Sovereign Credit Default Swap and Stock Markets in Central and Eastern European Countries: Are Feedback Effects at Work?

The purpose of the paper is to investigate the relationship between sovereign Credit Default Swap (CDS) and stock markets in nine emerging economies from Central and Eastern Europe (CEE), using daily data over the period January 2008-April 2018. The analysis deploys a Vector Autoregressive model, focusing on the direction of Granger causality between the credit and stock markets. We find evidence of the presence of bidirectional feedback between sovereign CDS and stock markets in CEE countries. The results highlight a transfer entropy of risk from the private to public sector over the whole period and respectively, from the public to private transfer entropy of risk during the European sovereign debt crisis only in Romania and Slovenia. Another finding that deserves particular attention is that the linkage between the CDS spreads and stock markets is time-varying and subject to regime shifts, depending on global financial conditions, such as the sovereign debt crisis. By providing insights on the inter-temporal causality of the comovements of the CDS-stock markets, the paper has significant practical implications for risk management practices and regulatory policies, under different market conditions of European emerging economies.

Bayesian vector autoregressive model for multi-subject effective connectivity inference using multi-modal neuroimaging data.

In this article a multi-subject vector autoregressive (VAR) modeling approach was proposed for inference on effective connectivity based on resting-state functional MRI data. Their framework uses a Bayesian variable selection approach to allow for simultaneous inference on effective connectivity at both the subject- and group-level. Furthermore, it accounts for multi-modal data by integrating structural imaging information into the prior model, encouraging effective connectivity between structurally connected regions. They demonstrated through simulation studies that their approach resulted in improved inference on effective connectivity at both the subject- and group-level, compared with currently used methods. It was concluded by illustrating the method on temporal lobe epilepsy data, where resting-state functional MRI and structural MRI were used. Hum Brain Mapp 38:1311-1332, 2017. © 2016 Wiley Periodicals, Inc.

Revealing the dynamic network structure of the Beck Depression Inventory-II.

BACKGROUND: Structured interviews and questionnaires are important tools to screen for major depressive disorder. Recent research suggests that, in addition to studying the mean level of total scores, researchers should focus on the dynamic relations among depressive symptoms as they unfold over time. Using network analysis, this paper is the first to investigate these patterns of short-term (i.e. session to session) dynamics for a widely used psychological questionnaire for depression - the Beck Depression Inventory (BDI-II). METHOD: With the newly developed vector autoregressive (VAR) multilevel method we estimated the network of symptom dynamics that characterizes the BDI-II, based on repeated administrations of the questionnaire to a group of depressed individuals who participated in a treatment study of an average of 14 weekly assessments. Also the centrality of symptoms and the community structure of the network were examined. RESULTS: The analysis showed that all BDI-II symptoms are directly or indirectly connected through patterns of temporal influence. In addition, these influences are mutually reinforcing, 'loss of pleasure' being the most central item in the network. Community analyses indicated that the dynamic structure of the BDI-II involves two clusters, which is consistent with earlier psychometric analyses. CONCLUSION: The network approach expands the range of depression research, making it possible to investigate the dynamic architecture of depression and opening up a whole new range of questions and analyses. Regarding clinical practice, network analyses may be used to indicate which symptoms should be targeted, and in this sense may help in setting up treatment strategies.

Capturing the risk of persisting depressive symptoms: A dynamic network investigation of patients' daily symptom experiences.

What drives the large differences across patients in terms of treatment efficacy of major depressive disorder (MDD) is unclear. A network approach to psychopathology may help to reveal underlying mechanisms determining patients' capacity for recovery. We used daily diary MDD symptom data and six-month follow-up data on depression to examine how dynamic associations between symptoms relate to the future course of MDD. Daily experiences of depressive symptoms of 69 participants were assessed by means of the SCL-90-R depression subscale, three days a week for a period of six weeks, as part of a larger intervention study. Multilevel vector autoregressive modelling was used to estimate networks of dynamic symptom connections. Long-term outcome was determined by the percentage change in Hamilton Depression Rating Scale (HDRS) score between pre-intervention and six-month follow-up. For patients with more persisting symptoms, the symptom 'feeling everything is an effort' most strongly predicted other symptoms. The networks of the two groups did not significantly differ in overall connectivity. Findings suggest that future research should not solely focus on the presence or intensity of individual symptoms when predicting long-term outcomes, but should also examine the role of a specific symptom in the larger network of dynamic symptom-to-symptom interactions.

Computerized diagnosis of respiratory disorders. SVM based classification of VAR model parameters of respiratory sounds.

INTRODUCTION: This article is part of the Focus Theme of Methods of Information in Medicine on "Biosignal Interpretation: Advanced METHODS for Studying Cardiovascular and Respiratory Systems". OBJECTIVES: This work proposes an algorithm for diagnostic classification of multi-channel respiratory sounds. METHODS: 14-channel respiratory sounds are modeled assuming a 250-point second order vector autoregressive (VAR) process, and the estimated model parameters are used to feed a support vector machine (SVM) classifier. Both a three-class classifier (healthy, bronchiectasis and interstitial pulmonary disease) and a binary classifier (healthy versus pathological) are considered. RESULTS: In the binary scheme, the sensitivity and specificity for both classes are 85% ± 8.2%. In the three-class classification scheme, the healthy recall (95% ± 5%) and the interstitial pulmonary disease recall and precision (100% ± 0% both) are rather high. However, bronchiectasis recall is very low (30% ± 15.3%), resulting in poor healthy and bronchiectasis precision rates (76% ± 8.7% and 75% ± 25%, respectively). The main reason behind these poor rates is that the bronchiectasis is confused with the healthy case. CONCLUSIONS: The proposed method is promising, nevertheless, it should be improved such that other mathematical models, additional features, and/or other classifiers are to be experimented in future studies.

Dynamic compositional modeling of pedestrian crash counts on urban roads in Connecticut.

Uncovering the temporal trend in crash counts provides a good understanding of the context for pedestrian safety. With a rareness of pedestrian crashes it is impossible to investigate monthly temporal effects with an individual segment/intersection level data, thus the time dependence should be derived from the aggregated level data. Most previous studies have used annual data to investigate the differences in pedestrian crashes between different regions or countries in a given year, and/or to look at time trends of fatal pedestrian injuries annually. Use of annual data unfortunately does not provide sufficient information on patterns in time trends or seasonal effects. This paper describes statistical methods uncovering patterns in monthly pedestrian crashes aggregated on urban roads in Connecticut from January 1995 to December 2009. We investigate the temporal behavior of injury severity levels, including fatal (K), severe injury (A), evident minor injury (B), and non-evident possible injury and property damage only (C and O), as proportions of all pedestrian crashes in each month, taking into consideration effects of time trend, seasonal variations and VMT (vehicle miles traveled). This type of dependent multivariate data is characterized by positive components which sum to one, and occurs in several applications in science and engineering. We describe a dynamic framework with vector autoregressions (VAR) for modeling and predicting compositional time series. Combining these predictions with predictions from a univariate statistical model for total crash counts will then enable us to predict pedestrian crash counts with the different injury severity levels. We compare these predictions with those obtained from fitting separate univariate models to time series of crash counts at each injury severity level. We also show that the dynamic models perform better than the corresponding static models. We implement the Integrated Nested Laplace Approximation (INLA) approach to enable fast Bayesian posterior computation. Taking CO injury severity level as a baseline for the compositional analysis, we conclude that there was a noticeable shift in the proportion of pedestrian crashes from injury severity A to B, while the increase for injury severity K was extremely small over time. This shift to the less severe injury level (from A to B) suggests that the overall safety on urban roads in Connecticut is improving. In January and February, there was some increase in the proportions for levels A and B over the baseline, indicating a seasonal effect. We found evidence that an increase in VMT would result in a decrease of proportions over the baseline for all injury severity levels. Our dynamic model uncovered a decreasing trend in all pedestrian crash counts before April 2005, followed by a noticeable increase and a flattening out until the end of the fitting period. This appears to be largely due to the behavior of injury severity level A pedestrian crashes.