Catálogo de publicaciones - libros

Many events such as conferences or informational events at institutions consist of a number of single workshops among which participants can choose a limited number to visit. The workshop assignment problem is a linear optimization problem that finds a feasible assignment of participants to workshops maximizing the social welfare function expressed by preferences of participants for workshops. Several problem constraints reflect the structure and limitations of the event and define possible workshop combinations.

Due to the increasing competition in liberalized electricity markets, a succesful customer retention as well as a cost efficient allocation of electric energy become more and more important. Therefore, new, innovative strategies are sought, which promise on the one hand a long-term customer retention and assure, on the other hand, a more cost-efficient provision of electric energy.

In the process of the generation and trading planning, generation companies maximize the contribution margin, i.e. the difference of the revenues of energy trades and the costs for generating and purchasing electrical energy. The planning time horizon of this process covers a period of one month to one year. The complex planning task necessitates the application of computer-based tools [1]. Due to structural changes within the power industry, these tools have to be adapted continuously to the new conditions.

After years of rapid economic development, China is calling for development of a circular economy and resource recycling, to solve both the resource shortage problem and the environmental problems. Based on the analysis of the current status and the challenges of the electronic waste (e-waste) recycling in China, this paper seeks to design a financially feasible and environmentally safe e-waste recycling system. An integrated assessment model is proposed, in which the environmental, social and economic impact of recycling scenarios can be assessed simultaneously. The economic impact can be derived from an optimization model for the reverse logistics network.

The challenge in modelling electricity prices is mainly caused by it’s non-storability. Spot prices are thus determined by the current demand/supply interaction, but hardly by expectations about the future. They show characteristics as mean-reversion, seasonal patterns, an immense volatility and spikes, which cannot be captured with standard stock market models. On contrary, there exists growing markets, where financial futures contracts are traded. These contracts are storable and show similar characteristics to other financial assets. In particular they feature a significant lower volatility then spot prices. Moreover, the volatility is decreasing in the time to maturity.

Due to substantial changes in competition, capital market conditions, and supervisory frameworks, holistic analysis of an insurance company’s assets and liabilities takes on special relevance. An important tool in this context is dynamic financial analysis (DFA). DFA is a systematic approach to financial modeling in which financial figures are projected under a variety of possible scenarios by showing how outcomes are affected by changing internal and/or external factors. The discussion in Europe about new risk-based capital standards (Solvency II project) and the development of International Financial Reporting Standards (IFRS), as well as expanding catastrophe claims, have made DFA an useful tool for cash flow projection and decision making, especially in the non-life and reinsurance businesses (for an overview, see [2]).

Reinsurance contracts represent a very important tool for insurance companies to manage their risk portfolio. In general, they are used if an insurer is not willing or not able to hold certain risk exposures or parts thereof on its own. There exist two main contract types to cede claims to a reinsurer, namely proportional and non-proportional ones. With the quota share reinsurance , a well-known variant of the former ones, a fixed percentage of the claim sizes is ceded to the reinsurance company. Excess of loss and stop loss are non-proportional types and the reinsurer is only liable to pay if certain losses are exceeded. In practice insurance companies usually place a number of different reinsurance contracts, a so-called reinsurance program .

One of the main differences in measuring capital requirements for credit risk due to the Internal Ratings Based Approach (IRBA) of the new regulatory Capital Standards (Basel II) is the enlarged acceptance of collaterals in order to reduce (supervisory) credit risk. Whereas in the Advanced IRBA own models for estimating the effects of collaterals can be used, in the Foundation IRBA concrete formulas are given. However, these regulations only explain the case of a single claim collateralized by a single asset or guarantee. If numerous collaterals are available for multiple loans, we receive an allocation problem of collaterals to loans with the objective of keeping regulatory capital low. In this article we model the corresponding optimization problem. Since numerical standard procedures often converge towards the wrong local minimum, we show how to apply evolutionary algorithms as well as simulated annealing.

While over the past few years both banking supervisors and researchers have focussed their attention on banks’ credit risk, the spotlight is now being turned again on interest rate risk. One reason for this is its character as a kind of systemic risk: there is evidence that a rise in interest rates affects most banks negatively. An historical example of a banking crisis caused by high interest rates is the ‘Savings and Loan Crisis’ which occurred in the USA during the 1980s.3 Between 1980 and 1988, 563 of the approximately 4,000 savings and loan institutions failed, while further failures were prevented by 333 supervisory mergers. The total costs of the crisis are estimated at USD160 billion. Recently, the Basel Committee on Banking Supervision published principles for the management and supervision of interest rate risk that go far beyond current practice.^4 However, few data are available concerning banks’ interest rate risk exposure.

For a long time, interest rates have been considered one of the macroeconomic factors determining stock returns. The role of interest rates in the return generating process of stocks has therefore been extensively investigated in general, but particularly so with regard to financial institutions, which are often deemed to be more sensitive to changes in interest rates than stocks from other industries. Generally, this specific sensitivity has been attributed to i.) the predominant role of financial (i.e. nominal) assets and liabilities on the balance sheets of financial intermediaries and ii.) the maturity transformation performed especially by depository institutions and the resulting maturity mismatch of assets and liabilities (see [14] for an extensive review).