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A new search technique is developed to locate the hidden target (object) in one of the Ndisjoint regions that are not identical. The lost object follows a bivariate distribution. Minimizing the search effort with discount reward has been applied instead of reducing the expected search time. Moreover, the minimum number of searchers is determined in order to minimize the total expected cost. Assuming the object's position has a Circular Normal distribution, the Kuhn-Tucker necessary conditions are implemented to get the optimum search plan.

Some international pharmaceutical companies have succeeded in producing vaccines against COVID-19. Countries all over the world have aimed to obtain these vaccines with minimum cost. We consider a set of K-independent Markovian waiting lists. Each list contains a set of countries, where each one of them has an exponential service time and a Poisson arrival process. These companies differ in some characteristics such as the vaccine production cost and the speed of the required quantity delivery. We present a new detection model that helps in providing an appropriate decision to choose a suitable company. Moreover, the concept of balking and the retention of reneged countries is taken into consideration under the quality control process of each waiting list. Under steady state, we face an interesting and difficult discrete stochastic optimization problem. Its solution gives an optimal distribution of the searching effort, which is bounded by a known probability distribution. A simulation study has been derived to get the minimum value of the paid cost random values. The highest service rate, the total expected profit of each queuing system, and the optimum performance measures, which depend on this cost, have been obtained to show the effectiveness of this model.

The purpose of this paper is to examine the spillover of returns, information and volatility of returns, and conditional variance-covariance between the stock markets of developed countries namely the United States of America, the United Kingdom and China (US, UK and CH) and the stock markets of Gulf Cooperation Council (GCC) countries (Kuwait, United Arab Emirates, Qatar, Saudi Arabia, Oman, and Bahrain) using daily returns spanned from 2 March 2003 to 9 December 2010. We consider shocks and volatility spillover model by applying a multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) model using; MGARCH-BEKK to identify the source and magnitude of volatility and shock spillover. We get the correlation between GCC markets is positive, indicating that there is a common factor which is driving the markets towards the same direction. Evidence shows that the own-shocks and volatility in GCC markets are highly significant. Cross-information spillover effects, as another observable trend, are found between Qatar and Oman. Furthermore, the results show that UA is significantly affected by spillover (return, shocks and volatility) from developed markets, while there have been no significant effects seen from Kuwait markets. This study takes a new empirical look in the sense that the models incorporating all the countries under investigation are estimated jointly utilizing multivariate GARCH-BEKK formulation. In addition, this paper should be interesting for academicians as well as practitioners. Including those interested in modelling multivariate volatility for financial market risk management.