Electricity price forecasting accounting for renewable energies: optimal combined forecasts

Electricity price forecasting is an interesting problem for all the agents involved in electricity market operation. For instance, every profit maximisation strategy is based on the computation of accurate one-day-ahead forecasts, which is why electricity price forecasting has been a growing field of research in recent years. In addition, the increasing concern about environmental issues has led to a high penetration of renewable energies, particularly wind. In some European countries such as Spain, Germany and Denmark, renewable energy is having a deep impact on the local power markets. In this paper, we propose an optimal model from the perspective of forecasting accuracy, and it consists of a combination of several univariate and multivariate time series methods that account for the amount of energy produced with clean energies, particularly wind and hydro, which are the most relevant renewable energy sources in the Iberian Market. This market is used to illustrate the proposed methodology, as it is one of those markets in which wind power production is more relevant in terms of its percentage of the total demand, but of course our method can be applied to any other liberalised power market. As far as our contribution is concerned, first, the methodology proposed by García-Martos et al(2007 and 2012) is generalised twofold: we allow the incorporation of wind power production and hydro reservoirs, and we do not impose the restriction of using the same model for 24h. A computational experiment and a Design of Experiments (DOE) are performed for this purpose. Then, for those hours in which there are two or more models without statistically significant differences in terms of their forecasting accuracy, a combination of forecasts is proposed by weighting the best models(according to the DOE) and minimising the Mean Absolute Percentage Error (MAPE). The MAPE is the most popular accuracy metric for comparing electricity price forecasting models. We construct the combi nation of forecasts by solving several nonlinear optimisation problems that allow computation of the optimal weights for building the combination of forecasts. The results are obtained by a large computational experiment that entails calculating out-of-sample forecasts for every hour in every day in the period from January 2007 to Decem ber 2009. In addition, to reinforce the value of our methodology, we compare our results with those that appear in recent published works in the field. This comparison shows the superiority of our methodology in terms of forecasting accuracy.

Estimating and forecasting generalized fractional Long memory stochastic volatility models

In recent years fractionally differenced processes have received a great deal of attention due to its flexibility in financial applications with long memory. This paper considers a class of models generated by Gegenbauer polynomials, incorporating the long memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine the statistical properties of the new model, suggest using the spectral likelihood estimation for long memory processes, and investigate the finite sample properties via Monte Carlo experiments. We apply the model to three exchange rate return series. Overall, the results of the out-of-sample forecasts show the adequacy of the new GLMSV model.

Are there non-linearities in short-term interest rates?

The present paper investigates the characteristics of short-term interest rates in several countries. We examine the importance of nonlinearities in the mean reversion and volatility of short-term interest rates. We examine various models that allow the conditional mean (drift) and conditional variance (diffusion) to be functions of the current short rate.We find that different markets require different models. In particular, we find evidence of nonlinear mean reversion in some of the countries that we examine, linear mean reversion in others and no mean reversion in some countries. For all countries we examine, there is strong evidence of the need for the volatility of interest rate changes to be highly sensitive to the level of the short-term interest rate. Out-of-sample forecasting performance of one-factor short rate models is poor, stemming from the inability of the models to accommodate jumps and discontinuities in the time series data.

Using extreme value theory to measure value-at-risk for daily electricity spot prices

The recent deregulation in electricity markets worldwide has heightened the importance of risk management in energy markets. Assessing Value-at-Risk (VaR) in electricity markets is arguably more difficult than in traditional financial markets because the distinctive features of the former result in a highly unusual distribution of returns-electricity returns are highly volatile, display seasonalities in both their mean and volatility, exhibit leverage effects and clustering in volatility, and feature extreme levels of skewness and kurtosis. With electricity applications in mind, this paper proposes a model that accommodates autoregression and weekly seasonals in both the conditional mean and conditional volatility of returns, as well as leverage effects via an EGARCH specification. In addition, extreme value theory (EVT) is adopted to explicitly model the tails of the return distribution. Compared to a number of other parametric models and simple historical simulation based approaches, the proposed EVT-based model performs well in forecasting out-of-sample VaR. In addition, statistical tests show that the proposed model provides appropriate interval coverage in both unconditional and, more importantly, conditional contexts. Overall, the results are encouraging in suggesting that the proposed EVT-based model is a useful technique in forecasting VaR in electricity markets. (c) 2005 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.

Ein multisektoraler Sammelindikator für die Schweizer Konjunktur

Der multisektorale Sammelindikator für die Schweizer Gesamtkonjunktur weist gegenüber eine Reihe von methodischen Innovationen auf und berücksichtigt eine vergleichsweise grosse Anzahl von Indikatorreihen. Für den Stützbereich von 1991 bis 2002 erhalten wir auf Quartalsbasis einen stabilen Vorlauf von zwei Quartalen vor der Referenzreihe Vorjahreswachstumsrate des BIP, und auch die Niveaus der Wachstumsrate werden gut getroffen. Der neue Sammelindikator zeigt auch gute "out of sample" Prognoseeigenschaften, und zwar sowohl bezüglich des Vorlaufs als auch hinsichtlich der Niveaus der Referenzreihe.

Admissible monetary aggregates for the Euro area

We use the Fleissig and Whitney (2003) weak separability test to determine admissible levels of monetary aggregation for the Euro area. We find that the Euro area monetary assets in M2 and M3 are weakly separable and construct admissible Divisia monetary aggregates for these assets. We evaluate the Divisia aggregates as indicator variables, building on Nelson (2002), Reimers (2002), and Stracca (2004). Specifically, we show that real growth of the admissible Divisia aggregates enter the Euro area IS curve positively and significantly for the period from 1980 to 2005. Out of sample, we show that Divisia M2 and M3 appear to contain useful information for forecasting Euro area inflation.

Using monthly returns to model conditional heteroscedasticity

This empirical study examines the extent of non-linearity in a multivariate model of monthly financial series. To capture the conditional heteroscedasticity in the series, both the GARCH(1,1) and GARCH(1,1)-in-mean models are employed. The conditional errors are assumed to follow the normal and Student-t distributions. The non-linearity in the residuals of a standard OLS regression are also assessed. It is found that the OLS residuals as well as conditional errors of the GARCH models exhibit strong non-linearity. Under the Student density, the extent of non-linearity in the GARCH conditional errors was generally similar to those of the standard OLS. The GARCH-in-mean regression generated the worse out-of-sample forecasts.

The leverage effect in the UK stock market

This study seeks to explain the leverage in UK stock returns by reference to the return volatility, leverage and size characteristics of UK companies. A leverage effect is found that is stronger for smaller companies and has greater explanatory power over the returns of smaller companies. The properties of a theoretical model that predicts that companies with higher leverage ratios will experience greater leverage effects are explored. On examining leverage ratio data, it is found that there is a propensity for smaller companies to have higher leverage ratios. The transmission of volatility shocks between the companies is also examined and it is found that the volatility of larger firm returns is important in determining both the volatility and returns of smaller firms, but not the reverse. Moreover, it is found that where volatility spillovers are important, they improve out-of-sample volatility forecasts. © 2005 Taylor & Francis Group Ltd.

Trade balance and real exchange rate:new evidence from Mauritius-UK trade

Two main questions are addressed here: is there a long-run relationship between trade balance and real exchange rate for the bilateral trade between Mauritius and UK? Does a J-curve exist for this bilateral trade? Our findings suggest that the real exchange rate is cointegrated with the trade balance and we find evidence of a J-curve effect. We also find bidirectional causality between the trade balance and the real exchange rate in the long-run. The real exchange rate also causes the trade balance in the short-run. In an out-of-sample forecasting experiment, we also find that real exchange rate contains useful information that can explain future movements in the trade balance.

Admissible monetary aggregates for the euro area

We use the Fleissig and Whitney [Fleissig, A.R., Whitney, G.A., 2003. A new PC-based test for Varian's weak separability conditions. Journal of Business and Economics Statistics 21 (1), 133–144] weak separability test to determine admissible levels of monetary aggregation for the Euro area. We find that the Euro area monetary assets in M2 and M3 are weakly separable and construct admissible Divisia monetary aggregates for these assets. We show that real growth of the admissible Divisia aggregates enters the Euro area IS curve positively and significantly for the period from 1980 to 2005. Out of sample, we show that Divisia M2 and M3 appear to contain useful information for forecasting Euro area inflation.

Breaks in the UK household sector money demand function

We use non-parametric procedures to identify breaks in the underlying series of UK household sector money demand functions. Money demand functions are estimated using cointegration techniques and by employing both the Simple Sum and Divisia measures of money. P-star models are also estimated for out-of-sample inflation forecasting. Our findings suggest that the presence of breaks affects both the estimation of cointegrated money demand functions and the inflation forecasts. P-star forecast models based on Divisia measures appear more accurate at longer horizons and the majority of models with fundamentals perform better than a random walk model.

Forecasting the term structure when short-term rates are near zero

This paper compares the experience of forecasting the UK government bond yield curve before and after the dramatic lowering of short-term interest rates from October 2008. Out-of-sample forecasts for 1, 6 and 12 months are generated from each of a dynamic Nelson-Siegel model, autoregressive models for both yields and the principal components extracted from those yields, a slope regression and a random walk model. At short forecasting horizons, there is little difference in the performance of the models both prior to and after 2008. However, for medium- to longer-term horizons, the slope regression provided the best forecasts prior to 2008, while the recent experience of near-zero short interest rates coincides with a period of forecasting superiority for the autoregressive and dynamic Nelson-Siegel models. © 2014 John Wiley & Sons, Ltd.