Expectation formation and regime switches

Economic agents often have to make decisions in environments affected by regime switches but expectation formation has hardly been explored in this context. We report about a laboratory experiment whose participants judgmentally forecast three time series subject to regime switches. The participants make forecasts without context knowledge and without support from statistical software. Their forecasts are only based on the previous realizations of the time series. Our interest is the explanation of the average forecasts with a simple model, the bounds & likelihood heuristic. In previous studies it was shown that this model can explain average forecasting behavior very well given stable and stationary time series. We find that the forecasts after a structural break are characterized by a higher variance and less accuracy over several periods. Considering this transition phase in the model, the heuristic performs even slightly better than the Rational Expectations Hypothesis.     

Private equity and regulatory capital

Regulatory capital requirements for European banks have been put forward in the Basel II Capital Framework and subsequently in the capital requirements directive (CRD) of the EU. We provide a detailed discussion of the capital requirements for private equity investments under different approaches. For the internal model approach we present a structural model that we calibrate to a proprietary dataset. We modify the standard Merton structural model to make it applicable in practice and to capture stylized facts of private equity investments. We also implement the early default feature with a fast simulation algorithm. Our results support capital requirements lower than in Basel II, but not as low as in CRD, thereby giving adverse incentives to banks for using advanced risk models. A sensitivity analysis shows that this finding is robust to parameter uncertainty and stress scenarios.     

Multidimensionales Verfahren zur Bestimmung des Regelleistungsbedarfes unter Berücksichtigung von Unsicherheiten

One of the most crucial constraints in operation of electrical power supply systems is the permanent balance between generation and load. Reserve power is held ready to be able to keep this balance also in case of the occurrence of unpredictable events like power plant outages or inevitable deviations of power injections from their predicted values. At this, the allocation of operating reserves is in the field of conflict between level of reliability and cost effectiveness. The amount of reserve generation capacity required in a control area heavily depends on prediction quality which constitutes the need for high quality predictions. This article is about the influence of prediction quality on the amount of reserve generation capacity required in a control area. The algorithms for the assessment of required reserve generation capacity known from literature use the variation calculus technique to account for the aforementioned unpredictable events and prediction errors. In this article, an extension to a convolution-based method is presented in which the uncertainties of the predictions are expressed using intervals. Each parameter is represented by an independent dimension, assuming statistical independence among them. This allows the distinct analysis of each parameter’s influence on the amount of required reserve generation capacity. In addition, two methods for the visualization of the multi-dimensional results are presented, allowing a comprehensive analysis of the parameters’ influences. Standard distribution functions are used to represent the parameters for the simulation and the results are shown as probability density functions of possible imbalances. Based on forecasts of the development of conventional power plants and regenerative generation in the years 2020, 2030 and 2040, the required control power of the future German transmission system is calculated. Influences and dependencies are identified with the extended convolution-based method.     

CO2 Speicherung in Deutschland: Eine Brückentechnologie als Klimalösung?

The technology for CO₂ capture, transport and storage (CCTS) in the power plant and industrial sector are considered as an important component in a portfolio of technologies for CO₂ reduction. However, delayed or canceled demonstration projects show that the cost of the capture unit, the necessary pipeline infrastructure and the scarce storage potential in geological formations restrict the CCTS potential. In addition, growing public resistance could lead a further reduction of the CO₂ abatement potential. In this paper the contribution of the CCTS technology for CO₂ reduction in the energy and industrial sectors in Germany is calculated using the model CCTSMOD. It turns out that the application of CCTS is economically interesting for selected industrial sectors under CO₂ certificate prices of 50 €/t and for the power sector under certificate prices of 75 €/t. Because of the limited storage potential, lack of alternative mitigation options and low cost of the deposition, a predominant use of technology in the steel and cement sector is recommended.     

Interaction effects between dynamic hybrid products and traditional deferred annuities in the German life insurance market

Financial Markets and Portfolio Management - Dynamic hybrid products emerged in 2007 and are now well established in the German life insurance market. In this article, we study interaction effects...