Theory Modeling and Empirical Evidence for Value-at-Risk based Assets Allocation Insurance Strategies

Constant Proportion Portfolio Insurance (CPPI) is the most popular portfolio insurance strategy using hedging strategy to protect principal while a wave upward or downward trend in the market is noted. Nevertheless, since the original CPPI was proposed, its performance has been limited to relevant parameters of strategy. And since there is no clear, definite and systematic rule of decision has get been proposed, it also has unstable performance and worse upside capture, especially for the multiplier (Mv) in model parameters, it has far great influence to end-of-period return. If Mv can be decided with its initial value setting and dynamic tuning via certain appropriate approach, under a decent mechanism of market timing selection, the strategy can therefore acquire excess return of min-max operation due to sharp improvement of upside capture, and also can provide hedging function within the insured volume when the market declines. This paper presents a systematic method using the value-at-risk control method to dynamically adjust the CPPI strategy parameter Mv, called asset allocation insurance strategy value-at-risk based asset allocation insurance strategy model (VALIS). We proof that the proposed model is a dynamic asset allocation insurance strategy, which is conservative but also aggressive; and shows that it is in compliance with the characteristics of idea portfolio insurance strategy, and is feasible and effective. From an empirical study of the Pan-Pacific market, we found that in any type of market or trend it is clearly better than the major benchmark indices, and it outperform other traditional portfolio insurance strategy

Macrophage migration inhibitory factor (MIF) family in arthropods : Cloning and expression analysis of two MIF and one D-dopachrome tautomerase (DDT) homologues in Mud crabs, Scylla paramamosain

Acknowledgements This research was supported by grants from the National Natural Science Foundation of China (Nos. 31172438 and U1205123), the Natural Science Foundation of Fujian Province (No. 2012J06008 and 201311180002) and the projects-sponsored by SRF. TW received funding from the MASTS pooling initiative (The Marine Alliance for Science and Technology for Scotland) funded by the Scottish Funding Council (grant reference HR09011) and contributing institutions.Peer reviewedPostprin

Reliability of 95% confidence interval revealed by expected quality-of-life scores: an example of nasopharyngeal carcinoma patients after radiotherapy using EORTC QLQ-C 30

<p>Abstract</p> <p>Background</p> <p>Many researchers use observed questionnaire scores to evaluate score reliability and to make conclusions and inferences regarding quality-of-life outcomes. The amount of false alarms from medical diagnoses that would be avoided if observed scores were substituted with expected scores is interesting, and understanding these differences is important for the care of cancer patients. Using expected scores to estimate the reliability of 95% confidence intervals (CIs) is rarely reported in published papers. We investigated the reliability of patient responses to a quality-of-life questionnaire and made recommendations for future studies of the quality of life of patients.</p> <p>Methods</p> <p>A total of 115 patients completed the EORTC core questionnaire QLQ-C30 (version 3) after radiotherapy. The observed response scores, assumed to be one-dimensional, were summed and transformed into expected scores using the Rasch rating scale model with WINSTEPS software. A series of simulations was performed using a unified bootstrap procedure after manipulating scenarios with different questionnaire lengths and patient numbers to estimate the reliability at 95% confidence intervals. Skewness analyses of the 95% CIs were compared to detect different effects between groups according to the two data sets of observed and expected response scores.</p> <p>Results</p> <p>We found that (1) it is necessary to report CIs for reliability and skewness coefficients in papers; (2) data derived from expected response scores are preferable to making inferences; and (3) visual representations displaying the 95% CIs of skewness values applied to item-by-item analyses can provide a useful interpretation of quality-of-life outcomes.</p> <p>Conclusion</p> <p>Reliability coefficients can be reported with 95% CIs by statistical software to evaluate the internal consistency of respondent scores on questionnaire items. The SPSS syntax procedures for estimating the reliability of the 95% CI, expected score generation and visual skewness analyses are demonstrated in this study. We recommend that effect sizes such as a 95% CI be reported along with <it>p </it>values reporting significant differences in quality-of-life studies.</p

Web-based computer adaptive assessment of individual perceptions of job satisfaction for hospital workplace employees

<p>Abstract</p> <p>Background</p> <p>To develop a web-based computer adaptive testing (CAT) application for efficiently collecting data regarding workers' perceptions of job satisfaction, we examined whether a 37-item Job Content Questionnaire (JCQ-37) could evaluate the job satisfaction of individual employees as a single construct.</p> <p>Methods</p> <p>The JCQ-37 makes data collection via CAT on the internet easy, viable and fast. A Rasch rating scale model was applied to analyze data from 300 randomly selected hospital employees who participated in job-satisfaction surveys in 2008 and 2009 via non-adaptive and computer-adaptive testing, respectively.</p> <p>Results</p> <p>Of the 37 items on the questionnaire, 24 items fit the model fairly well. Person-separation reliability for the 2008 surveys was 0.88. Measures from both years and item-8 job satisfaction for groups were successfully evaluated through item-by-item analyses by using <it>t</it>-test. Workers aged 26 - 35 felt that job satisfaction was significantly worse in 2009 than in 2008.</p> <p>Conclusions</p> <p>A Web-CAT developed in the present paper was shown to be more efficient than traditional computer-based or pen-and-paper assessments at collecting data regarding workers' perceptions of job content.</p