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The sixth sanction package of the European Union in the context of the aggression against Ukraine excludes Sberbank, the largest Russian bank, from the SWIFT network. The increasing use of SWIFT as a tool for sanctions stimulates the rollout of alternative payment information systems by the governments of Russia and China. This policy white paper informs about the alternatives at hand, as well as their advantages and disadvantages. Careful reflection about these issues is particularly important, given the call for an “Economic Article 5” tabled for the next NATO meeting. Finally, the white paper highlights the need for institutional reforms, if policymakers decide to return SWIFT to the status of a global public good after the war.
The use of catastrophe bonds (cat bonds) implies the problem of the so called basis risk, resulting from the fact that, in contrast to traditional reinsurance, this kind of coverage cannot be a perfect hedge for the primary’s insured portfolio. On the other hand cat bonds offer some very attractive economic features: Besides their usefulness as a solution to the problems of moral hazard and default risk, an important advantage of cat bonds can be seen in the presumably lower transaction costs compared to (re)insurance products. Insurance coverage usually incurs costs of acquisition, monitoring and loss adjustment, all of which can be reduced by making use of the financial markets. Additionally, cat bonds are only weakly correlated with market risk, implying that in perfect financial markets these securities could be traded at a price including just small risk premiums. Although these aspects have been identified in economic literature, to our knowledge there has been no publication so far that formally addresses the trade-off between basis risk and transaction cost. In this paper, therefore, we introduce a simple model that enables us to analyze cat bonds and reinsurance as substitutional risk management tools in a standard insurance demand theory environment. We concentrate on the problem of basis risk versus transaction cost, and show that the availability of cat bonds affects the structure of optimal reinsurance contract design in an interesting way, as it leads to an increase of indemnity for small losses and a decrease of indemnity for large losses.
This paper proposes the Shannon entropy as an appropriate one-dimensional measure of behavioural trading patterns in financial markets. The concept is applied to the illustrative example of algorithmic vs. non-algorithmic trading and empirical data from Deutsche Börse's electronic cash equity trading system, Xetra. The results reveal pronounced differences between algorithmic and non-algorithmic traders. In particular, trading patterns of algorithmic traders exhibit a medium degree of regularity while non-algorithmic trading tends towards either very regular or very irregular trading patterns. JEL Classification: C40, D0, G14, G15, G20
According to disposition effect theory, people hold losing investments too long. However, many investors eventually sell at a loss, and little is known about which psychological factors contribute to these capitulation decisions. This study integrates prospect theory, utility maximization theory, and theory on reference point adaptation to argue that the combination of a negative expectation about an investment’s future performance and a low level of adaptation to previous losses leads to a greater capitulation probability. The test of this hypothesis in a dynamic experimental setting reveals that a larger total loss and longer time spent in a losing position lead to downward adaptations of the reference point. Negative expectations about future investment performance lead to a greater capitulation probability. Consistent with the theoretical framework, empirical evidence supports the relevance of the interaction between adaptation and expectation as a determinant of capitulation decisions. Keywords: Investments , Adaptation , Reference Point , Capitulation , Selling Decisions , Disposition Effect , Financial Markets JEL Classification: D91, D03, D81