Risks2014, 2(2), 146-170; doi:10.3390/risks2020146 - published online 15 April 2014 Show/Hide Abstract
Abstract: In 2006, the Netherlands commenced market based reforms in its health care system. The reforms included selective contracting of health care providers by health insurers. This paper focuses on how health insurers may increase their market share on the health insurance market through selective contracting of health care providers. Selective contracting is studied by eliciting the preferences of health care consumers for attributes of health care services that an insurer could negotiate on behalf of its clients with health care providers. Selective contracting may provide incentives for health care providers to deliver the quality that consumers need and demand. Selective contracting also enables health insurers to steer individual patients towards selected health care providers. We used a stated preference technique known as a discrete choice experiment to collect and analyze the data. Results indicate that consumers care about both costs and quality of care, with healthy consumers placing greater emphasis on costs and consumers with poorer health placing greater emphasis on quality of care. It is possible for an insurer to satisfy both of these criteria by selective contracting health care providers who consequently purchase health care that is both efficient and of good quality.
Risks2014, 2(2), 132-145; doi:10.3390/risks2020132 - published online 1 April 2014 Show/Hide Abstract
Abstract: This paper is focused on solving different hard optimization problems that arise in the field of insurance and, more specifically, in reinsurance problems. In this area, the complexity of the models and assumptions considered in the definition of the reinsurance rules and conditions produces hard black-box optimization problems (problems in which the objective function does not have an algebraic expression, but it is the output of a system (usually a computer program)), which must be solved in order to obtain the optimal output of the reinsurance. The application of traditional optimization approaches is not possible in this kind of mathematical problem, so new computational paradigms must be applied to solve these problems. In this paper, we show the performance of two evolutionary and swarm intelligence techniques (evolutionary programming and particle swarm optimization). We provide an analysis in three black-box optimization problems in reinsurance, where the proposed approaches exhibit an excellent behavior, finding the optimal solution within a fraction of the computational cost used by inspection or enumeration methods.
Risks2014, 2(2), 103-131; doi:10.3390/risks2020103 - published online 1 April 2014 Show/Hide Abstract
Abstract: We argue that the present crisis and stalling economy that have been ongoing since 2007 are rooted in the delusionary belief in policies based on a “perpetual money machine” type of thinking. We document strong evidence that, since the early 1980s, consumption has been increasingly funded by smaller savings, booming financial profits, wealth extracted from house price appreciation and explosive debt. This is in stark contrast with the productivity-fueled growth that was seen in the 1950s and 1960s. We describe the transition, in gestation in the 1970s, towards the regime of the “illusion of the perpetual money machine”, which started at full speed in the early 1980s and developed until 2008. This regime was further supported by a climate of deregulation and a massive growth in financial derivatives designed to spread and diversify the risks globally. The result has been a succession of bubbles and crashes, including the worldwide stock market bubble and great crash of October 1987, the savings and loans crisis of the 1980s, the burst in 1991 of the enormous Japanese real estate and stock market bubbles, the emerging markets bubbles and crashes in 1994 and 1997, the Long-Term Capital Management (LTCM) crisis of 1998, the dotcom bubble bursting in 2000, the recent house price bubbles, the financialization bubble via special investment vehicles, the stock market bubble, the commodity and oil bubbles and the current debt bubble, all developing jointly and feeding on each other until 2008. This situation may be further aggravated in the next decade by an increase in financialization, through exchange-traded-funds (ETFs), speed and automation, through algorithmic trading and public debt, and through growing unfunded liabilities. We conclude that, to get out of this catch 22 situation, we should better manage and understand the incentive structures in our society, we need to focus our efforts on our real economy and we have to respect and master the art of planning and prediction. Only gradual change, with a clear long term planning, can steer our financial and economic system from the turbulence associated with the perpetual money machine to calmer and more sustainable waters.
Risks2014, 2(2), 89-102; doi:10.3390/risks2020089 - published online 1 April 2014 Show/Hide Abstract
Abstract: In this work, we examine Thomas Reuters News Analytics (TRNA) data. We found several fascinating discoveries. First, we document the phenomenon that we label “Jam-the-Close”: The last half hour of trading (15:30 to 16:00 EST) contains a substantial and statistically significant amount of news sentiment releases. This finding is robust across years and months of the year. Next, upon further investigations we found that the “novelty” score is on average 0.67 in this period vs. 2.09 prior to midday. This indicates that “new” news is flowing at a rapid pace prior to the close. Finally, we discuss the implication of such phenomena in the context of existing financial literature.
Risks2014, 2(1), 74-88; doi:10.3390/risks2010074 - published online 14 March 2014 Show/Hide Abstract
Abstract: Business and credit cycles have an impact on credit insurance, as they do on other businesses. Nevertheless, in credit insurance, the impact of the systemic risk is even more important and can lead to major losses during a crisis. Because of this, the insurer surveils and manages policies almost continuously. The management actions it takes limit the consequences of a downturning cycle. However, the traditional modeling of economic capital does not take into account this important feature of credit insurance. This paper proposes a model aiming to estimate future losses of a credit insurance portfolio, while taking into account the insurer’s management actions. The model considers the capacity of the credit insurer to take on less risk in the case of a cycle downturn, but also the inverse, in the case of a cycle upturn; so, losses are predicted with a more dynamic perspective. According to our results, the economic capital is over-estimated when not considering the management actions of the insurer.
Risks2014, 2(1), 49-73; doi:10.3390/risks2010049 - published online 11 March 2014 Show/Hide Abstract
Abstract: In a bonus-malus system in car insurance, the bonus class of a customer is updated from one year to the next as a function of the current class and the number of claims in the year (assumed Poisson). Thus the sequence of classes of a customer in consecutive years forms a Markov chain, and most of the literature measures performance of the system in terms of the stationary characteristics of this Markov chain. However, the rate of convergence to stationarity may be slow in comparison to the typical sojourn time of a customer in the portfolio. We suggest an age-correction to the stationary distribution and present an extensive numerical study of its effects. An important feature of the modeling is a Bayesian view, where the Poisson rate according to which claims are generated for a customer is the outcome of a random variable specific to the customer.