Detection of Multiple Structural Breaks in Large Covariance Matrices

This article studies multiple structural breaks in large contemporaneous covariance matrices of high-dimensional time series satisfying an approximate factor model. The breaks in the second-order moment structure of the common components are due to sudden changes in either factor loadings or covariance of latent factors, requiring appropriate transformation of the factor models to facilitate estimation of the (transformed) common factors and factor loadings via the classical principal component analysis. With the estimated factors and idiosyncratic errors, an easy-to-implement CUSUM-based detection technique is introduced to consistently estimate the location and number of breaks and correctly identify whether they originate in the common or idiosyncratic error components. The algorithms of Wild Binary Segmentation for Covariance (WBS-Cov) and Wild Sparsified Binary Segmentation for Covariance (WSBS-Cov) are used to estimate breaks in the common and idiosyncratic error components, respectively. Under some technical conditions, the asymptotic properties of the proposed methodology are derived with near-optimal rates (up to a logarithmic factor) achieved for the estimated breaks. Monte Carlo simulation studies are conducted to examine the finite-sample performance of the developed method and its comparison with other existing approaches. We finally apply our method to study the contemporaneous covariance structure of daily returns of S&P 500 constituents and identify a few breaks including those occurring during the 2007–2008 financial crisis and the recent coronavirus (COVID-19) outbreak. An package "” is provided to implement the proposed algorithms.

A comparison of methods for forecasting value at risk and expected shortfall of cryptocurrencies

Several procedures to forecast daily risk measures in cryptocurrency markets have been recently implemented in the literature. Among them, long‐memory processes, procedures taking into account the presence of extreme observations, procedures that include more than a single regime, and quantile regression‐based models have performed substantially better than standard methods in terms of forecasting risk measures. Those procedures are revisited in this paper, and their value at risk and expected shortfall forecasting performance are evaluated using recent Bitcoin and Ethereum data that include periods of turbulence due to the COVID‐19 pandemic, the third halving of Bitcoin, and the Lexia class action. Additionally, in order to mitigate the influence of model misspecification and enhance the forecasting performance obtained by individual models, we evaluate the use of several forecast combining strategies. Our results, based on a comprehensive backtesting exercise, reveal that, for Bitcoin, there is no single procedure outperforming all other models, but for Ethereum, there is evidence showing that the GAS model is a suitable alternative for forecasting both risk measures. We found that the combining methods were not able to outperform the better of the individual models.

Exploring proven strategies that enable remote workers to take active breaks during their workday

Remote work has been gaining in popularity for years, even before the COVID-19 pandemic began. Along with its perceived benefits, remote work often results in individuals spending long hours at a computer or on the phone. Consequently, remote workers may find that a large portion of their day is spent sitting without taking any kind of break, especially those for physical activity. The purpose of this action research study was to explore proven strategies that enable remote workers to take active breaks during their workday. Data was collected from longtime remote workers during Cycle 1 research through 11 semi-structured interviews and document analyses. Data analysis led to 12 themes that responded to the research questions. Along with the literature and a focus group, these findings informed the action step, which was designed, executed, and evaluated in Cycle 2. The action step involved four longtime remote workers sharing their lived experiences around remote work, breaks, and activity through a podcast series. These podcasts were consumed by 16 new remote workers who answered qualitative survey questions to determine the impact of the podcasts on their break taking during their workdays. The research found that a remote worker's work environment, degree of autonomy, and break options influence how they fit in breaks during their workdays. The findings suggest that remote workers need consistent organizational support;that having autonomy to manage their workdays is critical for remote workers;and all breaks "are not created equal". (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Cointegration between high base metals prices and backwardation: Getting ready for the metals super-cycle

This paper is devoted to test agents' behavior in the markets of hard commodities by trying to distinguish between managing future price structures to hedge their positions and speculating in on prices. We do a triple analysis: cointegration on the time series, structural breaks over the full time series and panel data. The analysis of the full series and the identification of structural breaks allows us to discover the connection between high prices and the negative futures price structure (backwardation) in rising prices scenarios of tin, copper, aluminium, and zinc. Moreover, we obtain that the base metals full matrix (price and futures price structure) is cointegrated in our analysis that uses panel data methods. We believe that these results are important for agents in the markets, as commodity traders or brokers, to maximize profits in their hedging positions.

How Effective are Policy Interventions Against the COVID-19 Infection Rates?

Studies on the COVID-19 pandemic are more likely to concentrate on the effects of the virus while ignoring its timeseries characteristics, particularly its stationarity characteristics. Thus, this study attempts to investigate the effectiveness of policy interventions against COVID-19 by determining the permanent or transitory effects in 5 major regions and the ten most infected countries. Using the endogenous multiple breaks unit root tests introduced by Kapetanios (2005), the findings indicate that only the impacts of shocks to COVID-19 infection rates in France are likely to be permanent. However, the transitory effect is found in Brazil, Germany, Iran, Italy, Russia, Spain, Turkey, the United Kingdom, and the United States. The country where the shock has a permanent impact is suitable for policy interventions, including lockdowns, social isolation, and local isolation. While herd immunity, which protects the entire population against COVID-19, is better ideal for application in countries that experience shocks with a transitory effect.

U.S. House Prices by Census Division: Persistence, Trends and Structural Breaks

This paper uses fractional integration methods to examine persistence, trends and structural breaks in United States house prices, more specifically the monthly Federal Housing Finance Agency House Price Index for census divisions, and the United States as a whole over the period from January 1991 to August 2022. The full sample estimates imply that the order of integration of the series is above one in all cases, and is particularly high for the aggregate series, implying high levels of persistence. However, when the possibility of structural breaks is taken into account, segmented trends are detected. The subsample estimates of the fractional differencing parameter tend to be lower, with mean reversion occurring in a number of cases. This means that shocks in the series are expected to be transitory in these subsamples, disappearing in the long run by themselves. In addition, the time trend coefficient is at its highest in the last subsample, which in most cases starts around May 2020 coincident with the beginning of the coronavirus pandemic. The results provide clear evidence of differences between census divisions, which implies that appropriate housing policies should be designed at the local (rather than at the federal) level. © 2023, The Author(s).

Effects of COVID-19 on the relationship between inflation and REITs returns in South Africa

PurposeThis research aims to ascertain the extent to which the coronavirus disease 2019 (COVID-19) epidemic affected the relationship between inflation and real estate investment trusts (REITs) returns in South Africa.Design/methodology/approachThis research used the Johansen cointegration test and effective test in establishing if there is a long-run cointegrating equation between the variables. To ascertain if COVID-19 resulted in a different relationship regime between inflation and REITs returns, the sequential Bai-Perron method was used.FindingsBetween December 2013 and July 2022, there was no evidence of a long-run relationship between inflation and REITs returns, and a restricted vector autoregressive (VAR) model with a period lag for each variable best describing the relationship. Using the sequential Bai-Perron method, for one break, the results show February 2020 as a structural break in the relationship. A cointegrating equation is also found for the period before the structural break and another after the break. Interestingly, the relationship is negative before the break and a new positive relationship (regime) is confirmed after the noted break.Practical implicationsThis research helps REITs stakeholders to position themselves in light of any changes to macroeconomic activity within South Africa.Originality/valueThis is one of the first studies to test inflation relationship with REITs returns in South Africa and the effects of COVID-19 thereof. This research helps REITs stakeholders to position themselves in light of any changes to macroeconomic activity within South Africa.

Lessons from Animal Culling during Human Pandemics: Is Vaccination a Viable Option for Animals?

Animal reservoirs for respiratory and coronavirus have been major health concerns. Zo-onosis due to coronavirus involves bats, civet cat, camels, pangolins and now the minks. In the same vein, influenza pandemics occur when a new strain of the influenza virus is transmitted to humans from another animal species. Species thought to be of particular importance in the emer-gence of new human influenza strains are swine and poultry and these hosts are often culled during epidemics or pandemics. It is often too easy for humans to forget that millions of animals can die or be slaughtered in human pandemics, including the recent cull of minks in Europe and chickens in Asia. To co-exist with nature in a sustainable way, we must respect our animals by ensuring their welfare and immunizing them against pathogens where possible. Zoonotic diseases are here to stay and will continue to cause major epidemics and pandemics. The other side of the coin is that reverse zoonosis can also have devastating effects on animal populations if pandemics are not effectively prevented and controlled. Unfortunately, none of the COVID-19 vaccines in production are set aside to save the minks. We advocate that animals be immunized to save human lives.

Reactions of Long-run Private Consumption in Malaysia to Crises, Economic Leadership and Telecommunication Technology

We estimate the long-run reactions of private consumption in Malaysia to crises, economic leadership, information and communications technology (ICT), and other key determinants using time series econometrics. This study covers the quarterly sample from 1990:Q1 to 2020:Q4. We find that Malaysia's private consumption and its key determinants are cointegrated, demonstrating that a reliable long-run private consumption function can be estimated. We find that both economic and health crises, namely the Asian financial crisis in 1997/98, SARS and COVID-19 pandemic are likely to reduce private consumption in Malaysia. However, the long-run estimation results show that ICT and economic leadership are positively related to consumption. Therefore, policymakers should set the goal of encouraging the development of ICT infrastructure and good economic leadership in order to promote private consumption, which eventually sustains long-term economic growth and development.

E-Learning and Body Aches among Palestinian University Students during Covid-19 Pandemic: The Effects of Several Behavioural Changes

Background/Objective: COVID-19 pandemic has changed our lives in the current century. During the COVID-19 lockdown, most countries switched their education methods to e-learning. The use of different electronic devices for e-learning for long hours is associated with several musculoskeletal pain that varies based on the sitting position the students use during e-learning. The aim of our study is to examine the association between different body position used during the e-learning and the different body aches experienced by the students. We also aimed to examine if several types of behavioral modifications and/or exercise practices by the students might minimize body aches associated with e-learning. Method(s): The subjects of this study were students from An-Najah university in Palestine. 385 questionnaires were filled using Google forms questionnaire and all subjects were using e-learning due to COVID-19 pandemic. Result(s): Our study showed that a large percentage of participants experienced musculoskeletal pain during the use of electronic devices for e-learning. The location and severity of pain was correlated with the sitting position used during e-learning. Furthermore, behavioral changes during e-learning like taking breaks and changing sitting position minimized the experienced pain during e-learning but no significant decrease in pain was observed by engaging in several exercise practices. Conclusion(s): The university students that participated in this study had an increase in body aches during the e-learning process that is associated with their sitting position. Awareness programs should be lunched to university students to help them minimize this pain based on behavioral changes and proper exercise training during the e-learning. Copyright © 2022, Anka Publishers. All rights reserved.

The effects of us covid-19 policy responses on cryptocurrencies, fintech and artificial intelligence stocks: A fractional integration analysis

This paper assesses the impact of US policy responses to the Covid-19 pandemic on various technology-related assets such as cryptocurrencies, financial technology, and artificial intelligence stocks using fractional integration techniques. More precisely, it analyzes the behavior of the percentage returns in the case of nine major coins (Bitcoin-BITC, Stella-STEL, Litecoin-LITE, Ethereum-ETHE, XRP (Ripple), Dash, Monero-MONE, NEM, Tether-TETH) and two technology-related stock market indices (the KBW NASDAQ Technology Index-KFTX, and the NASDAQ Artificial Intelligence index-AI) over the period 1 January 2020-5 March 2021. The results suggest that fiscal measures such as debt relief and fiscal policy announcements had positive effects on the series examined during the pandemic, when an increased mortality rate tended instead to drive them down;by contrast, monetary measures and announcements appear to have had very little impact and the Covid-19 containment measures none at all.

Nudging digital physical activity breaks for home studying of university students-A randomized controlled trial during the COVID-19 pandemic with daily activity measures.

University students are of particular public health interest because they are at high risk for physical inactivity and sedentary behaviors. In conjunction with the COVID-19 pandemic, sedentariness and physical inactivity were reinforced, as the pandemic led to an increase in home studying. Physical activity (PA) breaks have been identified as promoting factors for university students' physical and mental health. Therefore, the present study explored an approach to nudge students to take PA breaks at home while studying. The purpose was to test the effectiveness of digital nudging for PA breaks for 10 days using a randomized intervention design during the COVID-19 pandemic. It included an intervention group who received daily digital motivational prompts for PA break videos and a minimal intervention control group who got low-level access to PA break videos via a one-time link sent to the media library. Using a sample of university students in the southwest of Germany (n = 57), two-level binary logistic regression models were calculated to predict daily participation in PA breaks during the intervention period depending on the nudging intervention, as well as previous participation in PA breaks, the general PA level of the subjects before the intervention, the time spent on PA and on home studying in a day, the kind of day during the intervention (weekday vs. weekend), and the students' age. Results revealed that the digital nudging intervention did not show any significant effect on the likelihood to participate in PA breaks on a given day (0.69 ≤ ß ≤ 0.75, p > 0.3). Instead, an individual-level effect revealed that the longer a student studied at home over the course of a day, the more likely he or she was to take a PA break (1.07 ≤ ß ≤ 1.11, p < 0.001). Current findings show that individual characteristics such as daily time spent on home studying, which can change over the course of the intervention phase, are relevant considerations within nudging intervention in university setting. This provides initial insights especially for digital PA breaks for students during home studying.

A comparison of methods for forecasting value at risk and expected shortfall of cryptocurrencies

Several procedures to forecast daily risk measures in cryptocurrency markets have been recently implemented in the literature. Among them, long-memory processes, procedures taking into account the presence of extreme observations, procedures that include more than a single regime, and quantile regression-based models have performed substantially better than standard methods in terms of forecasting risk measures. Those procedures are revisited in this paper, and their value at risk and expected shortfall forecasting performance are evaluated using recent Bitcoin and Ethereum data that include periods of turbulence due to the COVID-19 pandemic, the third halving of Bitcoin, and the Lexia class action. Additionally, in order to mitigate the influence of model misspecification and enhance the forecasting performance obtained by individual models, we evaluate the use of several forecast combining strategies. Our results, based on a comprehensive backtesting exercise, reveal that, for Bitcoin, there is no single procedure outperforming all other models, but for Ethereum, there is evidence showing that the GAS model is a suitable alternative for forecasting both risk measures. We found that the combining methods were not able to outperform the better of the individual models. © 2022 John Wiley & Sons Ltd.

Estimating the risk of SARS-CoV-2 deaths using a Markov switching-volatility model combined with heavy-tailed distributions for South Africa.

BACKGROUND: SARS-CoV-2 (Covid-19 virus) infection exposed the unpreparedness of African countries to health-related issues, South Africa included. Africa recorded more than 211 853 deaths as a consequence of Covid-19. When rare and deadly diseases require urgent hospitalisation strikes, governments and healthcare providers are usually caught unprepared, resulting in huge loss of lives. Usually, at the beginning of such pandemics, there is no rich data for health practitioners and academics to be able to forecast the number of patients or deaths related to the pandemic. This study aims to predict the number of deaths associated with Covid-19 infection. With the availability of the number of deaths on a daily basis, the results stemming from this study are important to inform and plan health policy. METHODS: This study uses the daily number of deaths due to Covid-19 infection. Exploratory data analysis reveals that the data exhibits non-normality, three structural breaks and volatility clustering characteristics. The Markov switching (MS)-generalized autoregressive conditional heteroscedasticity (GARCH)-type model combined with heavy-tailed distributions is fitted to the returns of the data. Using available daily reported Covid-19-related deaths up until 26 August 2021, we report 10-day ahead forecasts of deaths. All forecasts are compared to the actual observed values in the forecasting period. RESULTS: The Anderson-Darling Goodness of fit test confirms that the fitted models are adequate for the data. The Kupiec likelihood ratio test and the root mean square error (RMSE) were used to select the robust model at different risk levels. At 95% the MS(3)-GARCH(1,1) combined with Pearson's type IV distribution (PIVD) is the best model. This indicates that the proposed best-fitting model is reasonable and can be used for predicting the daily number of deaths due to Covid-19. CONCLUSION: The MS(3)-GARCH(1,1)-PIVD model provides a reliable and accurate method for predicting the minimum number of death due to Covid-19. The accuracy of the proposed model will assist policymakers, academics and health practitioners in forecasting the volatility of future health-related deaths in which the predictability of volatility plays an integral role in health risk management.

Volatility Interdependence Between Cryptocurrencies, Equity, and Bond Markets.

This paper investigates (i) the return-volatility spillover between Bitcoin, Ethereum, Ripple, and Litecoin, (ii) the interdependence between cryptocurrencies' volatility and the US equity and bond markets' volatility, and (iii) the impact of the Covid-19 outbreak on the cryptocurrencies' return-volatility. A two-step estimation approach is considered where Univariate General Autoregressive Conditional Heteroskedastic models are estimated to model the volatility of the four cryptocurrencies then a Simultaneous Equation Model is estimated to model the interconnection between the cryptocurrency volatilities, the US equity and bond markets' volatility, and Covid-19 outbreak. We show that return-volatility spillovers exist among Bitcoin, Ethereum, and Litecoin while Ripple is the main transmitter of shocks. We find that the cryptocurrency market is detached from the US stock market but not from the US bond market. Finally, we show that a high economic and financial uncertainty in the US stock market due to pandemic outbreaks affects the price of Litecoin, Bitcoin, and Ethereum. However, shocks are short-lived. Our findings have practical implications; as the evidence of volatility spillovers among cryptocurrencies and their relative isolation from the majority of mainstream assets should be factored into the valuation and portfolio diversification strategies of investors. In crisis times such as those induced by Covid-19, investors who seek protection from downward movements in bond markets could benefit from taking a position in Ethereum. Policymakers can also rely on our findings to time their intervention to stabilize markets and control uncertainties inherent to stressful periods.

Analysis of Learning Effect through Voice Signal Analysis in Online Education Environment

The new coronavirus infection (COVID-19) that appeared suddenly has permeated our daily lives and changed our way of life. In the field of education, education is being conducted in a non-face-to-face teaching method to prevent the spread of coronavirus. In the end, e-Learning, a new educational and training system that can provide a lifelong education environment in the 21st century information society, is increasing in use in the field of education. The biggest advantage of the online education system is that it provides an environment in which you can learn the necessary contents anytime, anywhere. However, there are cases in which the learning effect is reduced because various learning support is not available in the online space due to the sudden change of the educational environment due to the covid-19. Therefore, in this thesis, a study was conducted to analyze the learning effect of online education with voice signals for college students who are receiving education through an online education environment. To this end, the learning effect was classified into a group saying that the learning effect increased and a group that decreased due to online education, and the voices of the subjects in each group were collected. As a result of the experiment, the results of the vocal cord vibration (Pitch), Degree of voice breaks, Jitter and Shimmer were consistent among the elements of voice signal analysis between two groups. Copyright © 2022 by the author(s).

Receiving the Coronavirus Vaccine while Fasting (a Comparative Study between the Fatwa of the MUI and World Fatwa Councils)

This research aims to know the ruling on the validity of fasting by receiving a vaccine for the Coronavirus or not, through the fatwa of the Indonesian Council of Scholars, compared to the fatwas of the fatwa councils in the Islamic world, using the analytical and comparative methodology. Fasting is obligatory for Muslims in the month of Ramadan in particular, and it may be required in other months. On the other hand, the Coronavirus may spread in the world at the end of the year 2019. As of February 28, 2021, it has recorded one million and 335 thousand infections and 36 thousand and 166 deaths, and in Indonesia, which leads the government to take preventive measures, including positively receiving the Coronavirus vaccine for the people. And when Ramadan came, people were wondering about the ruling on taking it while fasting, and the Fatwa Committee of the Indonesian Council of Scholars issued a fatwa that fasting is not invalidated by receiving the vaccine, and it became clear that this fatwa agrees with the fatwas of the fatwa councils in the Islamic world.

New Online Reality

At the beginning of 2020 we assisted to an enormous increase of remote working due to Covid-19 pandemic. Largest effects arise, the increase in flexible working has impact on how people use their home, shops, offices, enterprises. All questions about positive and negative implications cannot leave people's mind. In this ecosystem at any point employees will inevitably have to work with other people at some point, must interact with clients and customers, need to work with colleagues, managers, suppliers and build relationships with them. Changing mindset, it is not an easy task. Some companies are creating solutions with that we do not forget to maintain personal relationships between employees. This paper discuss that points and give a prototype, to try effectively tackle a problem. © 2022 IEEE.

Nowcasting domestic demand using a dynamic factor model: the case of Ireland

The COVID-19 pandemic highlighted the need for timely information on the evolving economic impacts of such a crisis. During these periods, there is an increased need to understand the current state of the economy to guide the effective implementation of policy. This is made difficult by the fact that official estimates of economic indicators, such as those published by national statistical agencies, are released with a substantial lag. Using the case of Ireland, this article shows that the information contained in a panel of monthly economic indicators can be related to Quarterly National Accounts under the methodological framework of a dynamic factor model (DFM). The article also suggests that accounting for structural breaks improves the nowcasting performance of domestic demand. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

The end of the active work break? Remote work, sedentariness and the role of technology in creating active break-taking norms

Excessive sedentariness can impair workers' health and productivity. The move to working from home as a result of the Covid-19 pandemic eliminated many workday opportunities for physical activity. This, coupled with a blurring of boundaries between work and non-work periods, put many at risk of overwork and musculoskeletal issues. We examined how the sudden transition to working from home influenced people's ability to take physically active work breaks. We found that the absence of social norms associated with the presence of colleagues in the work environment left workers uncertain about whether and when it is appropriate to take breaks. The pressure to demonstrate productivity while working asynchronously led to increased sedentariness and decreased break-taking. We propose that online tools that promote flexible social norms around break-taking could empower remote workers to incorporate regular physical activity into their days, without compromising the beneficial aspects of asynchronous working. © 2022 ACM.