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This study explores hedging strategies that use the KTB futures to hedge the price risk of the KTB spot portfolio. The study establishes the price sensitivity, risk-minimization, bivariate GARCH (1,1) models as hedging models, and analyzes their hedging performances. The sample period covers from September 29, 1999 to September 18, 2001. Time-matched prices at 11:00 (11:30) of the KTB futures and spot were used in the analysis. The most important findings may be summarized as follows. First, while the average hedge ration of the price sensitivity model is close to one, both the risk-minimization and GARCH model exhibit hedge ratios that are substantially lower than one. Hedge ratios tend to be greater for daily data than for weekly data. Second, for the daily in-sample data, hedging effectiveness is the highest for the GARCH model with time-varying hedge ratios, but the risk-minimization model with constant hedge ratios is not far behind the GARCH model in its hedging performance. In the case of out-of-sample hedging effectiveness, the GARCH model is the best for the KTB spot portfolio, and the risk-minimization model is the best for the corporate bond portfolio. Third, for daily data, the in-sample hedge shows a better performance than the out-of-sample hedge, except for the risk-minimization hedge against the corporate bond portfolio. Fourth, for the weekly in-sample hedges, the price sensitivity model is the worst and the risk-minimization model is the best in hedging the KTB spot portfolio. While the GARCH model is the best against the KTB +corporate bond portfolio, the risk-minimization model is generally as good as the GARCH model. The risk-minimization model performs the best for the weekly out-of-sample data, and the out-of-sample hedges are better than the in-sample hedges. Fifth, while the hedging performance of the risk-minimization model with daily moving window seems somewhat superior to the traditional risk-minimization model when the trading volume increased one year after the inception of the KTB futures, on the average the traditional model is better than the moving-window model. For weekly data, the traditional model exhibits a better performance. Overall, in the Korean bond markets, investors are encouraged to use the simple risk-minimization model to hedge the price risk of the KTB spot and corporate bond portfolios.

This paper studies hedging strategies that use the KOSDAQ50 index futures to hedge the price risk of the KOSDAQ50 index spot portfolio. This study uses the minimum variance hedge model and bivariate ECT-GARCH (1,1) model as hedging models, and analyzes their hedging performances. The sample period covers from January 31, 2001 to December 31, 2002. The most important findings may be summarized as follows. First, both the risk-minimization and GARCH model exhibit hedge ratios that are substantially lower than one. Hedge ratios of the risk-minimization tend to be higher than those of GARCH model. Second, for the in-sample data, hedging effectiveness of GARCH model is higher than that of the risk-minimization, while for the out-of-sample data, hedging effectiveness of the risk-minimization with constant hedge ratios is not far behind the GARCH model in its hedging performance. Third, the hedging performance of KOSDAQ50 index futures is lower than that of KOSPI200 index futures, but higher than that of KTB futures. In conclusion, in the KOSDAQ50 index market, investors are encouraged to use the simple risk-minimization model to hedge the price risk of KOSDAQ50 spot portfolios.

The purpose of this paper is to find the optimal hedging strategy when an investor has budget constraints on both the initial capital and the future cash flow.

The paper follows the utility minimization of the total cost, using convex utility functions on both initial capital and future cash flows.

Closed‐form solutions of optimal hedging strategies are found in some specific but popular cases. It is also found that this method corresponds to the local risk minimization method in quadratic hedging.

Hedging strategies are calculated for only two popular choices. One may want to calculate hedging strategies for other popular utility functions such as power utility or HARA utility.

When a trader has some budget constraint in both initial capital and future cash flows, this paper gives a simple alternative.

Budget constraints on both initial capital and future cash flow are new to this kind of study. Connection to the local risk minimization strategy is original too.

This study aims to examine the use of cryptocurrencies and transactions in medical tourism and to discuss how this use provides advantages to healthcare institutions and states that provide medical tourism services.

This study is a descriptive, cross-sectional, correlational and methodological quantitative research. Data were collected through a questionnaire with 555 potential medical tourists. Data were analyzed with Pearson correlation and hierarchical regression using STATA.

The correlation results showed a statistically significant high and positive correlation between the use of cryptocurrencies and transactions in medical tourism and the medical tourist's intention. The variables that contributed to the medical tourist's intention were monetary risk minimization, access-security and malpractice-civil trial in the highest order of contribution. Accordingly, the monetary risk minimization was the most contributing to the medical tourist's intention.

This study provides a piece of initial empirical evidence on the contribution of using cryptocurrencies and transactions in medical tourism.

The purpose of this paper is to propose a methodology to reduce the potential risk of failures in high‐volume manufacturing.

The research documented here represents an even balance of theoretical and practical development, with validation of the methodology – referred to as integrated risk minimisation (IRM).

The success of the IRM is measured in terms of reduced defect rates, through either prediction or earlier detection of defects. The combination of an inline design and immediate operator feedback on all detected defects was a critical design element.

The electroplating process was chosen as a suitable case study to provide the platform from which the IRM could be developed and tested. Implementation into industry has generated the proof to support the IRM as a methodology that can successfully reduce potential risks in high‐volume manufacturing.

In a unique approach, failure modes and effects analysis is built into the IRM methodology, thus establishing a “closed loop” process. The IRM methodology is suitably generic, to allow the achievement of similar results for any high‐volume process.

This paper offers an introduction to dynamic economic planning under uncertainty, i.e. the use of econometric models together with mathematical optimization methods for the analysis and quantitative determination of optimal economic policies. The corresponding basic methodology (optimal feedback stochastic control of linear econometric models given a quadratic cost functional) is presented with particular regard to its practical application. The method is then applied for demonstration purposes to an econometric model of the Federal Republic of Germany.

The knowledge management (KM) sharing process plays an essential role in manufacturing under Green Implementation Network (GIN). This study aims to analyze the KM process of adopting a GIN to determine the relative importance of technical risk minimization. The proposed conceptual model was tested by considering two interrelated concepts (GIN and KM process).

Primary data from manufacturing companies in Henan province, China, were collected through 276 questionnaires. PLS-SEM and fuzzy set qualitative comparative analysis (fsQCA) were applied to investigate the configurational path of minimizing the technical risk in the manufacturing process.

The findings showed that the GIN and KM processes minimize the technical risk. The fsQCA reported multiple configurational of GIN and KM processes validated toward technical risk reduction. The study's findings contribute to the existing body of knowledge on technical risk reduction in manufacturing concerns by investigating the complex intersection between GIN and KM process.

This research adds to current GIN and KM literature by focusing on the green process using a resource-based view (RBV) and socio-technical theories. The current study provides practical and theoretical justification for explaining the relationship between GIN and KM processes. Moreover, this study adds to the literature by providing evidence that KM is an essential manufacturing industry enabler in minimizing technical risk.

This study aims to compare the level of needs for home-care robots amongst older adults, family caregivers and home-care staff and clarify the factors constituting these needs.

A cross-sectional, anonymous questionnaire survey was administered. It included 52 items related to needs for home-care robots rated on a four-point Likert scale. Means and standard deviations were calculated, and the Kruskal-Wallis test was performed for each item. Factor analysis was conducted on the needs of home-care staff.

Responses from 79 older adults, 54 family caregivers and 427 home-care staff were analysed. For all three groups, the level of agreement was high for the following needs: to inform family and support personnel immediately when older adults fall, about their location in case of natural disasters and about mismanagement of fire by older adults with dementia. For family caregivers and home-care staff, the level of need concerning monitoring was higher than for older adults. Extracted using factor analysis, the six factors representing the essential needs for home-care robots were risk minimisation, daily monitoring of the physical condition, supporting activities of daily living (ADL) and instrumental ADL, pre-empting problems, communication and miscellaneous support.

The results showed that the education of caregivers and the co-design process of robot development should involve home-care staff, older adults and family caregivers, which are important for making decisions about the use of home-care robots for older adults.

One of the agency conflicts between investors and managers in fund management is reflected by risk‐taking behaviors led by their different goals. The investors may stop their investments in risky assets before the end of the investment horizon to minimize risk, while the managers may do so to entrench their reputation so as to pursue better opportunities in the labor market. This study aims to consider a one principal‐one agent model to investigate this agency conflict.

The paper derives optimal asset allocation strategies for both parties by extending the traditional dynamic mean‐variance model and considering possibilities of optimal early stopping. Doing so illustrates the principal‐agent conflict regarding risk‐taking behaviors and managerial investment myopia in fund management.

This paper not only paves the way for further studies along this line, but also presents results useful for practitioners in the money management industry.

According to the theoretical analysis and numerical simulations, the paper shows that potential early stop can make the agency conflict worsen, and it proposes a way to mitigate this agency problem.

As one of the exploratory studies in investigating agency conflict regarding risk‐taking behaviors in the literature, this study makes multiple contributions to the literature on fund management, asset allocation, portfolio optimization, and risk management.

The purpose of this paper is to introduce some basic knowledge of statistical learning theory (SLT) based on random set samples in set‐valued probability space for the first time and generalize the key theorem and bounds on the rate of uniform convergence of learning theory in Vapnik, to the key theorem and bounds on the rate of uniform convergence for random sets in set‐valued probability space. SLT based on random samples formed in probability space is considered, at present, as one of the fundamental theories about small samples statistical learning. It has become a novel and important field of machine learning, along with other concepts and architectures such as neural networks. However, the theory hardly handles statistical learning problems for samples that involve random set samples.

Being motivated by some applications, in this paper a SLT is developed based on random set samples. First, a certain law of large numbers for random sets is proved. Second, the definitions of the distribution function and the expectation of random sets are introduced, and the concepts of the expected risk functional and the empirical risk functional are discussed. A notion of the strict consistency of the principle of empirical risk minimization is presented.

The paper formulates and proves the key theorem and presents the bounds on the rate of uniform convergence of learning theory based on random sets in set‐valued probability space, which become cornerstones of the theoretical fundamentals of the SLT for random set samples.

The paper provides a studied analysis of some theoretical results of learning theory.