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. ..

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

Hydro reservoir levels and power price dynamics: empirical insight on the nonlinear influence of fuel and emission cost on Nord Pool day-ahead electricity prices

This paper examines the dependency of the hourly day-ahead electricity price on fundamentals in the Nord Pool power market. We examine whether power prices depend differently on supply and demand variables when hydro reservoir levels are low than when they are high as we expect that the competitive environment changes as a consequence. When reservoir levels are high, all hydro power producers want to sell to prevent invaluable spillovers, which leads to competitive pressure. With lower reservoir levels, hydro power agents are more preserved about their actions. We examine the change in dynamics using a supply and demand model and split the sample in observations from periods with extreme low and high reservoir levels. We show that the parameters of the supply curve model significantly differ over the two samples. In addition, we show that the influence of the marginal costs on the price formation is significantly larger at lower reservoir levels. The insights of this paper improve the understanding of power price dynamics in relation with fundamentalacceptedVersion© International Research Center for Energy and Economic Development. This is the authors' accepted and refereed manuscript to the article. Published in The Journal of Energy and Development, http://www.jstor.org/stable/2481309

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

Predicting interest rate distributions using PCA & quantile regression

Principal component analysis (PCA) is well established as a powerful statistical technique in the realm of yield curve modeling. PCA based term structure models typically provide accurate fit to observed yields and explain most of the cross-sectional variation of yields. Although principal components are building blocks of modern term structure models, the approach has been less explored for the purpose of risk modelling—such as Value-at-Risk and Expected Shortfall. Interest rate risk models are generally challenging to specify and estimate, due to the regime switching behavior of yields and yield volatilities. In this paper, we contribute to the literature by combining estimates of conditional principal component volatilities in a quantile regression (QREG) framework to infer distributional yield estimates. The proposed PCA-QREG model offers predictions that are of high accuracy for most maturities while retaining simplicity in application and interpretability.publishedVersio

Term Premia in Norwegian Government Bond Yields

The typically observed upward sloping nominal yield curve implies that investors demand positive risk premia – or term premia – to hold long-term nominal bonds. Fundamentally, the term premium is compensation to investors for bearing interest rate risk and a component in the term structure of yields. There is substantial evidence of sizeable and time-varying term premia. As opposed to yields, term premia are not directly observable. In this paper we estimate term premia in Norwegian government bond yields from a set of dynamic term structure models (DTSM), covering the period from 2003/01 until 2021/04. In line with international studies, we find evidence of declining term premia over the sample period.acceptedVersio

Oil and Gas Risk Factor Sensitivities for US Energy Companies

Copyright (c) 2016 by the International Research Center for Energy and Economic Development (ICEED). All rights reserve

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.publishedVersion© 2012 The Authors. Published by David Publishing. This is an open access article under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/

Analysis and Forecasting of Electricity Price Risks with Quantile Factor Models

Forecasting quantile and value-at-risk levels for commodity prices is methodologically challenging because of the distinctive stochastic properties of the price density functions, volatility clustering and the importance of exogenous factors. Despite this, accurate risk measures have considerable value in trading and risk management with the topic being actively researched for better techniques. We approach the problem by using a multifactor, dynamic, quantile regression formulation, extended to include GARCH properties, and applied to both in-sample estimation and out-of-sample forecasting of traded electricity prices. This captures the specification effects of mean reversion, spikes, time varying volatility and demonstrates how the prices of gas, coal and carbon, forecasts of demand and reserve margin in addition to price volatility influence the electricity price quantiles. We show how the price coefficients for these factors vary substantially across the quantiles and offer a new, useful synthesis of GARCH effects within quantile regression. We also show that a linear quantile regression model outperforms skewed GARCH-t and CAViaR models, as specified on the shocks to conditional expectations, regarding the accuracy of out-of-sample forecasts of value-at-risk.submittedVersionThis is the pre-peer reviewed version of the following article: Analysis and Forecasting of Electricity Price Risks with Quantile Factor Models, which has been published in final form at http://www.iaee.org/en/publications/ejarticle.aspx?id=267

Value-at-risk in the European energy market: a comparison of parametric, historical simulation and quantile regression value-at-risk

This paper examines a set of value-at-risk (VaR) models and their ability to appropriately describe and capture price-change risk in the European energy market. We make in-sample, one-day-ahead VaR forecasts using one simple parametric model, one historical simulation model and one quantile regression (QR) model. We apply our models to nine different energy futures: Brent crude oil, API2 coal, UK natural gas, and three German and Nordic power futures in the period 2007–17. The models are tested at both long and short positions. Our research suggests that the QR model is easy to implement and offers accurate VaR forecasts in the European energy market. Copyright Infopro Digital Limited. All rights reserved. You may share this content using our article tools. Copying this content is for the sole use of the Authorised User (named subscriber), as outlined in our terms and conditions - https://www.infopro-insight.com/terms-conditions/insight-subscriptions/ If you would like to purchase additional rights please email [email protected] until 9.12.2020 due to copyright restrictions. Published by Incisive Media