One of the important goals of the EU is to ensure a secure, sustainable, and competitive energy system that is less dependent on external energy supply. Greater independence is planned to be achieved by diversifying energy sources, as well as investing in renewables and energy efficiency. One of the mechanisms is the demand response (DR) that provides a high level of energy independence for the consumer. In this paper, we explore perspectives of the development of DR with a mediating effect of the independent aggregators from an EU member state standpoint. We use a hybrid research methodology that combines instruments of strategic analysis, i.e., PESTLE framework and SWOT analysis, along with the integrated risk management framework in order to identify, evaluate and rank prominent risks to which this initiative is exposed. Interdependencies between the identified risk factors are also included and efficient mitigation measures are proposed. The findings of this exploratory research are aimed at developing the policies and strategies for the aggregators’ development in the medium term. The most emphasized risks detected in analysis are the investment risk, the legal risk, the risk of substitute technologies, the consumer behavior risk, the risk of opportunistic behavior and the risk of entry barriers.

This paper assesses the profitability of battery storage systems (BSS) by focusing on the internal rate of return (IRR) as a profitability measure which offers advantages over other frequently used measures, most notably the net present value (NPV). Furthermore, this study proposes a multi-objective optimisation (MOO) approach to IRR estimation instead of relying on the simple linear optimisation and compares the results to the popular linear optimisation with battery cycle-cost penalty. The analysis is conducted under perfect foresight conditions by considering multiple revenue streams: arbitrage trading in the day-ahead and intraday markets, peak shaving, participating in the primary reserves market, and from photovoltaic (PV) power-generation unit. Data are collected for the German power market for 2017 and 2021. The results show that MOO approach yields similar IRR estimates to the cycle-cost model in 2017. However, higher market volatility and increased electricity prices in 2021 resulted in tangible differences. The analysis shows that, if such conditions are coupled with a low battery capacity price, the MOO method significantly outperforms the cycle-cost model. The effects of battery calendar lifetime and state of charge which decrease profitability are also considered. Nevertheless, a noticeable rise in profitability in 2021 relative to 2017 could provide enough compensation to address the issue of relatively poor viability track record.

The paper provides, for the first time, the analysis of the quality of the GDP growth and inflation forecasts by multiple forecasters for the Croatian economy. Forecast data of 6 different institutions in the 2006–2015 period are analysed. Efficiency and biasedness test are conducted following the Davies and Lahiri econometric framework based on a three-dimensional panel dataset which includes multiple individual forecasters, target years and forecast horizons. In order to assess directional accuracy we follow the approach by Pesaran and Timmermann. Based on MAE values we find the forecasts to be accurate on a scale comparable to the European Commission’s forecast reported in 2016 for the EU and the euro area. GDP growth forecasts exhibit a strong bias related to a notable tendency to over-predict GDP growth. In the case of inflation forecasting the bias is still present for all forecasters, albeit less pronounced and not statistically significant for all of them. There is evidence of forecast inefficiency regarding both analysed variables. Overall, inflation forecasting presents less of a challenge due to specific monetary policy strategy and inaccurate national accounts data accompanied by extended revision process of the GDP data by the government’s statistics office.

U nastojanjima da se najpoznatiji jedno-faktorski model, model procjenjivanja kapitalne imovine (CAPM), primijeni u praksi često su se koristili dionički indeksi temeljeni na tržišnoj kapitalizaciji. Istraživanja su, međutim,...

In order to apply the CAPM in practice, the estimation of the market portfolio presents one of the biggest challenges. Even more so after the research in early 1990s pointed out the mean-variance inefficiency of stock market capitalization-weighted indices. In the paper we perform an in-sample optimisation for the CROBEX index constituents to test its efficiency ex post. Selected CROBEX index revisions from the period March 2005 - September 2017 are analysed. We find the index to be inefficient which confirms the results of earlier studies. However, since mean-variance optimisation often yields extreme portfolio weights, thus reducing the effective number of stocks in the portfolio, the focus of this research is on testing if improvement in efficiency over the CROBEX index is possible, with respect to the portfolio deconcentration level. The Luenberger's shortage function is added to the optimisation algorithm in order to increase the number of efficient portfolios analysed.