Market Designs for a Completely Renewable Power Sector

The article discusses whether the current German electricity market design is suitable for an electricity system completely based on renewable sources, and analyzes alternatives. Such a system becomes ever more likely due to the phase-out of nuclear power and the carbon reduction targets. Various existing scenarios for a completely renewable electricity system are analyzed and compared with respect to the contribution of different renewable technologies. Challenges for the market design arising from the differences between the current and a completely renewable system are identified??notably problems with cost recovery and investment incentives, an increased need for balancing and/or intraday adjustments, an increased diversity of actors, grid congestion and the continuing occurrence of market power. The current market design??s ability to solve these issues is assessed with the result that all but the critical problem of investment incentives and cost recovery can be solved by adapting certain rules. A?comparison with other suggested market designs reveals that some designs could ensure cost recovery and investment incentives. However, these market designs have other drawbacks. Therefore, the identification of the optimal market design for a completely renewable electricity system requires further research regarding the qualitative and quantitative effects of different changes to the current market design. The article concludes by developing concrete policy recommendations.     

Decision Usefulness of Cash and Accrual Information: Public Sector Managers’ Perceptions

This study examines the usefulness of accrual accounting information for internal decision‐making contexts in the Western Australian public sector. Based on questionnaire responses of public sector managers, it was found that accrual accounting is perceived to be more useful than cash accounting in 16 of the 19 decision situations. These results suggest that the perceived usefulness of the accrual accounting system has improved with the passage of time. It may well be that perceptions of the usefulness of information derived from an accounting system will change over time as users gain familiarity and experience with a system.     

Risk Management,Corporate Governance,and the Search for Long-Term Investors

This discussion explores a number of ways that more effective risk management, corporate governance, and communication with investors can help companies increase their effciency and long-run value. According to one of the panelists, recent surveys of corporate directors suggest that companies should devote more time and attention to three issues—strategy, risk management, and succession planning—and that strategy and risk are the “flipsides of the same coin.” As the panelist argues, “You can't talk about strategy without talking about what risks you're going to take—and what risks you decide to take has to depend on the core competencies that drive the corporate strategy.” In addition to making risk management a critical part of corporate strategy, another notable recommendation is to communicate a company's strategy and business plan as clearly as possible to investors, with the aim of attracting more sophisticated, long-term shareholders. Contrary to popular belief, such a group may well include some hedge funds and other activist shareholders. According to a newly released report on shareholder activism (produced and cited by another panelist), corporate boards should work harder to identify and engage the “largest 10 shareholders in the organization,” with the ultimate goal of cultivating a shareholder base that buys into the company's strategy.     

Einzelbesprechungen

Ohne Zusammenfassung     

Optimal asset allocation for a large number of investment opportunities

This paper introduces a stock‐picking algorithm that can be used to perform an optimal asset allocation for a large number of investment opportunities. The allocation scheme is based upon the idea of causal risk. Instead of referring to the volatility of the assets time series, the stock‐picking algorithm determines the risk exposure of the portfolio by concerning the non‐forecastability of the assets. The underlying expected return forecasts are based on time‐delay recurrent error correction neural networks, which utilize the last model error as an auxiliary input to evaluate their own misspecification. We demonstrate the profitability of our stock‐picking approach by constructing portfolios from 68 different assets of the German stock market. It turns out that our approach is superior to a preset benchmark portfolio. Copyright © 2005 John Wiley & Sons, Ltd.