Determinants of power spreads in electricity futures markets: A multinational analysis. ESRI WP580, December 2017

The growth in variable renewable energy (vRES) and the need for flexibility in power systems go hand in hand. We study how vRES and other factors, namely the price of substitute fuels, power price volatility, structural breaks, and seasonality impact the hedgeable power spreads (profit margins) of the main dispatchable flexibility providers in the current power systems - gas and coal power plants. We particularly focus on power spreads that are hedgeable in futures markets in three European electricity markets (Germany, UK, Nordic) over the time period 2009-2016. We find that market participants who use power spreads need to pay attention to the fundamental supply and demand changes in the underlying markets (electricity, CO2, and coal/gas). Specifically, we show that the total vRES capacity installed during 2009-2016 is associated with a drop of 3-22% in hedgeable profit margins of coal and especially gas power generators. While this shows that the expansion of vRES has a significant negative effect on the hedgeable profitability of dispatchable, flexible power generators, it also suggests that the overall decline in power spreads is further driven by the price dynamics in the CO2 and fuel markets during the sample period. We also find significant persistence (and asymmetric effects) in the power spreads volatility using a univariate TGARCH model

Boosting the Anatomy of Volatility

Risk and, thus, the volatility of financial asset prices plays a major role in financial decision making and financial regulation. Therefore, understanding and predicting the volatility of financial instruments, asset classes or financial markets in general is of utmost importance for individual and institutional investors as well as for central bankers and financial regulators. In this paper we investigate new strategies for understanding and predicting financial risk. Specifically, we use componentwise, gradient boosting techniques to identify factors that drive financial-market risk and to assess the specific nature with which these factors affect future volatility. Componentwise boosting is a sequential learning method, which has the advantages that it can handle a large number of predictors and that it-in contrast to other machine-learning techniques-preserves interpretation. Adopting an EGARCH framework and employing a wide range of potential risk drivers, we derive monthly volatility predictions for stock, bond, commodity, and foreign exchange markets. Comparisons with alternative benchmark models show that boosting techniques improve out-of-sample volatility forecasts, especially for medium- and long-run horizons. Another finding is that a number of risk drivers affect volatility in a nonlinear fashion

Arbitrage and Walrasian equilibrium economies with limited information

Equilibrium Theory;economic theory

Impact analysis of the CAP reform on main agricultural commodities

End of project reportThis study has been carried out for the European Commission's Joint Research Centre to analyze agricultural policies at Member State, EU15 and EU25 levels as well as for Bulgaria and Romania. The modelling tool allows for projections and policy analysis (up to a 10 year horizon) for the enlarged EU.This study has been carried out for the European Commission's Joint Research Centre, under Contract no 150267-2005-FIED-N

Regional Priority Setting in Queensland: A multi-criteria evaluation framework.

Consultancy report for the Queensland Department of Natural Resources and Mines.audit;Australia;Queensland;natural resources; mines

The Evaluation of Multiple Year Gas Sales Agreement with Regime Switching

A typical gas sales agreement (GSA), also called a gas swing contract, is an agreement between a supplier and a purchaser for the delivery of variable daily quantities of gas, between specified minimum and maximum daily limits, over a certain number of years at a specified set of contract prices. The main constraint of such an agreement that makes them difficult to value is that in each gas year there is a minimum volume of gas (termed take-or-pay or minimum bill) for which the buyer will be charged at the end of the year (or penalty date), regardless of the actual quantity of gas taken. We propose a framework for pricing such swing contracts for an underlying gas forward price curve that follows a regime-switching process in order to better capture the volatility behaviour in such markets. With the help of a recombining pentanomial tree, we are able to efficiently evaluate the prices of the swing contracts, find optimal daily decisions and optimal yearly use of both the make-up bank and the carry forward bank at different regimes. We also show how the change of regime will affect the decisions