A multivariate GARCH model with an infinite hidden Markov mixture

This paper proposes a new Bayesian semiparametric model that combines a multivariate GARCH (MGARCH) component and an infinite hidden Markov model. The new model nonparametrically approximates both the shape of unknown returns distributions and their short-term evolution. It also captures the smooth trend of the second moment with the MGARCH component and the potential skewness, kurtosis, and volatility roughness with the Bayesian nonparametric component. The results show that this more-sophisticated econometric model not only has better out-of-sample density forecasts than benchmark models, but also provides positive economic gains for a CRRA investor at different risk-aversion levels when transaction costs are assumed. After considering the transaction costs, the proposed model dominates all benchmark models/portfolios when No Short-Selling or No Margin-Trading restriction is imposed

The Association of Hobbies and Leisure Activities with Physician Burnout and Disengagement

Introduction: Burnout among physicians is a worldwide burden. While many causes of physician stress have been reported, we have found few quantitative studies of associations between burnout and participation in hobbies and interests outside of medicine. Our objective was to determine if health care professional burnout/disengagement could be mitigated by incorporating leisure interests and to characterize which specific interests, if any, are most significantly related. Methods: We conducted an online survey of 2,563 US-based physicians and 512 residents/fellows and queried their participation in a list of 117 individual hobbies, which we then further categorized into three perceived levels of social interactivity: 36 as “social,” 47 “isolated,” and 34 “indeterminate.” We utilized the Oldenburg Burnout Inventory to quantitate burnout and disengagement. In each of our 15 major categories of hobbies, burnout was significantly lower in those who were active in that category compared with those who were not (p ≤ 0.02) or who had given up certain hobbies (p ≤ 0.03). The highest levels of burnout were associated with discontinuance of hobbies, directly proportional to the number of hobbies given up. Across all demographic groups, lower burnout and disengagement levels were associated with a higher number of active hobbies and leisure activities. The least burnout and disengagement were associated with the subsets we defined as the most “social.” Specifically, despite being among the favorite hobbies by the majority of respondents, listening to music, home-based watching of TV and movies, and use of internet and video games were associated with the highest level of exhaustion. Results: Significant differences were seen across age groups, genders, and physician specialties in the level of burnout (p \u3c 0.01, p \u3c 0.01, p = 0.02, respectively) and job disengagement (p \u3c0.01, p = 0.02, p \u3c 0.01, respectively). Younger providers (age \u3c 60) and women had higher levels of burnout. Trainees had higher levels of burnout than full time, part time or retired physicians. North American graduates reported a slightly higher rate of burnout and disengagement than international graduates. 93.9% of physicians viewed outside interests as a substantial mitigation factor for burnout and disengagement. Conclusion: Our study identified associations rather than causality. Nevertheless, emphasizing hobbies and non-medical outside interests might well prove useful to temper epidemic burnout among healthcare professionals. We especially encourage those hobbies with stronger social underpinnings

Risk assessment model for sleep disturbance based on gastrointestinal myoelectrical activity in middle-aged and elderly people

BackgroundSleep disturbance has become a considerable factor affecting the quality of life for middle-aged and elderly people; however, there are still many obstacles to screening sleep disturbance for those people. Given the growing awareness of the association between gastrointestinal function and sleep disturbance, our study aims to predict the risk of sleep disturbance using gastrointestinal electrophysiological signals.MethodsThe Pittsburgh Sleep Quality Index and gastrointestinal electrophysiological signals of 914 participants in western China were used to establish the model. Demographic characteristics and routine blood test were collected as covariates. Participants were randomly assigned into two sets with a 7:3 ratio for training and validation. In the training set, the least absolute shrinkage and selection operator (LASSO) regression and stepwise logistic regression were used, respectively for variables selection and optimization. To assess the model performance, receiver operator characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were utilized. Then, validation was performed.ResultsThirteen predictors were chosen from 46 variables by LASSO regression. Then, age, gender, percentage of normal slow wave and electrical spreading rate on the pre-meal gastric channel, dominant power ratio on the post-meal gastric channel, coupling percent and dominant frequency on the post-meal intestinal channel were the seven predictors reserved by logistic regression. The area under ROC curve was 0.65 in the training set and 0.63 in the validation set, both exhibited moderate predictive ability. Furthermore, by overlapping the DCA results of two data-sets, there might be clinical net benefit if 0.35 was used as reference threshold for high risk of sleep disturbance.ConclusionThe model performs a worthy predictive potency for sleep disturbance, which not only provides clinical evidence for the association of gastrointestinal function with sleep disturbance, but also can be considered as an auxiliary assessment for screening sleep disturbance

Securing Smart Contract On The Fly

We present Solythesis, a source to source Solidity compiler which takes a smart contract code and a user specified invariant as the input and produces an instrumented contract that rejects all transactions that violate the invariant. The design of Solythesis is driven by our observation that the consensus protocol and the storage layer are the primary and the secondary performance bottlenecks of Ethereum, respectively. Solythesis operates with our novel delta update and delta check techniques to minimize the overhead caused by the instrumented storage access statements. Our experimental results validate our hypothesis that the overhead of runtime validation, which is often too expensive for other domains, is in fact negligible for smart contracts. The CPU overhead of Solythesis is only 0.12% on average for our 23 benchmark contracts