Skewed multifractal scaling of stock markets during the COVID-19 pandemic.

This article proposes a new paradigm of asymmetric multifractality in financial time series, where the scaling feature varies over two adjacent intervals. The proposed approach first locates a change-point and then performs a multifractal detrended fluctuation analysis (MF-DFA) on each interval. The study investigates the impact of the COVID-19 pandemic on asymmetric multifractal scaling by analyzing financial indices of the G3+1 nations, including the world's four largest economies, from January 2018 to November 2021. The results show common periods of local scaling with increasing multifractality after a change-point at the beginning of 2020 for the US, Japanese, and Eurozone markets. The study also identifies a significant transition in the Chinese market from a turbulent multifractal state to a stable monofractal state. Overall, this new approach provides valuable insights into the characteristics of financial time series and their response to extreme events.

Perceived discrimination based on the symptoms of covid-19, mental health, and emotional responses-the international online COVISTRESS survey.

BACKGROUND: Despite the potential detrimental consequences for individuals' health and discrimination from covid-19 symptoms, the outcomes have received little attention. This study examines the relationships between having personally experienced discrimination based on the symptoms of covid-19 (during the first wave of the pandemic), mental health, and emotional responses (anger and sadness). It was predicted that covid-19 discrimination would be positively related to poor mental health and that this relationship would be mediated by the emotions of anger and sadness. METHODS: The study was conducted using an online questionnaire from January to June 2020 (the Covistress network; including 44 countries). Participants were extracted from the COVISTRESS database (Ntotal = 280) with about a half declaring having been discriminated due to covid-19 symptoms (N = 135). Discriminated participants were compared to non-discriminated participants using ANOVA. A mediation analysis was conducted to examine the indirect effect of emotional responses and the relationships between perceived discrimination and self-reported mental health. RESULTS: The results indicated that individuals who experienced discrimination based on the symptoms of covid-19 had poorer mental health and experienced more anger and sadness. The relationship between covid-19 personal discrimination and mental health disappeared when the emotions of anger and sadness were statistically controlled for. The indirect effects for both anger and sadness were statistically significant. DISCUSSION: This study suggests that the covid-19 pandemic may have generated discriminatory behaviors toward those suspected of having symptoms and that this is related to poorer mental health via anger and sadness.

Geopolitical risk and the Saudi stock market: Evidence from a new wavelet packet multiresolution cross-causality

Recently, due to OPEC Plus cutting oil production, geopolitical tensions have escalated between the United States and Saudi Arabia. We explore the relationship between geopolitical risk and the Saudi stock market, developing a deep cross-causality approach based on wavelet methodology. Our sample includes Brexit, COVID-19, and the Russian-Ukrainian war. We identify causal patterns especially during times of crisis, evidencing one-way causality of geopolitical factors impacting the Saudi market. Scholars and policymakers will be interested in the sensitivity of the Saudi market to geopolitical risk.

Multiresolutional statistical machine learning for testing interdependence of power markets: A Variational Mode Decomposition-based approach

In the increasingly interconnected and digitized world, the field of electricity price forecasting has benefited from growing research especially due to the market liberalization and the connectedness between electrical systems. This study defines a novel multiscaled forecasting model based upon the Variational Mode Decomposition (VMD) to quantify multiscaled cross-correlation between two important European markets during COVID-19 pandemic. The VMD is known to be a strong information processing tool which is localized in both frequency and time, and is especially used for capturing nonstationary and nonlinear behaviors of time series. The set of new VMD techniques is applied on hourly electricity spot prices from the Nord Pool and MIBEL energy exchanges for the period ranging from January 2019 to March 2020. The sampled time series include a period of specific recession in the financial system, coinciding with the Brexit and COVID-19 event, which was accompanied by a significant collapse in the world’s economic sphere. The empirical results reveal a significant dependence between electricity markets across long- and medium-run investment time horizons, with evidence for dynamic lead–lag relationships at some frequency sub-bands. However, over the short-term (daily and intra-daily intervals), we notice a kind of independence between markets, especially in times of crisis, which offers investors different investment diversification opportunities. On the other hand, the accuracy of generated forecasts prove the interest of a conjoint modeling and the reliability of this new tool, in particular when the approach is adequately coupled with feedforward neural networks.

Causality of geopolitical risk on food prices: Considering the Russo–Ukrainian conflict

As the Russo–Ukrainian conflict obstructs the vast wheat production of Ukraine, we investigate the relationship over crises between geopolitical risk and prices of essential food commodities. We use multiresolution analysis to identify patterns concealed by high noise levels inherent with commodity prices during crises. Our sample includes such important periods as Brexit, COVID-19, and the current Russo–Ukrainian conflict. Results evidence a one-way causal relationship, with geopolitical factors significantly affecting food prices. Scholars interested in global development, as well as policy makers and international aid organizations will be especially interested in understanding the sensitivity of food prices to geopolitical risk.

Multiscaled causality of infections on viral testing volumes: The case of COVID-19 in Tunisia.

OBJECTIVES: Coronavirus disease (COVID-19) is one of the most detrimental pandemics that affected the humanity throughout the ages. The irregular historical progression of the virus over the first year of the pandemic was accompanied with far-reaching health and social damages. To prepare logistically against this worsening disaster, many public authorities around the world had set up screening and forecasting studies. This article aims to analyse the time-frequency co-evolution of the number of confirmed cases (NCC) in Tunisia and the related number of performed polymerase chain reaction (PCR) tests over the COVID-19 first year. Accurately predicting such a relationship allows Tunisian authorities to set up an effective health prevention plan. STUDY DESIGN: In order to keep pace with the speed of evolution of the virus, we used uninterrupted daily time series from the Tunisian Ministry of Public Health (TMPH) recorded over the COVID-19 first year. The objective is to: (1) analyse the time-frequency progress of the NCC in relationship with the number of PCR tests, (2) identify a multi-scale two-factor stochastic model in order to develop a robust bivariate forecasting technique. METHODS: We assume a bivariate stochastic process which is projected onto a set of wavelet sub-spaces to investigate the scale-by-scale co-evolvement the NCC/PCR over the COVID-19 first year. A wavelet-based multiresolutional causality test is then performed. RESULTS: The main results recommend the rejection of the null hypothesis of no instantaneous causality in both directions, while the statistics of the Granger test suggest failing to reject the null hypothesis of non-causality. However, by proceeding scale-by-scale, the Granger causality is proven significant in both directions over varying frequency bands. CONCLUSIONS: It is important to include the NCC and PCR variables in any time series model intended to predict one of these variables. Such a bivariate and multi-scale model is supposed to better predict the needs of the public health sector in screening tests. On this basis, testing campaigns with multiple periodicities can be planned by the Tunisian authorities.

The major worldwide stress of healthcare professionals during the first wave of the COVID-19 pandemic - the international COVISTRESS survey.

INTRODUCTION: The COVID-19 pandemic has initiated an upheaval in society and has been the cause of considerable stress during this period. Healthcare professionals have been on the front line during this health crisis, particularly paramedical staff. The aim of this study was to assess the high level of stress of healthcare workers during the first wave of the pandemic. MATERIALS AND METHODS: The COVISTRESS international study is a questionnaire disseminated online collecting demographic and stress-related data over the globe, during the pandemic. Stress levels were evaluated using non-calibrated visual analog scale, from 0 (no stress) to 100 (maximal stress). RESULTS: Among the 13,537 individuals from 44 countries who completed the survey from January to June 2020, we included 10,051 workers (including 1379 healthcare workers, 631 medical doctors and 748 paramedical staff). The stress levels during the first wave of the pandemic were 57.8 ± 33 in the whole cohort, 65.3 ± 29.1 in medical doctors, and 73.6 ± 27.7 in paramedical staff. Healthcare professionals and especially paramedical staff had the highest levels of stress (p < 0.001 vs non-healthcare workers). Across all occupational categories, women had systematically significantly higher levels of work-related stress than men (p < 0.001). There was a negative correlation between age and stress level (r = -0.098, p < 0.001). Healthcare professionals demonstrated an increased risk of very-high stress levels (>80) compared to other workers (OR = 2.13, 95% CI 1.87-2.41). Paramedical staff risk for very-high levels of stress was higher than doctors' (1.88, 1.50-2.34). The risk of high levels of stress also increased in women (1.83, 1.61-2.09; p < 0.001 vs. men) and in people aged <50 (1.45, 1.26-1.66; p < 0.001 vs. aged >50). CONCLUSIONS: The first wave of the pandemic was a major stressful event for healthcare workers, especially paramedical staff. Among individuals, women were the most at risk while age was a protective factor.