable, in the sense that their combination provides a robust trading procedure, generating an average profit of at least 26% per year, after considering trading costs and liquidity constraints. The results are robust to the different data partitions, and there is no evidence that the profitability of the trading strategies has decreased in recent years. We claim that this market allows for profitable speculation, namely by using combinations of non-linear signal extraction techniques.JEL Codes - G13; G14; Q4

On the Estimation of Extreme Values for Risk Assessment and Management: The ACER Method

In this paper we use an Average Conditional Exceedance Rate (ACER) method to model the tail of the price change distribution of daily spot prices in the Nordic electricity market, Nord Pool Spot. We use an AR-GARCH model to remove any seasonality, serial correlation and heteroskedasticity from the data before modelling the residuals from this filtering process with the ACER method. We show that using the conditional ACER method for Value-at-Risk forecasts give significant improvement over a standard AR-GARCH model with normal or Student’s-t distributed errors. Compared to a conditional generalized Pareto distribution (GPD) fitted with the Peaks-over-Threshold (POT) method, the conditional ACER method produces slightly more accurate quantile forecasts for the highest quantiles.publishedVersio

Modeling superior predictors for crude oil prices

A common perception in the literature is that oil price dynamics are most adequately explained by fundamental supply-and-demand factors. We use a general-to-specific approach and find that financial indicators are even more significant at modeling and predicting oil prices. We demonstrate empirically that the futures spreads level, high-yield bond spreads and PHLX Oil Service Sector (OSX) index are the best predictors of oil prices in the period February 2000–June 2013. (The OSX index is designed to track the performance of a set of companies involved in the oil services sector.) The OSX index is particularly interesting, as no study has analyzed its predictive power prior to our analysis. The relationship is intuitively meaningful, as stock prices, which strongly depend on the oil price, are determined in a market with well-informed investors that have strong incentives to gather correct market information. Moreover, the share prices serve as strong proxies or price signals, as they reflect future oil price expectations at any point of time. Furthermore, we demonstrate through an out-of-sample analysis that our most parsimonious model is superior to relevant benchmarks at forecasting oil price changes (two benchmarks were used: (1) a random walk and (2) ARIMA.2; 0; 2/, which was optimized in-sample by minimizing the Akaike information criterion). Our findings do not necessarily imply that the financial sector determines oil prices. On the contrary, we take the view that fundamental information is traceable from financial markets, and, hence, financial predictors serve as indicators for oil price fundamentals.publishedVersio

Electricity futures prices: time varying sensitivity to fundamentals

This paper provides insight in the time-varying relation between electricity futures prices and fundamentals in the form of prices of contracts for fossil fuels. As supply curves are not constant and different producers have different marginal costs of production, we argue that the relation between electricity futures prices and futures prices of underlying fundamentals such as natural gas, coal and emission rights are not constant and vary over time. We test this view by applying a model that linearly relates electricity futures prices to the marginal costs of production and calculate the log-likelihood of different time-varying and constant specifications of the coefficients. To do so, we formulate the model in state-space form and apply the Kalman Filter to observe the dynamics of the coefficients. We analyse historical prices of futures contracts with different delivery periods (calendar year and seasons, peak and off-peak) from Germany and the U.K. The results indicate that analysts should choose a time-varying specification to relate the futures price of power to prices of underlying fundamentals

Modelling day-ahead Nord Pool forward-price volatility: realized rolatility versus GARCH models

The argument that better volatility estimates can be obtained by using standard time-series techniques on non-parametric volatility measures constructed from high- frequency intradaily returns has been prevalent over the past decade. This study uses high- frequency data and the concept of realized volatility to make one-day-ahead predictions of Nord Pool forward-price volatility. We compare the predictions obtained from realized volatility using standard time-series techniques with the more traditional GARCH framework. Additionally, we examine whether different approaches of decomposing the total variation, and whether inclusion of exogenous effects, improves the accuracy or not. The main findings suggest that significant improvements in the one-day-ahead Nord Pool forward-price volatility predictions can be obtained by applying high-frequency data and the concept of realized volatility.publishedVersio

Covariance estimation using high-frequency data : an analysis of Nord Pool electricity forward data

The modeling of volatility and correlation is important in order to calculate hedge ratios, value at risk estimates, CAPM betas, derivate pricing and risk management in general. Recent access to intra-daily high-frequency data for two of the most liquid contracts at the Nord Pool exchange has made it possible to apply new and promising methods for analyzing volatility and correlation. The concepts of realized volatility and realized correlation are applied, and this study statistically describes the distribution (both distributional properties and temporal dependencies) of electricity forward data from 2005 to 2009. The main findings show that the logarithmic realized volatility is approximately normally distributed, while realized correlation seems not to be. Further, realized volatility and realized correlation have a long-memory feature. There also seems to be a high correlation between realized correlation and volatilities and positive relations between trading volume and realized volatility and between trading volume and realized correlation. These results are to a large extent consistent with earlier studies of stylized facts of other financial and commodity markets.publishedVersio

Electricity Futures Prices

This paper provides insight in the time-varying relation between electricity futures prices and fundamentals in the form of prices of contracts for fossil fuels. As supply curves are not constant and different producers have different marginal costs of production, we argue that the relation between electricity futures prices and futures prices of underlying fundamentals such as natural gas, coal and emission rights are not constant and vary over time. We test this view by applying a model that linearly relates electricity futures prices to the marginal costs of production and calculate the log-likelihood of different time-varying and constant specifications of the coefficients. To do so, we formulate the model in state-space form and apply the Kalman Filter to observe the dynamics of the coefficients. We analyse historical prices of futures contracts with different delivery periods (calendar year and seasons, peak and off-peak) from Germany and the U.K. The results indicate that analysts should choose a time-varying specification to relate the futures price of power to prices of underlying fundamentals

Cytokines Explored in Saliva and Tears from Radiated Cancer Patients Correlate with Clinical Manifestations, Influencing Important Immunoregulatory Cellular Pathways

publishedVersio

The One Ocean Expedition: Science and Sailing for the Ocean We Want

The One Ocean Expedition (OOE) was a 20-month long circumnavigation of the globe by the Norwegian sail training vessel Statsraad Lehmkuhl, and a recognised part of the UN decade of Ocean Science for Sustainable Development. The ship was equipped with modern instrumentation to collect high-quality data on ocean physics, chemistry, and biology. Many of the data series were available in near real time from an open repository. The scientific programme was executed along the sailing route of Statsraad Lehmkuhl, with occasional stops for stationary work. The aim of the data collection on board the vessel was to improve knowledge about the state of the world's ocean with regards to the distribution and diversity of organisms, environmental status, climate, and human pressures on the marine ecosystem. Another aim of the expedition was to educate ocean scientists and strengthen ocean literacy. The main types of instrumentation are sensors that measure continuously underway including echosounder, hydrophone, temperature and salinity probes, and various instruments that collect and analyse water sampled from an inlet in the ship's hull, including for environmental DNA and microplastic. Here, we describe the scientific instrumentation onboard Statsraad Lehmkuhl and present preliminary results from the Atlantic part of the expedition. While there are many challenges to using a sail ship for scientific purposes, there are also some key benefits as the vessel is quiet and has a low footprint. Furthermore, the use of a common set of instruments and procedures across the ocean also removes an uncertainty factor when comparing data between ocean areas.The One Ocean Expedition: Science and Sailing for the Ocean We WantpublishedVersio