Contracts between electric utilities typically offer a substantial amount of flexibility in the form of complex embedded options. Demand for such optionalities arises naturally from the unpredictability of power consumption and from the optionalities inherent in power plants. In the past, there rarely was the necessity to precisely evaluate the value of these optional parts, because electricity was not a commodity which could easily be traded, and because supply of electric power was assured by utility companies under regulatory control. In fact, most counterparts did not use the flexibility of the delivery contracts in a market-orientated way. In recent years, these matters have changed dramatically. In many countries electric power markets have been liberalized and exchanges and online trading platforms for electricity contracts have been founded. Market participants now take advantage of the optionality in their contracts by optimizing against market prices and looking for arbitrage opportunities. Therefore, it has become an important task for utilities to develop new pricing models for the contracts they buy and sell and to quantify and manage the involved risks.
As an example, assume that an electric utility needs additional power at times of high demand for the first 6 months of a year. Since the utility does not know exactly when the load will be high (as it depends on uncertain factors, such as weather conditions), it signs an optional contract. One possibility is, that the utility simply buys a portfolio of call options giving the right, but not the obligation, to buy electricity each hour within the delivery period with a capacity of up to 100 MW at a fixed price of 30 EUR/MWh. This option can be viewed as a cap on an hourly power price. Another, less expensive, possibility is the purchase of a swing option. This is a contract with delivery of a certain amount of commodity on dates in the future at a stipulated constant price. The delivery dates can be nominated at short notice by the buyer within a given delivery period. In our example we assume that the utility buys a swing option, which gives the right and the obligation, to buy electricity with a maximum capacity of 100 MW, and an energy amount of 100 GWh at a fixed price, whereby the delivery may be spread over the contract period of the first half of one year. For swing options the fixed price is often specified in a way that no up front fee for the option is necessary.
Hereditary hemochromatosis (HH) is a prevalent genetic disorder that results in the daily excess absorption of dietary iron. If untreated this disease leads to systemic organ failure and death. HH is caused by mutations to the gene coding for a protein called HFE, a type I transmembrane glycoprotein with a demonstrated role in regulating cellular iron homeostasis. HFE binds to the cell-surface receptor transferrin receptor (TfR), a dimeric type II transmembrane glycoprotein responsible for iron uptake into most mammalian cell types. TfR binds iron-loaded transferrin (Fe-Tf) from the blood and transports it to acidic recycling endosomes where iron is released from Fe-Tf in a TfR-facilitated process. Iron-free transferrin (apo-Tf) remains bound to TfR and is recycled to the cell surface, where apo-Tf rapidly dissociates from TfR upon exposure to the basic pH of blood. HFE and Fe-Tf can bind simultaneously to TfR to form a ternary complex, but HFE binding to TfR lowers the apparent affinity of the Fe-Tf/TfR interaction. This reduction could result from direct competition between HFE and Fe-Tf for receptor binding sites, from negative cooperativity, or both. We sought to understand the mechanism of HFE, Fe-Tf, and apo-Tf binding by TfR to help define HFE's role in iron homeostasis. We determined the binding constants for HFE, Fe-Tf, and apo-Tf to an extensive set of site-directed TfR mutants and discovered that HFE and Tf bind to an overlapping site on TfR, indicating the two proteins compete with each other for receptor binding. The mutagenesis results also identified differences in the contact points between TfR and the two forms of Tf, Fe-Tf and apo-Tf. By combining the mutations that are required for apo-Tf, but not Fe-Tf, binding we find that a highly conserved hydrophobic patch on the TfR surface is required for the receptor-mediated stimulation of iron release from Fe-Tf. From these data we propose a structure-based model for the mechanism of TfR-assisted iron release.
The trading activity of corporate insiders has attracted the attention of financial economists for more than 30 years. Most of the research devoted to the issue (e.g. the classical papers by Jaffee 1974, Finnerty 1976, Seyhun 1986 and Lakonishok and Lee 2001) has been motivated by the efficient markets paradigm. Analyzing the profitability of insider trades allows a test for strong form efficiency. By considering the profitability of mimicking strategies (i.e., trading strategies that buy [sell] shares after publication of the fact that insiders bought [sold]), a test for semi-strong form efficiency can be performed. The by-now common methodology is an event study in which the day on which insider trading occurred, or the day on which the insider trade was announced, are the events under scrutiny.
The determinants of insider trading profits have also been an important subject of investigation. Researchers have related measures of profitability to variables that measure the intensity of insider trading, the position of the insider within the firm, firm size, and the size of the bid-ask spread. Recent studies (most notably Fidrmuc et al. 2006) have broadened the scope of analysis by considering variables related to corporate governance and other appropriate firm-specific factors. Investigating this relationship is important, because it allows conclusions to be drawn about both the degree and the determinants of informational asymmetries between corporate insiders and the capital market.
