Efficiency versus Robustness of Markets - Why improving market efficiency should not be the only objective of market regulation

The efficiency of capital markets has been questioned almost as long as the efficient market hypothesis had been worked out. Numerous critics have been formulated against this hypothesis, questioning notably the behavioural assumptions underlying the efficient market hypothesis. The present contribution does not focus on the behavioural assumptions but rather looks at the implications of focusing purely on the objective of market efficiency when considering market design questions. Hence it aims at discussing the following, possibly rather fundamental issue: Is the objective of efficiency, which has guided most of the market reforms in the last decades, sufficient? Or has it to be complemented by the objective of robustness? Mathematical and engineering control theory has developed the concept of robust control (e.g. Zhou and Doyle, 1998) and it has been shown that there is always a trade-off between the efficiency of a control system and its robustness (cf. e.g. Safonov, 1981, Doyle et al., 1988). The efficiency of the system describes its reactions to disturbance signals. The lower the integral loss function over the so-called transfer or sensitivity function, the less a system is affected by disturbances such as demand fluctuations, and the more efficient is the control. The economic equivalent clearly is the maximisation of welfare, which results in an efficient economic system. Robustness by contrast is defined as stability of the control system in the presence of model uncertainty (deviations in the model parameters or misperceptions of the underlying system). These concepts are applied to the financial markets in their interaction with the real economy. The financial markets being understood as the controllers of real world activity through investments, the implications of misperceptions in the financial sphere are analysed both theoretically and in an application example. From the theory it may readily derived that financial markets providing efficient, i.e. welfare-optimal solutions, must have limitations with respect to robustness. Also in the application example it turns out that in the presence of potential misperception a reduction of irreversible cost shares in investments may lead to an increase in overall expected system costs. Hence improvements in (conventional) market efficiency may be counter-productive by facilitating misallocation of capital as a consequence of misperceptions in the financial markets. This leads to the conclusion that a sole focus on the efficiency objective in market design is problematic and some of the recent turmoil in financial markets may be explained by the lack of consideration given to robustness issues.market efficiency, robustness, optimal control, stochastic dynamic growth

Binary Mixtures of Particles with Different Diffusivities Demix

The influence of size differences, shape, mass and persistent motion on phase separation in binary mixtures has been intensively studied. Here we focus on the exclusive role of diffusivity differences in binary mixtures of equal-sized particles. We find an effective attraction between the less diffusive particles, which are essentially caged in the surrounding species with the higher diffusion constant. This effect leads to phase separation for systems above a critical size: A single close-packed cluster made up of the less diffusive species emerges. Experiments for testing of our predictions are outlined.Comment: 5 figures in main text, 8 figures in Supplemental Materia

OPTIMAL ENVIRONMENTAL POLICY DESIGN IN THE PRESENCE OF UNCERTAINTY AND TECHNOLOGY SPILLOVERS

The stylized model presented in this paper extends the approach developed by Fischer and Newell (2008) by analysing the optimal policy design in a context with more than one externality while taking explicitly into account uncertainty surrounding future emission damage costs. In the presence of massive uncertainties and technology spillovers, well-designed sup-port mechanisms for renewables are found to play a major role, notably as a means for compensating for technology spillovers, yet also for reducing the investors’ risks. How-ever, the design of these support mechanisms needs to be target-aimed and well-focused. Besides uncertainty on the state of the world concerning actual marginal emission damage, we consider the technological progress through R&D as well as learning-by-doing. A portfolio of three policy instruments is then needed to cope with the existing externalities and optimal instrument choice is shown to be dependent on risk aversion of society as a whole as well as of entrepreneurs. To illustrate the role of uncertainty for the practical choice of policy instruments, an em-pirical application is considered. The application is calibrated to recent global data from IEA and thus allows identifying the main drivers for the optimal policy mix. In addition to assumptions on technology costs and uncertainty of emission damage cost, the impor-tance of technology spillover clearly plays a key role. Yet under some plausible parame-ter settings, direct subsidies to production are found to be of lower importance than very substantial R&D supports.Externality, technology, learning, uncertainty, climate change, spillover, renewable energy, policy

Valuing fuel diversification in optimal investment policies for electricity generation portfolios

Optimal capacity allocation for investments in electricity generation assets can be deterministically derived by comparing technology specific long-term and short-term marginal costs. In an uncertain market environment, Mean-Variance Portfolio (MVP) theory provides a consistent framework to valuate financial risks in power generation portfolios that allows to derive the efficient fuel mix of a system portfolio with different generation technologies from a welfare maximization perspective. Because existing literature on MVP applications in electricity generation markets uses predominantly numerical methods to characterize portfolio risks, this article presents a novel analytical approach combining conceptual elements of peak-load pricing and MVP theory to derive optimal portfolios consisting of an arbitrary number of plant technologies given uncertain fuel prices. For this purpose, we provide a static optimization model which allows to fully capture fuel price risks in a mean variance portfolio framework. The analytically derived optimality conditions contribute to a much better understanding of the optimal investment policy and its risk characteristics compared to existing numerical methods. Furthermore, we demonstrate an application of the proposed framework and results to the German electricity market which has not yet been treated in MVP literature on electricity markets.power plant investments, peak load pricing, mean-variance portfolio theory, fuel mix diversification

The Cost of Equity of Network Operators - Empirical Evidence and Regulatory Practice

In many European countries, the deregulation of energy markets leading to the introduction of unbundling and incentive regulation for utilities firms has made the task of setting an adequate cost of equity more difficult. Firstly, Legal Unbundling led to the creation of many legally independent network operators that have to be regulated separately, excluding the generation or sales activities of mother firms. Identifying adequate costs of capital is thereby complicated by the fact that only very few network operators are traded on stock exchanges. Secondly, the increased pressure through incentive regulation schemes has reinforced the importance of setting the equity return adequately. The approaches chosen by regulatory agencies have often been accompanied by heavy criticism regarding methodology and empirical data sets used. In this context the question arises, how regulators set equity returns for network operators and whether the methodologies applied are in line with state-of-the-art capital market models. This paper therefore starts by providing an overview on empirical results, reviewing major published studies of betas and equity returns regarding utilities and network operators. This research helps to identify and discuss the most important drivers of capital costs which is an indispensable groundwork for determining adequate betas. Additionally, an overview of the current practice of regulatory equity return setting is provided. These results are then compared to an empirical analysis based on a recent data set with more than 20 network operators. Based on this data set the required equity returns according to different methodologies (CAPM, Fama-French-TFM, Ross-APT) are computed. This provides evidence that regulatory practice in Europe and Australia ignores the Fama-French-TFM or the APT, even though notably the Fama-French TFM shows the potential to provide improved estimates of required equity returns. The paper concludes by providing a suggestion on how to put the FF TFM into practice accounting for the size of non-stock listed network operators.Network operator, cost of capital, asset pricing models, regulation, cost of equity

Decentralized energy supply and electricity market structures

Small decentralized power generation units (DG) are politically promoted because of their potential to reduce GHG-emissions and the existing dependency on fossil fuels. A long term goal of this promotion should be the creation of a level playing field for DG and conventional power generation. Due to the impact of DG on the electricity grid infrastructure, future regulation should consider the costs and benefits of the integration of decentralized energy generation units. Without an adequate consideration, the overall costs of the electricity generation system will be unnecessarily high. The present paper analyses, based on detailed modelling of decentralized demand and supply as well as of the overall system, the marginal costs or savings resulting from decentralized production. Thereby particular focus is laid on taking adequately into account the stochasticity both of energy demand and energy supply. An efficient grid pricing system should then remunerate long-term grid cost savings to operators of decentralized energy production or/and charge long-term additional grid costs to these operators. With detailed models of decentralized demand and supply as well as the overall system, the marginal costs or savings resulting from decentralized production are determined and their dependency on characteristics of the grid and of the decentralized supply are discussed.electricity markets, decentralized power production, demand side management

PORTFOLIO OPTIMIZATION IN ELECTRICITY TRADING WITH LIMITED LIQUIDITY

In principle, portfolio optimization in electricity markets can make use of the standard mean-variance model going back to Markowitz. Yet a key restriction in most electricity markets is the limited liquidity. Therefore the standard model has to be adapted to cope with limited liquidity. An application of this model shows that the optimal hedging strategy for generation portfolios is strongly dependent on the size of the portfolio considered as well as on the variance-covariancematrix used and the liquidity function assumed.optimization; electricity, liquidity; electricity trading; mean-variance-model

The Impact of Institutions on the Decision How to Decide

The human mind is not a general problem solving machine. Instead of deliberately, consciously and serially processing the available information, men can rely on routines, rules, roles or affect for the purpose. They can bring in technology, experts or groups. For all of these reasons, men have a plurality of problem solving modes at their disposition. Often, the meta-choice of problem solving mode matters for behavioural output. Some performance standards are only to be met if a certain problem solving mode is used, like a well-established skill. Other requirements are easier to fulfil with some problem solving modes. This explains why institutions frequently impact on the choice of problem solving mode. To show how institutions are able to do that, a model of problem solving modes is developed. It allows to systematise the access points for institutional intervention.Decision Making, Problem Solving, Institutions

Understanding Collective Dynamics of Soft Active Colloids by Binary Scattering

Collective motion in actively propelled particle systems is triggered on the very local scale by nucleation of coherently moving units consisting of just a handful of particles. These units grow and merge over time, ending up in a long-range ordered, coherently-moving state. So far, there exists no bottom-up understanding of how the microscopic dynamics and interactions between the constituents are related to the system's ordering instability. In this paper, we study a class of models for propelled colloids allowing an explicit treatment of the microscopic details of the collision process. Specifically, the model equations are Newtonian equations of motion with separate force terms for particles' driving, dissipation and interaction forces. Focusing on dilute particle systems, we analyze the binary scattering behavior for these models, and determine-based on the microscopic dynamics-the corresponding collision-rule, i.e., the mapping of pre-collisional velocities and impact parameter on post-collisional velocities. By studying binary scattering we also find that the considered models for active colloids share the same principle for parallel alignment: the first incoming particle (with respect to the center of collision) is aligned to the second particle as a result of the encounter. This behavior is distinctively different to alignment in non-driven dissipative gases. Moreover, the obtained collision rule lends itself as a starting point to apply kinetic theory for propelled particle systems in order to determine the phase boundary to a long-range ordered, coherently-moving state. The microscopic origin of the collision rule offers the opportunity to quantitatively scrutinize the predictions of kinetic theory for propelled particle systems through direct comparison with multi-particle simulations.Comment: 19 pages, 12 figure