High-frequency enhanced VaR: A robust univariate realized volatility model for diverse portfolios and market conditions.

In the field of financial risk management, the accuracy of portfolio Value-at-Risk (VaR) forecasts is of critical importance to both practitioners and academics. This study pioneers a comprehensive evaluation of a univariate model that leverages high-frequency intraday data to improve portfolio VaR forecasts, providing a novel contrast to both univariate and multivariate models based on daily data. Existing research has used such high-frequency-based univariate models for index portfolios, it has not adequately studied their robustness for portfolios with diverse risk profiles, particularly under changing market conditions, such as during crises. Our research fills this gap by proposing a refined univariate long-memory realized volatility model that incorporates realized variance and covariance metrics, eliminating the necessity for a parametric covariance matrix. This model captures the long-run dependencies inherent in the volatility process and provides a flexible alternative that can be paired with appropriate return innovation distributions for VaR estimation. Empirical analyses show that our methodology significantly outperforms traditional univariate and multivariate Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) models in terms of forecasting accuracy while maintaining computational simplicity and ease of implementation. In particular, the inclusion of high-frequency data in univariate volatility models not only improves forecasting accuracy but also streamlines the complexity of portfolio risk assessment. This research extends the discourse between academic research and financial practice, highlighting the transformative impact of high-frequency data on risk management strategies within the financial sector.

Price, quality, and market dynamics of malaria rapid diagnostic tests: analysis of Global Fund 2009-2018 data.

BACKGROUND: Rapid diagnostic tests (RDTs) for malaria are a vital part of global malaria control. Over the past decade, RDT prices have declined, and quality has improved. However, the relationship between price and product quality and their larger implications on the market have yet to be characterized. This analysis used purchase data from the Global Fund together with product quality data from the World Health Organization (WHO) and Foundation for Innovative New Diagnostics (FIND) Malaria RDT Product Testing Programme to understand three unanswered questions: (1) Has the market share by quality of RDTs in the Global Fund's procurement orders changed over time? (2) What is the relationship between unit price and RDT quality? (3) Has the market for RDTs financed by the Global Fund become more concentrated over time? METHODS: Data from 10,075 procurement transactions in the Global Fund's database, which includes year, product, volume, and price, was merged with product quality data from all eight rounds of the WHO-FIND programme, which evaluated 227 unique RDT products. To describe trends in market share by quality level of RDT, descriptive statistics were used to analyse trends in market share from 2009 to 2018. A generalized linear regression model was then applied to characterize the relationship between price and panel detection score (PDS), adjusting for order volume, year purchased, product type, and manufacturer. Third, a Herfindahl-Hirschman Index (HHI) score was calculated to characterize the degree of market concentration. RESULTS: Lower-quality RDTs have lost market share between 2009 and 2018, as have the highest-quality RDTs. No statistically significant relationship between price per test and PDS was found when adjusting for order volume, product type, and year of purchase. The HHI was 3,570, indicating a highly concentrated market. CONCLUSIONS: Advancements in RDT affordability, quality, and access over the past decade risk stagnation if health of the RDT market as a whole is neglected. These results suggest that from 2009 to 2018, this market was highly concentrated and that quality was not a distinguishing feature between RDTs. This information adds to previous reports noting concerns about the long-term sustainability of this market. Further research is needed to understand the causes and implications of these trends.

Skilled Nursing Facility Participation in a Voluntary Medicare Bundled Payment Program: Association With Facility Financial Performance.

IMPORTANCE: Model 3 of the Bundled Payments for Care Improvement (BPCI) is an alternative payment model in which an entity takes accountability for the episode costs. It is unclear how BPCI affected the overall skilled nursing facility (SNF) financial performance and the differences between facilities with differing racial/ethnic and socioeconomic status (SES) composition of the residents. OBJECTIVE: The objective of this study was to determine associations between BPCI participation and SNF finances and across-facility differences in SNF financial performance. DESIGN, SETTING, AND PARTICIPANTS: A longitudinal study spanning 2010-2017, based on difference-in-differences analyses for 575 persistent-participation SNFs, 496 dropout SNFs, and 13,630 eligible nonparticipating SNFs. MAIN OUTCOME MEASURES: Inflation-adjusted operating expenses, revenues, profit, and profit margin. RESULTS: BPCI was associated with reductions of $0.63 million in operating expenses and $0.57 million in operating revenues for the persistent-participation group but had no impact on the dropout group compared with nonparticipating SNFs. Among persistent-participation SNFs, the BPCI-related declines were $0.74 million in operating expenses and $0.52 million in operating revenues for majority-serving SNFs; and $1.33 and $0.82 million in operating expenses and revenues, respectively, for non-Medicaid-dependent SNFs. The between-facility SES gaps in operating expenses were reduced (differential difference-in-differences estimate=$1.09 million). Among dropout SNFs, BPCI showed mixed effects on across-facility SES and racial/ethnic differences in operating expenses and revenues. The BPCI program showed no effect on operating profit measures. CONCLUSIONS: BPCI led to reduced operating expenses and revenues for SNFs that participated and remained in the program but had no effect on operating profit indicators and mixed effects on SES and racial/ethnic differences across SNFs.

People underestimate the errors made by algorithms for credit scoring and recidivism prediction but accept even fewer errors.

This study provides the first representative analysis of error estimations and willingness to accept errors in a Western country (Germany) with regards to algorithmic decision-making systems (ADM). We examine people's expectations about the accuracy of algorithms that predict credit default, recidivism of an offender, suitability of a job applicant, and health behavior. Also, we ask whether expectations about algorithm errors vary between these domains and how they differ from expectations about errors made by human experts. In a nationwide representative study (N = 3086) we find that most respondents underestimated the actual errors made by algorithms and are willing to accept even fewer errors than estimated. Error estimates and error acceptance did not differ consistently for predictions made by algorithms or human experts, but people's living conditions (e.g. unemployment, household income) affected domain-specific acceptance (job suitability, credit defaulting) of misses and false alarms. We conclude that people have unwarranted expectations about the performance of ADM systems and evaluate errors in terms of potential personal consequences. Given the general public's low willingness to accept errors, we further conclude that acceptance of ADM appears to be conditional to strict accuracy requirements.

Trade informativeness and liquidity in Bitcoin markets.

Liquid markets are driven by information asymmetries and the injection of new information in trades into market prices. Where market matching uses an electronic limit order book (LOB), limit orders traders may make suboptimal price and trade decisions based on new but incomplete information arriving with market orders. This paper measures the information asymmetries in Bitcoin trading limit order books on the Kraken platform, and compares these to prior studies on equities LOB markets. In limit order book markets, traders have the option of waiting to supply liquidity through limit orders, or immediately demanding liquidity through market orders or aggressively priced limit orders. In my multivariate analysis, I control for volatility, trading volume, trading intensity and order imbalance to isolate the effect of trade informativeness on book liquidity. The current research offers the first empirical study of Glosten (1994) to yield a positive, and credibly large transaction cost parameter. Trade and LOB datasets in this study were several orders of magnitude larger than any of the prior studies. Given the poor small sample properties of GMM, it is likely that this substantial increase in size of datasets is essential for validating the model. The research strongly supports Glosten's seminal theoretical model of limit order book markets, showing that these are valid models of Bitcoin markets. This research empirically tested and confirmed trade informativeness as a prime driver of market liquidity in the Bitcoin market.

Worldwide production on sleep apnea from 2009-2018. Analysis of the ability to secure funding and international collaboration networks.

BACKGROUND: Obstructive sleep apnea (OSA) is an emerging health problem, but information on scientific production in this subject area is scarce. We aim to evaluate the scientific production on OSA from 2009 to 2018 to illustrate its worldwide distribution, topic areas, and ability to secure funding, as well as to describe international collaboration networks in this field. METHODS: Articles published between 2009 and 2018 were extracted from the Science Citation Index Expanded via Web of Science (WoS) using the search term "obstructive sleep apn*". Publication year, number and country of authors, journal, subject category, key words, funding source and number of citations received were recorded. We also conducted network analyses for key words and international collaboration. RESULTS: 12,666 articles on OSA were located, which had increased from 895 documents in 2009 to 1592 in 2018. The progressive growth in scientific production on OSA had outpaced the growth rate of total WoS production since 2012.50% of the articles declared some type of funding, with a citation index higher than manuscripts that were not funded. The manuscripts were distributed in journals from 135 subject categories of the WoS, and keyword distribution showed a dispersed pattern with a high number of nodes. The international collaboration rate was 18.2%, and the country network showed the United States as the hegemonic node. CONCLUSION: World production on OSA has grown at a higher rate than global production and shows notable thematic dispersion as well as a high ability to secure funding, which increases its impact.

Financial inclusion and physical health functioning among aging adults in the sub-Saharan African context: Exploring social networks and gender roles.

BACKGROUND: It remains poorly understood how financial inclusion influences physical health functioning in later life in sub-Saharan African context and whether the association differs by gender and social relationships. We aim 1) to examine the associations of financial inclusion with functional impairment during older age in Ghana; and 2) to evaluate whether gender and social networks modify this association. METHODS: The cross-sectional analyses are based on a sample (N = 1,201) of study participants aged 50 years and over (M = 66.5 years, SD = 11.9, 63.3% female) deriving from the 2016-2017 AgeHeaPsyWel-HeaSeeB Study. Ordinary least squares (OLS) regression analyses with interactions were performed to estimate the link between financial inclusion and functional health and how the association is modified by gender and older age social networks. RESULTS: The mean financial inclusion score was 1.66 (SD = 1.74) in women and 2.33 (SD = 1.82) in men whilst mean activities of daily living (ADL) score was 13.03 (SD = 4.99) and 14.85 (SD = 5.06) in women and men respectively. We found that financial inclusion was associated with decreases in ADL (total sample: ß = -.548, p < .001; women: ß = -.582, p < .001; men: ß = -1.082 p < .001) and instrumental ADL (IADL) (total sample: ß = -.359, p = .034; women: ß = -.445, p = .026 but not in men). Social networks significantly moderated the association of financial inclusion with ADL such that the financially included who were embedded in a stronger constellation of social networks were 6% less likely to report ADL impairment compared to those with weaker social networks (ß = -.062, p = .025). CONCLUSIONS: The study provides empirical evidence for a better understanding of the association between financial inclusion and physical health functioning in the context of later life social networks. Interventions for functional health through financial inclusion in sub-Saharan Africa should include improving interpersonal and social networks for older adult and also through gender lenses.

Diversity-driven knowledge distillation for financial trading using Deep Reinforcement Learning.

Deep Reinforcement Learning (RL) is increasingly used for developing financial trading agents for a wide range of tasks. However, optimizing deep RL agents is notoriously difficult and unstable, especially in noisy financial environments, significantly hindering the performance of trading agents. In this work, we present a novel method that improves the training reliability of DRL trading agents building upon the well-known approach of neural network distillation. In the proposed approach, teacher agents are trained in different subsets of RL environment, thus diversifying the policies they learn. Then student agents are trained using distillation from the trained teachers to guide the training process, allowing for better exploring the solution space, while "mimicking" an existing policy/trading strategy provided by the teacher model. The boost in effectiveness of the proposed method comes from the use of diversified ensembles of teachers trained to perform trading for different currencies. This enables us to transfer the common view regarding the most profitable policy to the student, further improving the training stability in noisy financial environments. In the conducted experiments we find that when applying distillation, constraining the teacher models to be diversified can significantly improve their performance of the final student agents. We demonstrate this by providing an extensive evaluation on various financial trading tasks. Furthermore, we also provide additional experiments in the separate domain of control in games using the Procgen environments in order to demonstrate the generality of the proposed method.

Investor attention and cryptocurrency: Evidence from the Bitcoin market.

This paper adds to the growing literature of cryptocurrency and behavioral finance. Specifically, we investigate the relationships between the novel investor attention and financial characteristics of Bitcoin, i.e., return and realized volatility, which are the two most important characteristics of one certain asset. Our empirical results show supports in the behavior finance area and argue that investor attention is the granger cause to changes in Bitcoin market both in return and realized volatility. Moreover, we make in-depth investigations by exploring the linear and non-linear connections of investor attention on Bitcoin. The results indeed demonstrate that investor attention shows sophisticated impacts on return and realized volatility of Bitcoin. Furthermore, we conduct one basic and several long horizons out-of-sample forecasts to explore the predictive ability of investor attention. The results show that compared with the traditional historical average benchmark model in forecasting technologies, investor attention improves prediction accuracy in Bitcoin return. Finally, we build economic portfolios based on investor attention and argue that investor attention can further generate significant economic values. To sum up, investor attention is a non-negligible pricing factor for Bitcoin asset.

Compound distributions for financial returns.

In this paper, we propose six Student's t based compound distributions where the scale parameter is randomized using functional forms of the half normal, Fréchet, Lomax, Burr III, inverse gamma and generalized gamma distributions. For each of the proposed distribution, we give expressions for the probability density function, cumulative distribution function, moments and characteristic function. GARCH models with innovations taken to follow the compound distributions are fitted to the data using the method of maximum likelihood. For the sample data considered, we see that all but two of the proposed distributions perform better than two popular distributions. Finally, we perform a simulation study to examine the accuracy of the best performing model.

The interdependency structure in the Mexican stock exchange: A network approach.

Our goal in this paper is to study and characterize the interdependency structure of the Mexican Stock Exchange (mainly stocks from Bolsa Mexicana de Valores) for the period 2000-2019 which provide a one shot big-picture panorama. To this end, we estimate correlation/concentration matrices from different models and then compute centralities and modularity from network theory.

Financial Literacy Among Health Professions Graduate Students.

AIMS: The impact of student debt management on mental health, career choices, and advanced training in allied health professions is unknown. The purpose of this project was to pilot a survey that identifies students' financial literacy and self-efficacy. METHODS: A cross-sectional survey containing 43 items related to financial habits, savings knowledge, credit and borrowing strategies, and investment knowledge was administered to assess financial literacy, self-efficacy, and career plans in a group of health professions graduate students. RESULTS: 134 of 268 surveys were completed by a variety of health professions. Financial habits and credit and borrowing categories scored the highest at 50% correct. Students scored the lowest on investment knowledge with an average of 25% correct responses. The overall mean self-efficacy score was 15.5±3.8. Three independent variables had a significant correlation of determination with overall financial literacy, which included marital status, older age, and individuals who identified as white non-Hispanic. Similarly, identification as white non-Hispanic had a significant correlation of determination with financial self-efficacy, but there were no significant differences based on age or marital status. CONCLUSIONS: Allied health students demonstrated low financial literacy and self-efficacy. Health care educators should consider delivering educational content to address these deficits.

Model of Success: World Association for the Advancement of Veterinary Parasitology African Foundation (1997-2019).

No abstract available.

Segmentation of time series in up- and down-trends using the epsilon-tau procedure with application to USD/JPY foreign exchange market data.

We propose the epsilon-tau procedure to determine up- and down-trends in a time series, working as a tool for its segmentation. The method denomination reflects the use of a tolerance level ε for the series values and a patience level τ in the time axis to delimit the trends. We first illustrate the procedure in discrete random walks, deriving the exact probability distributions of trend lengths and trend amplitudes, and then apply it to segment and analyze the trends of U.S. dollar (USD)/Japanese yen (JPY) market time series from 2015 to 2018. Besides studying the statistics of trend lengths and amplitudes, we investigate the internal structure of the trends by grouping trends with similar shapes and selecting clusters of shapes that rarely occur in the randomized data. Particularly, we identify a set of down-trends presenting similar sharp appreciation of the yen that are associated with exceptional events such as the Brexit Referendum in 2016.

Lack of Strategic Funding and Long-Term Job Security Threaten to Have Profound Effects on Cardiovascular Researcher Retention in Australia.

BACKGROUND: Cardiovascular disease is the leading cause of death in Australia. Investment in research solutions has been demonstrated to yield health and a 9.8-fold return economic benefit. The sector, however, is severely challenged with success rates of traditional peer-reviewed funding in decline. Here, we aimed to understand the perceived challenges faced by the cardiovascular workforce in Australia prior to the COVID-19 pandemic. METHODS: We used an online survey distributed across Australian cardiovascular societies/councils, universities and research institutes over a period of 6 months during 2019, with 548 completed responses. Inclusion criteria included being an Australian resident or an Australian citizen who lived overseas, and a current or past student or employee in the field of cardiovascular research. RESULTS: The mean age of respondents was 42±13 years, 47% were male, 85% had a full-time position, and 40% were a group leader or laboratory head. Twenty-three per cent (23%) had permanent employment, and 82% of full-time workers regularly worked >40 hours/week. Sixty-eight per cent (68%) said they had previously considered leaving the cardiovascular research sector. If their position could not be funded in the next few years, a staggering 91% of respondents would leave the sector. Compared to PhD- and age-matched men, women were less likely to be a laboratory head and to feel they had a long-term career path as a cardiovascular researcher, while more women were unsure about future employment and had considered leaving the sector (all p<0.05). Greater job security (76%) and government and philanthropic investment in cardiovascular research (72%) were highlighted by responders as the main changes to current practices that would encourage them to stay. CONCLUSION: Strategic solutions, such as diversification of career pathways and funding sources, and moving from a competitive to a collaborative culture, need to be a priority to decrease reliance on government funding and allow cardiovascular researchers to thrive.

Will colleges survive the storm of declining enrollments? A computational model.

The approaching decline in the U.S. college-age population, sometimes referred to as a "demographic storm," has been identified as an existential threat to the future of American colleges and universities. This article conducts a model-driven analysis of three plausible college-level responses to declining applications. It draws on systems theory to conceptualize a tuition-dependent college as a complex service system and to develop a system dynamics model that captures key causal interrelationships and multiple feedback effects between faculty, facilities, tuition revenue, financials, reputation, and outcomes. Simulations with the college model suggest that common solutions such as reducing faculty or adding campus facilities may improve the college's short-term financial position, but they are insufficient to ensure its long-term viability. This model contributes to the research literature on the economics of higher education, and model-driven academic management and strategy. It also provides useful implications and insights that can inform policy-makers and college leaders.

Predicting Nursing Home Financial Distress Using the Altman Z-Score.

This article uses a modified Altman Z-score to predict financial distress within the nursing home industry. The modified Altman Z-score model uses multiple discriminant analysis (MDA) to examine multiple financial ratios simultaneously to assess a firm's financial distress. This study utilized data from Medicare Cost Reports, LTCFocus, and the Area Resource File. Our sample consisted of 167 268 nursing home-year observations, or an average of 10 454 facilities per year, in the United States from 2000 through 2015. The independent financial variables, liquidity, profitability, efficiency, and net worth were entered stepwise into the MDA model. All of the financial variables, with the exception of net worth, significantly contributed to the discriminating power of the model. K-means clustering was used to classify the latent variable into 3 categorical groups: distressed, risk-of-financial distress, and healthy. These findings will provide policy makers and practitioners another tool to identify nursing homes that are at risk of financial distress.

Rural Hospital Mergers Increased Between 2005 and 2016-What Did Those Hospitals Look Like?

The objective of this study is to determine whether key hospital-level financial and market characteristics are associated with whether rural hospitals merge. Hospital merger status was derived from proprietary Irving Levin Associates data for 2005 through 2016 and hospital-level characteristics from HCRIS, CMS Impact File Hospital Inpatient Prospective Payment System, Hospital MSA file, AHRF, and U.S. Census data for 2004 through 2016. A discrete-time hazard analysis using generalized estimating equations was used to determine whether factors were associated with merging between 2005 and 2016. Factors included measures of profitability, operational efficiency, capital structure, utilization, and market competitiveness. Between 2005 and 2016, 11% (n = 326) of rural hospitals were involved in at least one merger. Rural hospital mergers have increased in recent years, with more than two-thirds (n = 261) occurring after 2011. The types of rural hospitals that merged during the sample period differed from nonmerged rural hospitals. Rural hospitals with higher odds of merging were less profitable, for-profit, larger, and were less likely to be able to cover current debt. Additional factors associated with higher odds of merging were reporting older plant age, not providing obstetrics, being closer to the nearest large hospital, and not being in the West region. By quantifying the hazard of characteristics associated with whether rural hospitals merged between 2005 and 2016, these findings suggest it is possible to determine leading indicators of rural mergers. This work may serve as a foundation for future research to determine the impact of mergers on rural hospitals.