Robust optimal investment–reinsurance strategies for an insurer with multiple dependent risks

This paper considers a robust optimal investment and reinsurance problem with multiple dependent risks for an Ambiguity-Averse Insurer (AAI), who is uncertain about the model parameters. We assume that the surplus of the insurance company can be allocated to the financial market consisting of one risk-free asset and one risky asset whose price process satisfies square root factor process. Under the objective of maximizing the expected utility of the terminal surplus, by adopting the technique of stochastic control, closed-form expressions of the robust optimal strategy and the corresponding value function are derived. The verification theorem is also provided. Finally, by presenting some numerical examples, the impact of some parameters on the optimal strategy is illustrated and some economic explanations are also given. We find that the robust optimal reinsurance strategies under the generalized mean–variance premium are very different from that under the variance premium principle. In addition, ignoring model uncertainty risk will lead to significant utility loss for the AAI.     

When does data envelopment analysis outperform a naïve efficiency measurement model?

This paper compares the results from data envelopment analysis (DEA) to a naïve efficiency measurement model, which generates a scalar efficiency score by averaging all output–input ratios. Random data and real-life data are used to test the relative performance of the naïve model against various DEA models. The results suggest that the proposed the naïve model replicates DEA efficiency scores almost perfectly for constant return-to-scales and low heterogeneity in output–input data. It is therefore concluded that heterogeneity in output–input data is important to take advantage of the capability of DEA. It is also shown that heterogeneity is more relevant to efficiency measurement than the number of dimensions.     

Information enhancement—A tool for approximate representation of optimal strategies from influence diagrams

The main source of complexity problems for large influence diagrams is that the last decisions have intractably large spaces of past information. Usually, it is not a problem when you reach the last decisions; but when calculating optimal policies for the first decisions, you have to consider all possible future information scenarios. This is the curse of knowing that you shall not forget. The usual approach for addressing this problem is to reduce the information through assuming that you do forget something (Nilsson and Lauritzen, 2000, LIMID [1]), or to abstract the information through introducing new nodes (Jensen, 2008) [2]. This paper takes the opposite approach, namely to assume that you know more in the future than you actually will. We call the approach information enhancement. It consists in reducing the space of future information scenarios by adding information links. We present a systematic way of determining fruitful information links to add.     

Can belief structures improve our understanding of safety climate survey data?

Questionnaire-based surveys are a standard method used for assessing the safety climate within an organization. However, their analysis – in particular data aggregation – poses several challenges, among which are subjective judgment, incompleteness and uncertainty. This paper explores the use of approaches based on belief structures for aggregating data from safety climate questionnaires. Data relevant to this study were collected through a questionnaire administered to the employees of a nuclear research centre. The results show that, while belief structures may offer a promising way to represent data collected from questionnaires, the existing aggregation methods are not always adequate. Averaging schemes applied to belief structures seem the most suited – among the methods investigated – in the specific problem context analyzed. The analysis of the survey data shows the limitations of quantitative approaches for safety culture assessment and the need to always complement these with in-depth qualitative analysis.     

Can remembering history from predecessor promote cooperation in the next generation?

People usually think that helping the next generation to remember history can promote cooperation in dilemma games. We show that is not always the case when agents have memory. Agents play with each neighbor by game history and strategies (such as TFT and WSLS), and the next generation inherits good strategies from the predecessor. We analyze the system’s cooperation ratio by comparing the 2 sources of history at the beginning of each generation: (a) inherited from the predecessor; (b) randomly initialized with different cooperation ratio. We find that with unconditional imitation update rule, agents who remember history get lower cooperation ratio than those who randomly initialize the history; while with replicator rule, higher initial cooperation ratio promotes higher final cooperation. We also do additional experiments to investigate the R, ST, P reciprocity and strategies distribution of the systems.     

Does a Bayesian approach generate robust forecasts? Evidence from applications in portfolio investment decisions

We employ a statistical criterion (out-of-sample hit rate) and a financial market measure (portfolio performance) to compare the forecasting accuracy of three model selection approaches: Bayesian information criterion (BIC), model averaging, and model mixing. While the more recent approaches of model averaging and model mixing surpass the Bayesian information criterion in their out-of-sample hit rates, the predicted portfolios from these new approaches do not significantly outperform the portfolio obtained via the BIC subset selection method.     

Can home-owners benefit from stochastic programming models? A study of mortgage choice in Denmark

The Danish mortgage market is large and sophisticated. However, most Danish mortgage banks advise private home-owners based on simple, if sensible, rules of thumb. In recent years a number of papers (from Nielsen and Poulsen in J Econ Dyn Control 28:1267–1289, 2004 over Rasmussen and Zenios in J Risk 10:1–18, 2007 to Pedersen et al. in Ann Oper Res, 2013) have suggested a model-based, stochastic programming approach to mortgage choice. This paper gives an empirical comparison of performance over the period 2000–2010 of the rules of thumb to the model-based strategies. While the rules of thumb slightly outperform a passive benchmark on average and are less risky than pure adjustable rate loans, we find considerable gains from using the model-based strategies. Using a strategy that minimizes conditional-value-at-risk lowers average effective yearly interest rate over a 10-year horizon by 0.3–0.9 %-points (depending on the borrower’s level of conservatism) compared to the rules of thumb without increasing the risk. The answer to the question in the title is thus affirmative.     

How do coalitions get built? Evidence from an extensive form coalition game with and without communication

In the laboratory, we investigate a non-cooperative three-person coalition game with externalities and the opportunity to extend existing coalitions. One bargainer, the builder, can propose and build a coalition over two stages. We examine the hypothesis that both absolute and relative payoffs affect the coalition formation process (and outcome). We observe many inefficient two-person final coalitions, and that the distribution of outcomes is sensitive to the constellation of both absolute and relative payoffs. Relative payoffs appear to be applied more myopically than has been observed in bilateral sequential bargaining games or suggested by social preference models. We hypothesize that the prevalence of two-person coalitions stems from builder’s uncertainty about individual acceptance thresholds. In fact, allowing nonbinding communication among the bargainers increases the prevalence of efficient coalitions. The main implication is that efficient coalition building involves strategies for mitigating the strategic uncertainty inherent in building coalitions.     

Does playing several competitions influence a team’s league performance? Evidence from Spanish professional football

The sporting performance of professional football teams has often been assessed considering their results in the major regular competition, namely the national league. Here, we show that evaluating league performance without controlling for extra games played in other competitions might produce misleading results. Using Data Envelopment Analysis, we assess the performance of Spanish professional football teams in the League controlling for the extra games played in the King’s Cup, Champions League and UEFA Cup. Results show that assessing performance omitting extra games underestimates teams’ true performance in the League, the more extra games played leading to greater bias. Consequently, the multioutput nature of football must be considered when assessing team performance.     

Can an a priori error estimate for an approximate solution of an ill-posed problem be comparable with the error in data?

For a linear operator equation of the first kind with perturbed data, it is shown that the global (on typical sets) a priori error estimate for its approximate solution can have the same order as that for the approximate data only if the operator of the problem is normally solvable. If the operator of the problem is given exactly, this is possible only if the problem is well-posed (stable).     

Is the Home Equity Conversion Mortgage in the United States sustainable? Evidence from pricing mortgage insurance premiums and non-recourse provisions using the conditional Esscher transform

The purpose of this paper is to build a modeling and pricing framework to investigate the sustainability of the Home Equity Conversion Mortgage (HECM) program in the United States under realistic economic scenarios, i.e., whether the premium payments cover the fair premiums for the inherent risks in the HECM program. We note that earlier HECM models use static mortality tables, neglecting the dynamics of mortality rates and extreme mortality jumps. The earlier models also assume housing prices follow a geometric Brownian motion, which contradicts the fact that housing prices exhibit strong autocorrelation and varying volatility over time. To solve these problems, we propose a generalized Lee-Carter model with asymmetric jump effects to fit the mortality data, and model the house price index via an ARIMA-GARCH process. We then employ the conditional Esscher transform to price the non-recourse provision of reverse mortgages and compare it with the calculated mortgage insurance premiums. The HECM program turns out to be sustainable based on our model setup and parameter settings.