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The purpose of this paper is to build a phase-type risk model with stochastic return on investment and random observation periods to characterize the ruin quantities under which the insurance company may take effective investment strategies to avoid bankruptcy.

By the Markov property and Ito’s formula, this paper derives the integro-differential equations in which the interclaim times follow a phase-type distribution. Using the sinc method, this paper obtains the approximate solutions of the expected discounted penalty function. The numerical examples are given to verify the robustness of the proposed sinc method.

This paper discloses the relationship between the investment strategy and initial surplus level. The insurance company with a high initial surplus level prefers high risk portfolios to earn more profit. Contrarily, the insurance company would invest low risk portfolios to avoid bankruptcy. In addition, this paper shows that a short observation period would bring higher ruin probability.

The risk model is distinct in that a phase-type risk model is constructed with stochastic return on investment and random observation periods. These considerations in the risk model are in sharp contrast to the setting in which the stochastic return on investment is observed continuously. In practice, the insurance company only can periodically observe the surplus level to check the balance of the book. This setting, therefore, is difficult to adopt. This paper develops a sinc method to solve the approximate solutions of the expected discounted penalty function.

The purpose of this paper is to present a novel approach to coordination of multi‐agent teams, and in particular multi‐robot teams. The new approach is based on models of organisational sociology, namely the concept of social networks. The social relationships used in the model that is presented in this paper are trust and kinship relationships, but modified for use in heterogeneous multi‐robot teams.

The coordination of a robot team is achieved through task allocation. The proposed task allocation mechanism was tested in the multi‐robot team task allocation simulation.

The social networks‐based task allocation algorithm has performed according to expectations and the obtained results are very promising. Some intriguing similarities with higher mammalian societies were observed and they are discussed in this paper. The social networks‐based approach also exhibited the ability to learn and store information using social networks.

The research focused on simulated environments and further research is envisaged in the physical environments to confirm the applicability of the presented approach.

In this paper, the proposed coordination was applied to simulated multi‐robot teams. It is important to note that the proposed coordination model is not robot specific, but can also be applied to almost any multi‐agent system without major modifications.

The paper emphasizes applicability of considering multi‐robot teams as socially embodied agents. It also presents a novel and efficient approach to task allocation.

The purpose of this paper is to investigate the moderating effect of disease risk in terms of the major signals (i.e. status, reputation and self-representation) on the e-consultation platforms.

In this study, the proposed research hypotheses are tested using the transaction data collected from xywy.com (in Need of Therapy). In fact, xywy.com is one the leading e-consultation service websites in China that provides a platform for the interactions between the physicians and patients (Yu et al., 2016; Peng et al., 2015). Generally speaking, it has all the needed design elements and in other words, a standard e-consultation website should have such items/components as physician homepage, physician review, free consultation, paid consultation and recommendation systems.

The obtained results reveal that all attributes including status, reputation and self-representation have a positive impact on physician’s online order volume. Moreover, there is a positive moderating effect of disease risk onto the online reputation, indicating a higher effect exists for the diseases with high risk. However, the effect of offline status and online self-representation is not moderated by the disease risk, indicating market signals (online reputation) may have a stronger predictive power than seller signals (offline status and online self- representation), and therefore market signals are more effective when/if the disease risk is high.

E-consultation has gradually become a significant trend to provide the healthcare services, in the emerging economy such as China because of shortage of medical resources but having an adequate access in internet usage. The impacts of signals on the health care market have been validated by previous studies. However, the research focusing on the moderating effect of signaling environment in the health care industry is still lacking. As a result, the value of this research helps to bridge the aforementioned research gap.

This paper examines the Random Walk Hypothesis (RWH) for aggregate New Zealand share market returns, as well as the CRSP NYSE‐AMEX (USA) index during the 1980‐2001 period. Using several indices, we rely on the variance‐ratio test and find evidence to support the rejection of the RWH with some evidence of a momentum effect. However, we find evidence to suggest the behaviour of share prices to be time‐dependent in New Zealand. For example, we find the indices tested were closer to random after the 1987 share market crash. Further analysis showed even stronger results for periods subsequent to the passage of the Companies Act 1993 and the Financial Reporting Act 1993. We also find evidence that indices based on large capitalisation stocks are more likely to follow a random walk compared to those based on smaller stocks. For the USA index, we find stronger evidence of random behaviour in our sample period compared to the earlier period examined by Lo and Mackinlay (1988)

THERE is a tendency to regard the industrial problems which face us as unique to British firms. That is a blinkered outlook in days when the commerce of thought is international and no country is insulated from another.

I study state dependence in social assistance receipt in Germany using annual survey data from the German Socio-Economic Panel for the years 1995–2011. There is considerable observed state dependence, with an average persistence rate in benefits of 68 per cent comparing to an average entry rate of just above 3 per cent. To identify a possible structural component, I estimate a series of dynamic random-effects probit models that control for observed and unobserved heterogeneity and endogeneity of initial conditions. I find evidence of substantial structural state dependence in benefit receipt. Estimates suggest that benefit receipt one year ago is associated with an increase in the likelihood of benefit receipt today by a factor of 3.4. This corresponds to an average partial effect of 13 percentage points. Average predicted entry and persistence rates and the absolute level of structural state dependence are higher in Eastern Germany than in Western Germany. I find only little evidence for time variation in state dependence around the years of the Hartz reforms.

This chapter investigates the performance of different dimension reduction approaches for large vector autoregressions in multi-step ahead forecasts. The authors consider factor augmented VAR models using principal components and partial least squares, random subset regression, random projection, random compression, and estimation via LASSO and Bayesian VAR. The authors compare the accuracy of iterated and direct multi-step point and density forecasts. The comparison is based on macroeconomic and financial variables from the FRED-MD data base. Our findings suggest that random subspace methods and LASSO estimation deliver the most precise forecasts.