Given the potentially severe financial consequences due to climate change, understanding how climate risks contribute to firms’ credit risk is essential. Building on a Merton-type model, we propose a new model that introduces a random growth adjustment factor in the firm value dynamics to reflect the depreciation due to climate risks. We also review the current state of the literature on how structural models of credit risk are employed to model the impact of climate risk on financial markets. Motivated by the theoretical models, we utilize the information contained in the spreads of Credit Default Swap (CDS) contracts to construct a market-implied, forward-looking carbon risk (CR) factor. We examine empirically how the scope and speed of economic transformation vary across jurisdictions, sectors, and over time. Explicit carbon emission pricing enables lenders to sharpen their assessments. The breadth of the regulation intensifies financial repercussions from carbon risk. The impact differs significantly across industries, indicating that the market identifies which sectors are better poised for a transition to a low-carbon economy. Lenders expect that adjustments in carbon regulations in Europe will cause relatively higher policy-related costs in the near future.

This paper presents a new method for combining (or aggregating or ensembling) multivariate probabilistic forecasts, considering dependencies between quantiles and marginals through a smoothing procedure that allows for online learning. We discuss two smoothing methods: dimensionality reduction using Basis matrices and penalized smoothing. The new online learning algorithm generalizes the standard CRPS learning framework into multivariate dimensions. It is based on Bernstein Online Aggregation (BOA) and yields optimal asymptotic learning properties. The procedure uses horizontal aggregation, i.e., aggregation across quantiles. We provide an in-depth discussion on possible extensions of the algorithm and several nested cases related to the existing literature on online forecast combination. We apply the proposed methodology to forecasting day-ahead electricity prices, which are 24-dimensional distributional forecasts. The proposed method yields significant improvements over uniform combination in terms of continuous ranked probability score (CRPS). We discuss the temporal evolution of the weights and hyperparameters and present the results of reduced versions of the preferred model. A fast C＋＋implementation of the proposed algorithm is provided in the open-source R-Package profoc on CRAN.

On the market where prospective renters meet dwelling offers, competitive forces and rational behavior on both sides would imply that the monthly basic rent should reflect differences in expected monthly heating costs – other things being equal. We test this hypothesis by specifying a hedonic price model reflecting a total-cost-of-renting perspective. Drawing on 844,229 apartment listings for rent from 2014 to 2020 on a small spatial scale, we find a premium for more energy-efficient apartments; however, it is rather small. If the energy performance score decreases by 10 kWh/m2a, the monthly basic rent increases, on average, by roughly €0.01 per square meter living area. The expected energy cost savings thereby exceed the premium by a factor of three to seven. Rather, we find discounts of up to 9.2 % if apartments use heating technologies that are known to be inefficient. We explore various explanations for these outcomes, considering both landlord and renter behavior.

Abstract

We propose a simple approach to synthesize presumably information-driven insider trading signals for the cross-section of stocks. We find that the resulting composite strategy can predict returns, predominantly in equal-weighted portfolios, in our global sample. The results indicate that the benefits of our composite strategy reflect a short-term informational advantage of insiders. Finally, cross-country analysis reveals that varying insider trading restrictions between countries have limited explanatory power for the benefits of the composite strategy.

Distribution grids are more and more penetrated by decentralized, flexible units. For this reason, aspects of active system operation become increasingly relevant there. The management of many active units on the lower voltage network levels is a challenge that requires improved data communication and multi-energy-system operation management. In this work, we propose a common data layer for different generic technology models that can be used in a standardized manner to depict energy consumption, generation, and conversion. The technology models allow to compute system operation points or dispatch schedules for different stakeholders related to distribution grids e.g., system operators.

Abstract

We evaluate the relative and absolute performance of competing factor-based asset pricing models in international regions and globally. Our holistic analysis controls for model transaction costs and incorporates both right-hand-side tests (based on maximum squared Sharpe ratios) and left-hand-side tests (individual return predictors, composite mispricing proxies). The overall view of the tests shows that recently proposed models tend to perform better than classical models, but otherwise perform comparably. This finding, the performance of the models in some of the LHS tests as well as further results collectively suggest the need for new powerful asset pricing models for global equity markets.

Abstract

Negative screening (of "sin" stocks) is the most common strategy used by socially responsible investors. There is no consensus in the literature whether these exclusions result in higher cost of capital (and hence higher expected returns) for targeted firms. The existing literature identifies sin companies using industry classification codes (IC). We propose an alternative measure of firms' exposure to sin activities (sinfulness) based on textual analysis (TA). Sinfulness captures both cross-sectional and time-series variation in firms' exposure to sin activities. The correlation between the IC and TA sin indicators is only 0.69, with twice as many sin stocks in TA than in IC. TA reveals several important false positive and numerous false negative sin stocks in IC. While the number of publicly listed sin-related stocks has declined by 43% between 1997 and 2021, their total market capitalization has increased almost threefold from about $200bn to $600bn during the same period. A sin-weighted portfolio of sin stocks earns an annualized Fama-French 6-factor alpha of 4%. Overall, our study highlights important shortcomings of using IC to identify sinful firms and resurrects the sin premium, that is, more sinful stocks have higher expected returns.

This survey article reviews the current state of literature on how structural models of credit risk are employed to model the impact of climate risk on financial markets. We discuss how the two prominent types of climate risk, physical and transition risk, are captured by the seminal Merton model and its well-known extensions. Theoretical and practical advantages and drawbacks are worked out and an outlook on possible model improvements is provided.

Market-based congestion management has been proposed as a more efficient means of coping with congestion issues in zonal market designs. However, strategic behaviour has been identified as a fundamental problem of market-based approaches. This paper focuses on strategic behaviour in the setting of a local flexibility market. In this type of market, trading occurs in parallel with the intraday market and flexibility is verified by reporting baselines through a verification platform. Under these conditions, market monitoring based on statistical tests is presented as a countermeasure. By implementing tests that are robust against autocorrelation and based on the illustrative example, it is shown that the identification of strategic behaviour is possible. In combination with appropriate regulatory sanctioning, strategic behaviour can become less attractive and, in the best case, be prevented leading to a reduction in congestion management costs. The simplified example presented in this paper can serve as a basis for more complex use cases where additional factors need to be considered.

Combination and aggregation techniques can significantly improve forecast accuracy. This also holds for probabilistic forecasting methods where predictive distributions are combined. There are several time-varying and adaptive weighting schemes such as Bayesian model averaging (BMA). However, the quality of different forecasts may vary not only over time but also within the distribution. For example, some distribution forecasts may be more accurate in the center of the distributions, while others are better at predicting the tails. Therefore, we introduce a new weighting method that considers the differences in performance over time and within the distribution. We discuss pointwise combination algorithms based on aggregation across quantiles that optimize with respect to the continuous ranked probability score (CRPS). After analyzing the theoretical properties of pointwise CRPS learning, we discuss B- and P-Spline-based estimation techniques for batch and online learning, based on quantile regression and prediction with expert advice. We prove that the proposed fully adaptive Bernstein online aggregation (BOA) method for pointwise CRPS online learning has optimal convergence properties. They are confirmed in simulations and a probabilistic forecasting study for European emission allowance (EUA) prices.

With energy generation becoming increasingly decentralized, the need for congestion management across grid voltage levels is also increasing. To enable fair sharing of congestion costs among grid operators, these costs must be allocated to congested grid elements. We propose using the Shapley value for this purpose. The Shapley value is a cooperative game theory concept that was developed to share a total surplus generated by a coalition of players between the players based on their marginal contributions to the coalition. We apply this concept to share the costs of congestion management between grid elements based on their contributions to overall congestion management costs. To reduce the computational complexity of the Shapley value, we introduce two novel simplification approaches and compare them to existing methods using a numerical example based on CIGRE benchmark grids. The first method exploits the fact that the characteristic function for the congestion costs is obtained from an optimal power flow computation (i.e., a constrained optimization problem). It utilizes knowledge about which constraints are non-binding in the optimization to derive the values of related coalitions without calculating them. The second method takes advantage of the fact that the congestion management cost-allocation game is monotone and derives the values of coalitions based on this property. Both methods are implemented and compared to sampling. Using the first method, we are able to reduce computational complexity to less than 20% of that of the original problem while maintaining exact results. Our second approach is not dependent on detailed knowledge of the underlying optimization problem and can reduce the computational time by almost half with exact results and much further when compromising precision. While the methods are presented through an application example, they can be applied to other games with similar properties.

The future transformation of the European electricity system will be strongly influenced by both an ongoing
integration of variable renewable energy sources (VREs) and an increased proliferation of electric vehicles (EVs).
This combination will cause considerable uncertainty, especially since EV diffusion may greatly vary regarding
both the spatio-temporal penetration pattern and the achievable flexibility level. Notably, power plant investment
in the long run and dispatch in the short run will be affected. Hence, this paper assesses the impact of EV
penetration on the integration of VREs and the costs of CO2 emission reduction as well as the necessary investments
in controllable plant capacities under the consideration of frequency reserve and backup capacity
requirements. Applying an extended European energy system model, we found that EVs with a high flexibility
level may contribute tremendously to improved VRE integration, alleviating the number of necessary VRE investments
to achieve emission-reduction goals. Simultaneously, overall system costs are reduced even though
necessary investments in controllable plants, ensuring the abovementioned system stability needs, significantly
increase. Policy makers should hence ensure sufficient incentives to both exploit the EVs’ potential and safeguard
corresponding investments in controllable plants, which need to remain attractive even though full-load hours
are decreasing.

The German government is aiming for a climate-neutral building stock before 2050 to meet the defined goals of the Climate Action Plan 2050. Increasing the building stock's energy efficiency is therefore a high priority, and investments by private homeowners will greatly influence this, as around 46.5% of German homes are owner-occupied. To identify the possible monetary benefits of investments in energy retrofits, we investigate whether energy efficiency is reflected in the property values of German single-family homes. Therefore, we examine potential heterogeneous effects across regions. With 422,242 individual observations on a 1 km2-grid level from 2014 to 2018, this study adds to the extant literature by 1) examining the energy efficiency effect on housing values for the entire country and specifically investigating regional disparities in this context, and 2) estimating an energy efficiency value-to-cost ratio to compare housing values' increase with initial investment costs and future energy cost savings. Applying hedonic analysis, we find a positive relationship between energy efficiency and asking prices. If energy efficiency increases by 100 kWh/m2a, prices increase by 6.9% on average. We also find evidence for regional disparities. The effects are significantly weaker in large cities than in other urban areas, whereas the impact in rural regions is much stronger. According to this, housing shortage and higher purchasing power per capita were identified as drivers for low energy efficiency premiums. Finally, there is evidence that about 98% of future energy cost savings are already reflected in a higher housing value under myopic expectations regarding future energy prices.
 

Abstract

Motivated by the mixed evidence on the performance of (downside) volatility-managed equity factor portfolios in the U.S., I study the performance of nine (downside) volatility-managed equity factors before and after considering transaction costs in a set of 45 international equity markets. My results suggest that volatility management is most promising for market, value, profitability, and momentum portfolios and that the performance can be enhanced by applying downside volatility instead of total volatility (variance) as a scaling factor. Nevertheless, a marginal trader would find it difficult to profit from these strategies as only the managed market and momentum strategies are partially robust to my transaction cost estimations. Collectively, my results suggest that the persistence of abnormal returns of (downside) volatility-managed equity factors can largely be explained by the associated transaction costs. Finally, my cross-country analysis suggests that the slow trading hypothesis is partially able to explain cross-country performance differences of volatility-managed value and momentum portfolios.

Abstract

We demonstrate in our experiment that an exogenous shock does not lead to increasing risk aversion, and has ultimately no significant impact on investors’ risk preference in general. To do so, we keep subjects’ risk and return expectations fixed and focus solely on loss in wealth. As a theoretical framework, we use the expected utility approach and take the class of HARA-utility functions to analyse subjects’ preferences. Particularly, our methodical approach affords insights into the impact of economic fluctuations on investors’ risk-taking and the measurement of risk preferences per se. We conclude that cautious investment behavior after an economic crisis might rather be due to changes in the perception of risk and return. Moreover, we give evidence that, in general, it is not sufficient to explain investors’ risk-taking solely by preferences.

Using Credit Default Swap spreads, we construct a forward-looking, market-implied carbon risk factor and show that carbon risk affects firms’ credit spread. The effect is larger for European than North American firms and varies substantially across industries, suggesting the market recognises where and which sectors are better positioned for a transition to a low-carbon economy. Moreover, lenders demand more credit protection for those borrowers perceived to be more exposed to carbon risk when market-wide concern about climate change risk is elevated. Finally, lenders expect that adjustments in carbon regulations in Europe will cause relatively larger policy-related costs in the near future.

We examine if the trading activity on the German intraday electricity market is linked to fundamental as well as market-induced factors. Thus, we propose a novel point process model in which the intensity process of order arrivals consists of a self-exciting term and additional exogenous factors, such as the production of renewable en- ergy or the activated volume on the balancing market. The model parameters are estimated by a maximum like- lihood approach that explicitly accounts for such factor processes. By comparing the proposed model to several nested models, we investigate whether adding the exogenous factors significantly increases the accuracy of the model fit. We find that intensity processes that only take into account exogenous factors are improved if we add a self-exciting term. On the other hand, to capture the market dynamics correctly, pure self-exciting models need to be extended such that they additionally account for exogenous impacts.

We use point processes to analyze market order arrivals on the intraday market for hourly electricity deliveries in Germany in the second quarter of 2015. As we distinguish between buys and sells, we work in a multivariate setting. We model the arrivals with a Hawkes process whose baseline intensity comprises either only an exponentially increasing component or a constant in addition to the exponentially increasing component, and whose excitation decays exponentially. Our goodness-of-fit tests indicate that the models where the intensity of each market order type is excited at least by events of the same type are the most promising ones. Based on the Akaike information criterion, the model without a constant in the baseline intensity and only self-excitation is selected in almost 50% of the cases on both market sides. The typical jump size of intensities in case of the arrival of a market order of the same type is quite large, yet rather short lived. Diurnal patterns in the parameters of the baseline intensity and the branching ratio of self-excitation are observable. Contemporaneous relationships between different parameters such as the jump size and decay rate of self and cross-excitation are found.

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9221982

Abstract

The business press is a key information intermediary in stock markets, but little is known about how journalists themselves process information. To test competing hypotheses, I combine composite mispricing scores constructed from about 200 cross-sectional anomalies with the content of about two million firm-specific newspaper articles. I find that journalists tend to write positively (negatively) about stocks likely to be undervalued (overvalued). The effect is strongest for national newspapers and overvalued stocks. These and further findings collectively lend more, though not unambiguous, support to the bright side of financial journalism. In most cases, journalists act as “watchdogs”, not as “cheerleaders”.

Will be published after the CIRED 2020 in Berlin.

currently under review

working paper can be found here

WeatherAggReOpt

Projektvorstellung

Clustering Investment Choice

Das Paper kann wie folgt zitiert werden: “This article can already be cited using the year of online availability and the DOI as follows: Author(s), Article Title, Journal (Year), DOI.”

Abstract

Motivated by McLean and Pontiff (2016), we study the pre- and post-publication return predictability of 241 cross-sectional anomalies in 39 stock markets. Based on more than two million anomaly country-months, we nd that the United States is the only country with a reliable post-publication decline in long/short returns. Collectively, our meta-analysis of return predictors suggests that barriers to arbitrage trading may create segmented markets and that anomalies tend to represent mispricing rather than data mining.

Existing research indicates that on the intraday market for power deliveries in Germany market orders tend to arrive in clusters. To capture such clustering, point processes with an intensity depending on past events, so-called Hawkes processes, appear to be promising. We consider the question whether there is a temporal structure prevalent in the parameters of Hawkes processes estimated for adjacent delivery hours. First we model a diurnal seasonality pattern found in the data and provide an economic intepretation for it. For the remaining decomposed series, we then propose simple (vector) autoregressive models to describe the serial structure. To evaluate our model we conduct a forecasting study. Testing against a benchmark model and a model without any serial structure, we find evidence for our proposed model. Our study reveals that capturing the serial structure in the parameters proves to be useful in understanding the underlying market microstructure.

Conference Paper:

16th International Conference on the European Energy Market (EEM), Ljubljana 2019

There is an ongoing debate on the introduction of capacity markets in most European countries while a few of them have already established capacity markets. Since the implementation of independent national capacity markets is not in line with the target of a pan-European internal electricity market we investigate the impacts of uncoordinated capacity markets compared with coordinated capacity markets. A probabilistic approach for the determination of capacity requirements is proposed and a European electricity market model (E2M2s) is applied for evaluation. The model simultaneously optimizes investments and dispatch of power plants. Besides the impact on generation investments, market prices and system costs we analyze effects on production and security of supply. While coordinated capacity markets reveal high potentials for cross-border synergies and cost savings, uncoordinated and unilateral implementations can lead to inefficiencies, in particular free riding effects and endanger security of supply due to adverse allocation of generation capacity.

Den Volltext finden Sie hier.

http://www.et-energie-online.de/AktuellesHeft/Inhalt.aspx

Weitere Details finden Sie hier.

Abstract

By quantifying the tone of firm-specific articles in leading national newspapers between 1989 and 2010, we propose a bottom-up measure of aggregate journalist disagreement. In line with theoretical considerations, our novel high-frequency proxy for differences of opinion negatively forecasts the market return, in particular during recessions. Moreover, it has predictive power for the cross-section of stock returns. Collectively, our insights support asset pricing theories incorporating belief dispersion and highlight the role of the media in this context.

We present a stochastic modelling approach to describe the dynamics of hourly electricity prices. The suggested methodology is a stepwise combination of several mathematical operations to adequately characterize the distribution of electricity spot prices. The basic idea is to analyze day-ahead prices as panel of 24 cross-sectional hours and to identify principal components of hourly prices to account for the cross correlation between hours. Moreover, non-normality of residuals is addressed by performing a normal quantile transformation and specifying appropriate stochastic processes for time series before fit. We highlight the importance of adequate distributional forecasts and present a framework to evaluate the distribution forecast accuracy. The application for German electricity prices 2015 reveal that: (i) An autoregressive specification of the stochastic component delivers the best distribution but not always the best point forecasting results. (ii) Only a complete evaluation of point, interval and density forecast, including formal statistical tests, can ensure a correct model choice.

Abstract

Relying on the Stambaugh, Yu, and Yuan (2015) mispricing score and on 45 countries between 1994 and 2013, I document economically meaningful and statistically significant cross-sectional stock return predictability around the globe. In contrast to the widely held belief, mispricing associated with the 11 long/short anomalies underlying the composite ranking measure appears to be at least as prevalent in developed markets as in emerging markets. Additional support for this conjecture is obtained, among others, from tests for biased expectations based on the behavior of anomaly spreads surrounding earnings announcements as well as from within-country variation in development.

Emissions Trading Systems (ETSs) with fixed caps lack provisions to address systematic imbalances in the supply and demand of permits due to changes in the state of the regulated economy. We propose a mechanism which adjusts the allocation of permits based on the current bank of permits. The mechanism spans the spectrum between a pure quantity instrument and a pure price instrument. We solve the firms' emissions control problem and obtain an explicit dependency between the key policy stringency parameter – the adjustment rate – and the firms' abatement and trading strategies. We present an analytical tool for selecting the optimal adjustment rate under both risk-neutrality and risk-aversion, which provides an analytical basis for the regulator's choice of a responsive ETS policy.

Abstract

Extensive research has revealed that alphabetical name ordering tends to provide an advantage to those positioned in the beginning of an alphabetical listing. This paper is the first to explore the implications of this alphabetic bias in financial markets. We find that U.S. stocks that appear near the top of an alphabetical listing have about 5% to 15% higher trading activity and liquidity than stocks that appear towards the bottom. The magnitude of these results is negatively related to firm visibility and investor sophistication. International evidence and fund flows further indicate that ordering effects can affect trading activity and liquidity.

Abstract

Are anomalies strongest when investor sentiment or limits of arbitrage are considered to be greatest? We empirically explore these theoretically deducted predictions. We first identify, categorize, and replicate 100 long-short anomalies in the cross-section of expected equity returns. We then comprehensively study their interaction with popular proxies for time-varying market-level sentiment and arbitrage conditions. We find a powerful (relatively weak) role of the variation in proxies for sentiment (arbitrage constraints). In this context, the predictive power of sentiment is mostly restricted to the short leg of strategy returns. Our insights collectively suggest that the dynamics of sentiment combined with the base level (and not primarily the variations) of limits to arbitrage provide at least a partial explanation for inefficiencies.

Mit dem weiteren Ausbau fluktuierender erneuerbarer Energien (EE) steigen in Zukunft die Anforderungen an die Aufrechterhaltung der Systembilanz. Aufgrund der Variabilität und Unvorhersehbarkeit der EE-Einspeisung wird sich der Regelleistungsbedarf in Zukunft erhöhen. Um dem entgegenzuwirken, sind adäquate Reservebemessungsverfahren von großer Bedeutung. Während in der Praxis in der Regel einfache deterministische oder probabilistische Modelle angewendet werden, entwickeln wir in diesem Beitrag ein verbessertes dynamisches Verfahren zur Reservebemessung unter Verwendung nichtparametrischer Verteilungen zur Beschreibung der Prognosefehler. Ein weiterer Mehrwert des vorgestellten Ansatzes besteht in der Berücksichtigung zeitabhängiger Wahrscheinlichkeitsverteilungen für Kraftwerksausfälle und einer anschließenden Faltung von konditionalen Wahrscheinlichkeitsverteilungen für Last-, Wind- und Solarprognosefehler. Das dynamische Verfahren wird mit einem statischen Ansatz zur Reservebemessung mit unkonditionalen Wahrscheinlichkeitsverteilungen verglichen. Basierend auf historischen Daten quantifizieren wir den Regelleistungsbedarf und analysieren die ökonomischen Auswirkungen dynamischer Reservebedarfe mit Hilfe einer Fallstudie für Deutschland und die Betrachtungsjahre 2013 und 2030.

Abstract

Relying on 2.2 million articles from 45 national and local U.S. newspapers between 1989 and 2010, we find that firms particularly covered by the media exhibit ceteris paribus significantly stronger momentum. The effect depends on article tone, reverses in the long-run, is more pronounced for stocks with high uncertainty, and stronger in states with high investor individualism. Our findings suggest that media coverage can exacerbate investor biases, leading return predictability to be strongest for firms in the spotlight of public attention. These results collectively lend credibility to an overreaction-based explanation for the momentum effect.

Abstract

This paper evaluates numerous diversification strategies as a possible remedy against widespread costly investment mistakes of individual investors. Our results reveal that a very broad range of simple heuristic allocation schemes offers similar diversification gains as well-established or recently developed portfolio optimization approaches. This holds true for both international diversification in the stock market and diversification over different asset classes. We thus suggest easy-to-implement allocation guidelines for individual investors.

With ever increasing shares of intermittent renewables also the need for flexible supply and demand resources as well as storages is increasing. In particular it is expected that decentralized storages and demand response can contribute to the integration of higher shares of renewables into the electricity system. This raises also a challenge related to market design which is often overlooked. The issue is how an existing small‐scale storage would optimally bid into a voluntary power exchange like those established in Europe.

It is shown that the optimal bidding strategy is based on a simple evaluation of the expected spread. If the expected spread implies a profitable operation of the storage, (almost) unconditional bids are submitted to the power exchange. Otherwise no bids are submitted. This bidding choice is shown to be suboptimal in a welfare perspective and the introduction of specific spread products is proposed to overcome this flaw in the current market design.

Paper available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2114177

Paper available at: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2114196

Economists concentrate usually on equilibria. For the case of the electricity market the long term equilibrium is given by the peak-load pricing model. In contrast control theory has focuses on system dynamics, that is how systems are affected by disturbances and shocks and how such situations may be controlled. Therefore rather deviations from system equilibria and ways to counteract those effects are analysed. Concerning uncertain impacts as input factor price changes and load pattern variations the dynamic perspective of control theory might provide a suitable tool for the analysis of electricity markets. Therefore in this paper the peak-load pricing model is translated to a control theoretic problem so that the impact of disturbances and the dynamics of investment activities can be examined.

Abstract

Exploiting several regional holidays in Germany as a source of exogenous cross-sectional variation in investor attention, we provide evidence that the well-known local bias at the individual level materially affects stock turnover at the firm level. The German setting offers favorable characteristics for this natural experiment. Stocks of firms located in holiday regions are temporarily strikingly less traded, both in statistical and economic terms, than otherwise very similar stocks in non-holiday regions. This negative turnover shock is robust and survives various tests for cross-sectional differences in information release. It is particularly pronounced in stocks less visible to non-local investors, and for smaller stocks disproportionately driven by retail investors. Our findings contribute to research on local bias, determinants of trading activity and limited attention.

Working Paper, available at ssrn.com/abstract=2065723

Im Rahmen dieses Papers wird das mögliche Design regionaler Märkte betrachtet. Ne-ben den erforderlichen Kommunikations- und Informationssystemen stellt dies einen wesentlichen Aspekt der Umsetzung dar. Neben einer Analyse der unterschiedlichen Handelsmechanismen und Auktionsverfahren bestehender Märkte werden insbesonde-re die spezifischen Anforderungen an einen regionalen Markt untersucht. Dabei spielen auch Fragestellungen wie die freiwillige oder mandatorischen Handelsteilnahme, das Engpassmanagement, technologische Charakteristika der Erzeugungsanlagen der Marktteilnehmer und die Ankopplung an übergeordnete Märkte eine Rolle. Aus diesen Überlegungen wird abgeleitet, dass insbesondere ein Modell mit mehreren Auktionen pro Tag, bei dem auch komplexe Gebote möglich sind, für einen regionalen Marktplatz mit vielfältigen unterschiedlichen Erzeugern und Nachfragern geeignet erscheint. Ent-scheidend ist jedoch auch die Frage der Kopplung mit den übergeordneten nationalen bzw. internationalen Märkten.

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. Well-designed support 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. However, 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 illus-trate the role of uncertainty for the practical choice of policy instruments, an empirical application is considered. Under some plausible parameter settings, direct subsidies to production are found to be of lower importance than very substantial R&D supports.

Strategic behavior by gas traders is likely to affect future gas prices. Within this article a computational game theoretic model is presented which allows assessing market power on the natural gas market and its influence on the electricity market. This model uses typical time segments to represent both seasonal load fluctuations on the natural gas market and hourly load fluctuations on the electricity market. An application is presented covering 40 regions and simultaneously optimizing dispatch and utilization of transmission lines on the power market as well as supply, transmission and storage on the natural gas market. The model is used to evaluate the influence of trader market power in the natural gas market on the electricity market. We compute price changes, sales volume and power plant utilization for three different market power specifications.

Paper available at:

www.sciencedirect.com/science/article/pii/S0095069610001051

Paper available at:

papers.ssrn.com/sol3/papers.cfm

As a consequence of liberalization, electric utilities have developed different asset management strategies with the aim of reducing total maintenance costs by prolonging replacement cycles. A cost-minimizing network operator has to optimize the trade-off between costs for early replacement and quality penalties he faces in the case of outages (due to a resulting higher number of failures). The basic purpose of this paper is to identify the optimal decisions for a network operator under output-based quality regulation and to determine the main drivers for his decisions. In order to derive general insights, an analytical framework is chosen for this paper. This work builds on the foundations of optimal maintenance and replacement strategies as developed across Operations Research, Production Economics and Engineering Production Theory. As main results we are able to analytically derive optimal replacement strategies for network components depending on the characteristics of their failure distributions linked to the equipment’s cost, respective replacement cost, and quality penalties.

Paper available at:

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1546105

Working Paper available at:

papers.ssrn.com/sol3/papers.cfm

Paper available at:

http://web.mit.edu/ceepr/www/publications/workingpapers/2009-018.pdf

Traditionell basiert das Engpassmanagement in Deutschland auf bilateralen Verträgen. Jedoch setzt eine Vergütung basierend auf bilateralen Verträgen nur begrenzt Anreize. So haben die Kraftwerksbetreiber keine Anreize, ihre Grenzkosten offenzulegen. Zudem ist die Teilnahme am Redispatch nicht verpflichtend, wodurch ein mögliches hohes Potential nicht genutzt wird. Aus diesen Gründen ist es wichtig, das Engpassmanagement in Deutschland neu zu gestaltet. In diesem Artikel werden daher verschiedene Redispatch-Vergütungsmodelle, wie sie in der Literatur diskutiert werden (Inderst, Wambach (2007), Hakvoort et al. (2009)), miteinander verglichen und hinsichtlich ihrer Anreize für Produzenten, Übertragungsnetzbetreiber und Konsumenten bewertet. Des Weiteren wird ein nachträglich zonales Modell als eine kurzfristige, effiziente Engpassmanagementmethode entwickelt. Der Fokus dieser Analyse liegt auf Deutschland als eines der größten Strommärkte in Europa.