Bayesian semiparametric multivariate realized GARCH modeling

This paper introduces a novel Bayesian semiparametric multivariate GARCH framework for modeling re- turns and realized covariance, as well as approximating their joint unknown conditional density. We extend existing parametric multivariate realized GARCH models by incorporating a Dirichlet Process mixture of countably infinite normal distributions for returns and (inverse-)Wishart distributions for realized covariance. This approach captures time-varying dynamics in higher-order conditional moments of both returns and realized covariance. Our new class of models demonstrates superior out-of-sample forecasting performance, providing significantly improved multiperiod density forecasts for returns and realized covariance, and competitive covariance point forecasts

The impact of social mood on financial markets

This thesis presents three empirical papers that explore the extent of UK social mood on UK stock indexes. The first paper utilises mobile, broadband, landline and pay TV complaints as an extra factor in an augmented-CAPM setting. Using time series OLS, this study finds empirical evidence that people who complain about actual or perceived mobile or broadband service failure experience catharsis, as increased mobile and broadband complaints lead to increased excess returns for smaller indexes and reduced excess returns for larger indexes. By contrast, results show that people who complain about landline and pay TV experience frustration, as increased landline and broadband complaints lead to increased excess returns for larger indexes and lowered excess returns for smaller indexes. In the second paper, wine, beer, spirits and cider receipts are used in Granger causality tests, VARs, and then impulse response tests. Using time series, this paper finds empirical evidence of mood enhancement through alcohol consumption – this is because an increase in wine, beer, spirits or cider consumption precedes a lowering of FTSE trading volume, which is consistent with the Mood Maintenance Hypothesis. Furthermore, an increase in beer consumption leads to an increase in smaller company index returns, which is consistent with the sentiment literature. Contrary to the existing literature, impulse response test results indicate that FTSE returns, or changes in trading volumes, have an insignificant impact on UK social mood measured by alcohol. The final paper makes use of Google searches for music genre to develop a novel Music Search Index (Music Index). Using The Music Index level and its rate of change on daily and monthly data, this paper finds evidence of mood affecting the FTSE through mood management. Mood management works by impacting peoples’ search for music (cognitivism) in order to find music that will extend or modify their current mood (emotivism)

NHS Mental Healthcare Staff Experiences of Working with Service-Users Displaying Hoarding Behaviours – A Thematic Analysis

Background: Hoarding Disorder (HD) became its own clinical entity in 2013 and, since then, it has gained more research attention. Evidence suggests that professionals responding to the complex needs of service-users displaying hoarding behaviour lack relevant expertise and highlight hoarding as notoriously difficult to treat. Multi-agency approaches are becoming increasingly popular in the management of hoarding; however, little is known about the treatment of hoarding in UK-based National Health Service (NHS) mental healthcare services. Aim: The aim of the current study was to qualitatively explore NHS mental healthcare staff experiences of working with adult service-users across the lifespan displaying hoarding behaviours. This was to gain a greater understanding of the condition, and to explore how staff respond to the needs of service-users within the context of the NHS. Method: Fifteen mental healthcare staff were recruited from six NHS Trusts in England. Semi-structured interviews were conducted, and the six steps of Reflexive Thematic Analysis were followed. Results: Five themes and fifteen subthemes were identified: (1) How staff understand hoarding behaviour: “The stuff is rarely the issue”; (2) Staff frustrations, challenges and systemic constraints; (3) Treatment approaches for hoarding; (4) Updating practice: Seeing hoarding as a diagnosis; (5) Service-users’ experiences of help. Conclusion: The results of this study highlight how mental healthcare staff attempt to understand hoarding by considering the numerous contributing factors associated with its onset and maintenance. There was ambiguity amongst staff regarding appropriate treatment for this population; however, adopting multi-agency approaches was seen to support service- users’ needs effectively. Staff reflect on the complexities of undertaking this work and consider the impact this has upon service-users and accessing help. Difficulties relating to staff role, service constraints and the lack of staff training are explored. Clinical and policy implications, including the development of best practice guidelines are discussed. Recommendations for future research are proposed

Are Foreign Firms Good for the Environment? FDI and Protected Areas

Despite the coexistence of three trends—increased economic integration, a dramatic reduction in biodiversity, and the implementation of national policies to reduce extinction risks—we know little about how foreign investment affects biodiversity. This paper focuses on the incentives that foreign direct investment (FDI) poses on governments’ foremost strategy to protect biodiversity: the establishment of protected areas. Protected areas have expanded in most countries at rates that are not explained merely by geography or environmental reasons. We argue that FDI is associated with the expansion of protected areas through two channels. First, multinational corporations can obtain reputational benefits from host countries’ commitment to protect biodiversity. Second, protected areas impose different costs on existing and prospective FDI, and rarely entail expropriation of foreign investment. This potentially shields foreign owned firms from domestic or international competition for the use of comparable resources. Statistical analyses on a sample of 60 developed and developing countries between 1984 and 2020 strongly support our expectations. Our findings shed new light on globalization’s non-economic implications and add to our understanding about how international factors influence the provision of public goods

An ecological momentary assessment study assessing repetitive negative thinking as a predictor for psychopathology

Repetitive negative thinking (RNT), an important transdiagnostic process, is commonly assessed using trait questionnaires. While these instruments ask respondents to estimate their general tendency towards RNT, ecological momentary assessment (EMA) allows to assess how much individuals actually engage in RNT in their daily lives. In a sample of N = 1,176 adolescents and young adults, we investigated whether average levels of RNT assessed via EMA predicted psychopathological symptoms. Adjusting for trait RNT measures and baseline scores on outcome measures, we found that average levels of RNT assessed via EMA significantly predicted higher depressive and anxiety symptoms as well as lower mental well-being at baseline, one-, three-, and twelve-month follow-up. Exploratory analyses of the association between temporal dynamics of RNT (e.g., RNT inertia) and psychopathological symptoms yielded inconsistent results. The high predictive power of average scores on the EMA-based RNT measure suggests that EMA is a promising tool for assessing RNT

Assessing the Risk of Low Energy Availability, Bone Mineral Density and Psychological Strain in Endurance Athletes

Background: Adequate energy intake is crucial for athletic performance and recovery. However, many endurance athletes experience Low Energy Availability (LEA), which, if prolonged, can detrimentally impact both health and performance. Methods: A total of 55 endurance athletes (23 females; 45 ± 13 years, 1.64 ± 0.06 m, 64.4 ± 11.4 kg and 32 males; 44 ± 13 years, 1.76 ± 0.18 m, 78.8 ± 9.2 kg) underwent physical assessments and completed questionnaires on dietary habits, training loads, and psychological stress. Dual-Energy X-ray Absorptiometry (DEXA) scans measured bone mineral density (BMD) in the lumbar L1-L4 spine, and body composition. Risk of LEA burnout, and psychological strain were assessed using sport-specific questionnaires. Results: Seventy-seven percent of female athletes were identified as at risk of LEA by the LEAF-Q. These females had higher body weight and fat percentage than those at low risk of LEA. Male athletes had a higher prevalence of low lumbar BMD (31%) compared to females, associated with older age, and longer training histories. Although only 9% of female athletes had low-BMD, those affected had a history of amenorrhea and were identified as at risk of LEA by the LEAF-Q. Conclusion: A high proportion of endurance athletes had low-BMD and were at risk of LEA. This underscores the need for targeted nutritional strategies to mitigate the risks associated with LEA and promote overall athlete well-being

Optimal reinsurance with multivariate risks and dependence uncertainty

In this paper, we study the optimal reinsurance design from the perspective of an insurer with multiple lines of business, where the reinsurance is purchased by the insurer for each line of business respectively. For the risk vector generated by the multiple lines of business, we suppose that the marginal distributions are fixed, but the dependence structure between these risks is unknown. Due to the unknown dependence structure, the optimal strategy is investigated for the worst-case scenario. We consider two types of risk measures: Value-at-Risk (VaR) and Range-Value-at-Risk (RVaR) including Expected Shortfall (ES) as a special case, and general premium principles satisfying certain conditions. To be more practical, the minimization of the total risk is conducted under some budget constraint. For the VaR-based model with only two risks, it turns out that the limited stop-loss reinsurance treaty is optimal for each line of business. For the model with more than two risks, we obtain two types of optimal reinsurance strategies if the marginals have convex or concave distributions on their tail parts by constraining the ceded loss functions to be convex or concave. Moreover, as a special case, the optimal quota-share reinsurance with dependence uncertainty has been studied. Finally, after applying our findings to two risks, some studies have been implemented to obtain both the analytical and numerical optimal reinsurance policies

Engine Bearing Analysis Under Diverse Conditions via Response Surface Methodology

With the increasing demand for reliable diagnostics in automotive components, accurately assessing engine bearing health under various conditions has become crucial. This study presents a robust methodology for identifying and distinguishing faults in internal combustion engine bearings. Specifically, the focus is on differentiating between healthy and faulty bearings by analyzing key predictive variables—temperature, engine speed, and humidity—and their effects on bearing vibration responses. Experiments were conducted at different levels using a Design of Experiments (DOE) framework, providing valuable insights into the nonlinear impacts of each factor on the vibration response of the engine bearing system. Root Mean Square (RMS) vibration data were analyzed using Analysis of Variance (ANOVA) to assess the significance of the model terms, indicating that engine rotation speed has the most significant effect on bearing vibrations, while environmental humidity exhibited the least significant effect. The rate of increase in RMS vibration was higher at sub-zero temperatures for healthy bearings and greater above 30°C, for faulty bearings. Additionally, interactions among all three predictors were found to be insignificant, demonstrating that each parameter independently influenced the vibration response. This combined approach—integrating DOE and Response Surface Methodology (RSM)—shows promising potential for predicting the dynamic behavior of main journal bearings in internal combustion engines

Deep Multimodal Imitation Learning-Based Framework for Robot-Assisted Medical Examination

Medical ultrasound examination is a challenging dexterous manipulation task for robots. Even for experienced sonographers, it involves real-time decision-making, motion control, and force regulation based on ultrasound images and patient feedback. In this article, we propose a unified framework for robot-assisted medical examination, specifically for the initial registration in artery scanning, by leveraging deep multimodal imitation learning, compliant control, and trajectory optimization. To process multimodal inputs during the initial registration phase, we investigate a deep imitation learning model that fuses RGB and ultrasound images, contact force, and proprioceptive data. The deep imitation learning model predicts the desired motion and contact force. We design a compliant controller in Cartesian space to track the desired trajectory and force. To smooth the trajectory and ensure safety, we employ a trajectory optimization planner between the deep imitation learning module and the low-level compliant controller. The generalization capability of the deep multimodal imitation learning module, control performance, and the quality of the acquired ultrasound images on both the Phantom and human subjects were evaluated. Experimental results show that the proposed approach significantly improves the success rate of autonomous ultrasound scanning from 75% to 90%, while also reducing the completion time

Attention mechanism based multimodal feature fusion network for human action recognition

Current human action recognition (HAR) methods focus on integrating multiple data modalities, such as skeleton data and RGB data. However, they struggle to exploit motion correlation information in skeleton data and rely on spatial representations from RGB modalities. This paper proposes a novel Attention-based Multimodal Feature Integration Network (AMFI-Net) designed to enhance modal fusion and improve recognition accuracy. First, RGB and skeleton data undergo multi-level preprocessing to obtain differential movement representations, which are then input into a heterogeneous network for separate multimodal feature extraction. Next, an adaptive fusion strategy is employed to enhance the integration of these multimodal features. Finally, the network assesses the confidence level of weighted skeleton information to determine the extent and type of appearance information to be used in the final feature integration. Experiments conducted on the NTU-RGB + D dataset demonstrate that the proposed method is feasible, leading to significant improvements in human action recognition accuracy