The Role of Digital Innovation and Its Impact on Competitiveness and Performance: The Case of Business Hotel in Indonesia: Acces la Success

This research examines the effect of digital innovation on the competitiveness and performance of hospitality businesses in Indonesia. This research was conducted with a quantitative research approach. Participants in this study are managers of hotel companies that implement online systems in Indonesia. The samples in this study were 218 respondents. Hypotheses are tested using the Structural Equation Modeling method and processed using Amos Software Version 23. The results show that there is a positive and significant effect between digital innovation on competitiveness, digital innovation and competitiveness also effect hotel business performance positively and significantly. We also found that competitiveness can mediate the effect of digital innovation on business performance. Therefore, we suggest improving business performance with enhancing competitiveness, to improve competitiveness can be done by increasing the implementation of digital innovation.

On stylized facts of cryptocurrencies returns and their relationship with other assets, with a focus on the impact of COVID-19

This study provides an empirical analysis on the main univariate and multivariate stylized facts iin return series of the two of the largest cryptocurrencies, namely Ethereum and Bitcoin. A Markov-Switching Vector AutoRegression model is considered to further explore the dynamic relationships between cryptocurrencies and other financial assets. We estimate the presence of volatility clustering, a rapid decay of the autocorrelation function, an excess of kurtosis and multivariate little cross-correlation across the series, except for contemporaneous returns. The analysis covers the pandemic period and sheds lights on the behaviour of cryptocurrencies under unexpected extreme events.

Higher-order moments and co-moments' contribution to spillover analysis and portfolio risk management

Unlike volatility, the skewness and kurtosis of asset returns are often neglected in the analysis of spillovers and risk management, although they capture the return asymmetry and fat-tailedness, respectively, arising from the non-normality of returns. In this paper, we provide evidence of the relevance and utility of considering spillovers in volatility and higher-order moments (skewness, and kurtosis) and co-moments (covariance, co-skewness, and co-kurtosis), and their implications for hedging. Using high-frequency data on the US stock, crude oil, and gold markets, a time-varying spillover approach and portfolio analysis, we reveal the following results. Firstly, besides volatility and covariance, co-skewness and co-kurtosis are relevant spillover transmitters across the stock, crude oil, and gold markets. Secondly, the level of total spillover increases when including not only covariance but also co-skewness and co-kurtosis, suggesting the relevance of considering higher order co-moments beyond volatility when studying spillovers. Thirdly, the inclusion of co-moments in the spillover analysis generates a significant improvement in hedging for all pairs, which is reflected in the significant increase in the utility function when co-skewness and co-kurtosis are considered. This result is noted when the COVID-19 sub-period is considered separately, except for oil‑gold. Overall, the findings matter for the system of interconnectivity across various assets and emphasize the implications and contributions of higher-order moments and co-moments to portfolio allocation and financial risk management. © 2023 Elsevier B.V.

Lockdown Effects on Air Quality in Megacities During the First and Second Waves of COVID-19 Pandemic

Air pollution is among the highest contributors to mortality worldwide, especially in urban areas. During spring 2020, many countries enacted social distancing measures in order to slow down the ongoing COVID-19 pandemic. A particularly drastic measure, the "lockdown”, urged people to stay at home and thereby prevent new COVID-19 infections during the first (2020) and second wave (2021) of the pandemic. In turn, it also reduced traffic and industrial activities. But how much did these lockdown measures improve air quality in large cities, and are there differences in how air quality was affected? Here, we analyse data from two megacities: London as an example for Europe and Delhi as an example for Asia. We consider data during first and second-wave lockdowns and compare them to 2019 values. Overall, we find a reduction in almost all air pollutants with intriguing differences between the two cities except Delhi in 2021. In London, despite smaller average concentrations, we still observe high-pollutant states and an increased tendency towards extreme events (a higher kurtosis of the probability density during lockdown) during 2020 and low pollution levels during 2021. For Delhi, we observe a much stronger decrease in pollution concentrations, including high pollution states during 2020 and higher pollution levels in 2021. These results could help to design policies to improve long-term air quality in megacities.

Hotel dynamic pricing, stochastic demand and covid-19.

We develop an innovative framework to study how hoteliers apply inventory control and price discrimination taking into account seasonality. We end up with a time-varying model that, using publicly available information, connects the early booking and last-minute pricing decisions. In doing so, we account for the expected demand size and price elasticity, the inventory put on sales, and the last-minute demand shocks. An analysis focused on 100 hotels in Milan (Italy) shows that during the Covid-19 last-minute discounts/surcharges remain stable over long periods while the role of advance booking as a lever for revenue management is reduced. Moreover, the pandemic has increased the last-minute adjustment at the short advance booking, especially for midweek days.

CRUDE OIL MARKET RISK EVALUATIONS UNDER THE PRESENCE OF THE COVID-19 PANDEMIC

Due to the outbreak of Covid-19, crude oil demand fell significantly. The West Texas Intermediate (WTI) crude oil price turned negative for the first time in its history. Given the importance of crude oil as a natural resource and its intrinsic tie to the economy, the price fall may significantly impact crude oil participants. Even though the pandemic has become a new norm globally, crude oil price movements will assuredly remain a concern for crude oil participants. This study uses MATLAB software to model various conditional mean and variance models to address the impact of Covid-19 on WTI and Brent crude oil prices, forecasting returns and measuring potential Value at Risk and Expected Shortfall. Daily crude oil prices of WTI and Brent are obtained from the U.S. Energy Information Administration (EIA) and cover from 4 January 2010 to 30 July 2021. The study period is divided into two periods to study the impact of Covid19 on crude oil prices. The conditional mean and variance models are evaluated by the Box-Jenkins methodology. This study found that both WTI and Brent returns do not follow a normal distribution, and the GJR(1,1) with student-t distribution outperformed the GARCH and EGARCH models. The best fit model of WTI is MA(1)-GJR(1,1), while the best fit model of Brent is MA(2)-GJR(1,1). The results reveal the impact of Covid-19 on crude oil prices and show a higher standard deviation during Covid-19 than before Covid-19. The high GARCH value indicates that the volatility is highly persistent and clustering. The forecast results reveal that the volatility of WTI and Brent crude oil prices will continue to rise in the future. Risk determination for both WTI and Brent was conducted, and the potential losses of WTI are found to be greater than that of Brent.

Abstracting Volatility Dynamics of South Korean Stock Market: A case study

The main aim of this research paper is to investigate the volatility dynamics of South Korean stock market. The sample data covers the long time period from December 1996 to September 2022, which also includes certain extreme events such as: the Asian financial crisis of 1997, the global financial crisis of 2008, the COVID-19 pandemic, the war between Russia and Ukraine. The econometric approach focuses on GARCH models and certain statistical tests. The empirical results contribute to the existing specialized literature.

Dynamics diagnosis of the COVID-19 deaths using the Pearson diagram.

The pandemic COVID-19 brings with it the need for studies and tools to help those in charge make decisions. Working with classical time series methods such as ARIMA and SARIMA has shown promising results in the first studies of COVID-19. We advance in this branch by proposing a risk factor map induced by the well-known Pearson diagram based on multivariate kurtosis and skewness measures to analyze the dynamics of deaths from COVID-19. In particular, we combine bootstrap for time series with SARIMA modeling in a new paradigm to construct a map on which one can analyze the dynamics of a set of time series. The proposed map allows a risk analysis of multiple countries in the four different periods of the pandemic COVID-19 in 55 countries. Our empirical evidence suggests a direct relationship between the multivariate skewness and kurtosis. We observe that the multivariate kurtosis increase leads to the rise of the multivariate skewness. Our findings reveal that the countries with high risk from the behavior of the number of deaths tend to have pronounced skewness and kurtosis values.

Stay positive: studying the change patterns in academics' job and life satisfaction during the Covid-19 pandemic through growth curve modeling

Purpose During the Covid-19 outbreak, universities around the globe were closed or went online due to lockdowns implemented to curb the pandemic's spread. This study aims to examine the changes in Malaysian academics' job and life satisfaction during a testing four-month period, from the beginning of the first Covid-19 lockdown until two months after it ended. It also assesses the impact of affective states and age group on these two constructs. Design/methodology/approach In this longitudinal study, the authors collected data from 220 academics in Malaysia at three time points in 2020, namely the beginning of the lockdown (April), the end of the lockdown (June) and two months after the lockdown (August). The authors applied multivariate latent growth curve (LGC) modeling to study changes in job satisfaction and life satisfaction. In addition, we added age group, as a time-invariant covariate, as well as positive and negative affect, as two time-varying covariates, to our LGC model. The authors estimated the LGC model using the EQS 6.4 statistical package. Findings The results show that both job and life satisfaction were stable over time, although their means were below the average. Positive affect was a significant predictor of both types of satisfaction, and age group was a significant predictor of job satisfaction. Practical implications The main implication the authors draw from this study is connected to job and life satisfaction's mean values being below average. In line with the affective events theory (AET), the authors recommend paying particular attention to work environment features, such as providing sufficient infrastructure for employees working from home and keeping social relations intact. Especially young academics should receive sufficient support. Originality/value The study is one of a limited number that examined longitudinal effects during the Covid-19 pandemic in the domains of human resource management and organizational behavior. Hence, this study expands our knowledge of employees' affect and attitudes during an unprecedented global health crisis, particularly in the under-researched area of the Malaysian higher education sector.

Exponentiated Gull Alpha Exponential Distribution with Application to COVID-19 Data

In this paper, the main aim is to define a statistical distribution that can be used to model COVID-19 data in Mexico and Canada. Using the method of exponentiation on the gull alpha exponential distribution introduces a new distribution with three parameters called the exponentiated gull alpha power exponential (EGAPE) distribution. The distribution has the benefit of being able to represent monotonic and nonmonotonic failure rates, both of which are often seen in dependability issues. It is possible to determine the quantile function as well as the skewness, kurtosis, and order statistics of the suggested distribution. The approach of maximum likelihood is used in order to calculate the parameters of the model, and the RMSE and average bias are utilised in order to evaluate how successful the strategy is. In conclusion, the flexibility of the new distribution is demonstrated by modeling COVID-19 data. From the practical application, we can conclude that the proposed model outperformed the competing models and therefore can be used as a better option for modeling COVID-19 and other related datasets.

Arab women feelings while seeking information during COVID-19 pandemic: applying PMT constructs

Purpose>This study investigates the effect of protection motivation theory (PMT) constructs on Arab women's feelings while seeking information during the COVID-19 pandemic.Design/methodology/approach>The study has adopted a mixed-method approach using semi-structured interviews and a questionnaire to explore PMT constructs' impact on women's feelings while seeking information on COVID-19. Several tests, such as standard deviation, mean, skewness, kurtosis and persons, were used to check the reliability of data and inter-relationships between constructs.Findings>The study results show a significant positive correlation between PMT constructs (perceived vulnerability, perceived severity, response efficacy, self-efficacy and response cost) with the feelings of Arab women during information seeking on COVID-19. However, the relationship between threat appraisal and feelings during information seeking was more substantial than coping appraisal and feelings during information seeking. The researchers hope that this study creates a baseline of cross-cultural studies on PMT constructs' effect on women's feelings while seeking health information.Research limitations/implications>The current study was conducted on female participants only. While the study intended to examine Arab women's feelings during information seeking with PMT's application, the results may be affected by other factors that were not considered in the current study. Furthermore, the questionnaire was distributed in three Arab countries, which means that the results cannot be generalized in other geographical contexts. Therefore, similar studies need to be conducted in larger geographical areas as cultural factors may produce different results.Originality/value>This study explores women's feelings while seeking COVID-19 information using the PMT constructs. As far as we know, this study is the first study to investigate Arab women's feelings while seeking health information during pandemics. PMT utilization is considered a new approach to discover and measure informational needs and feelings associated with it during pandemics.

Covid-19 Detection using Spectral and Statistical Features of Cough and Breath Sounds

The pandemic situation due to Corona Virus Disease of 2019 (COVID-19) is significant public health risk around the world. The infected people can spread this virus very quickly. Due to this reason, the early detection is essential to reduce its spread. This research effort aims to develop a method for diagnosis of COVID-19 based on the recording of cough and breath sounds. In this paper, a convolutional neural network (CNN) classifier is applied after train and test splitting for cough and breath sound features. The present work show that the combination of MFCC and cepstrum-based statistical features along with ZCR improve the accuracy of detection to the great extent. It shows great potential in the development of automatic COVID-19 detection tool.

Association of population migration with air quality: Role of city attributes in China during COVID-19 pandemic (2019-2021).

Atmospheric pollution studies have linked diminished human activity during the COVID-19 pandemic to improve air quality. This study was conducted during January to March (2019-2021) in 332 cities in China to examine the association between population migration and air quality, and examined the role of three city attributes (pollution level, city scale, and lockdown status) in this effect. This study assessed six air pollutants, namely CO, NO2, O3, PM10, PM2.5, and SO2, and measured meteorological data, with-in city migration (WCM) index, and inter-city migration (ICM) index. A linear mixed-effects model with an autoregressive distributed lag model was fitted to estimate the effect of the percent change in migration on air pollution, adjusting for potential confounding factors. In summary, lower migration was associated with decreased air pollution (other than O3). Pollution change in susceptibility is more likely to occur in NO2 decrease and O3 increase, but unsusceptibility is more likely to occur in CO and SO2, to city attributes from low migration. Cities that are less air polluted and population-dense may benefit more from decreasing PM10 and PM2.5. The associations between population migration and air pollution were stronger in cities with stringent traffic restrictions than in cities with no lockdowns. Based on city attributes, an insignificant difference was observed between the effects of ICM and WCM on air pollution. Findings from this study may gain knowledge about the potential interaction between migration and city attributes, which may help decision-makers adopt air-quality policies with city-specific targets and paths to pursue similar air quality improvements for public health but at a much lower economic cost than lockdowns.

Severe but plausible – or not?

In light of the Covid-19 crisis, the Federal Reserve (Fed) has carried out stress tests to assess whether major banks have sufficient capital to ensure their viability should a new and perhaps unprecedented crisis emerge. The Fed argues that the scenarios underpinning these stress tests are severe but plausible, yet they have not offered any evidence or framework for measuring the plausibility of their scenarios. If the scenarios are indeed plausible, it makes sense for banks to retain enough capital to with-stand their occurrence. If, however, the scenarios are not reasonably plausible, banks will have deployed capital less productively than they otherwise could have, thereby impairing credit expansion and economic growth. The authors apply a measure of statistical unusualness, called the Mahalanobis distance, to assess the plausibility of the Fed’s stress scenarios. A first pass of this analysis, based on conventional statistical assumptions, reveals that the Fed’s scenarios are not even remotely plausible. However, the authors offer two modifications to their initial analysis that increase the scenarios’ plausibility. First, they show how the Fed can minimally modify their scenarios to render them marginally plausible in a Gaussian world. And second, they show how to evaluate the plausibility of the Fed’s scenarios by replacing the theoretical world of normality with a distribution that is empirically grounded. © 2022 Infopro Digital Risk (IP) Limited.

Singlehanded or joint race? Stock market volatility prediction

This paper examines whether the realized skewness and kurtosis contain predictability for Shanghai Stock Exchange Sector Index. We find kurtosis contains more information to predict the Shanghai Stock Exchange Sector Index volatility. Importantly, the model considering the combination of both skewness and kurtosis has the best predictability for the stock market volatility. Moreover, we investigate the economic values of the models and asymmetric effects of skewness and kurtosis on stock market volatility, finding skewness (skewness <0) and kurtosis (kurtosis >3) own better forecasting performance. Finally, we discuss the predictability of skewness and kurtosis during two turbulent periods of China's stock bubble and the COVID-19 pandemic.

Diffusional Characteristics of Brain Matter after Stroke.

We analyzed characteristics of diffusion and its kurtosis obtained using diffusion-kurtosis MRI in the hemisphere contralateral to the one affected by acute cerebrovascular accident. Diffusion characteristics in the white and gray matter were compared using analysis of covariance (ANCOVA) in healthy subjects and stroke patients with consideration for the age and sex factors. Significant differences between the groups were revealed for apparent diffusion coefficient and mean kurtosis in the white matter. Age dependence was studied using regression analysis and, according to the results of ANCOVA, this factor was found to be significant for apparent diffusion coefficient and diffusion kurtosis in the white matter. Metrics are proposed that can be used to determine the risk of stroke.

Editorial

The first two Technical Articles are outstanding studies of the crystal structures of a pharmaceutical and an inorganic “Laves” C15 phase along with its magnetic properties. The third Technical Article, “Continuous Series of Symmetric Peak Profile Functions Determined by Standard Deviation and Kurtosis” by Dr. Takashi Ida, is of particular interest to me due to my prior profile modeling work and the development of standard reference materials (several decades ago) on computer modeling of PXRD profile shapes and addressing the shift in position and shape of diffraction profiles due to instrumental aberrations. The International Report provides an excellent summary of the meeting, exhibitors, invited talks, contributed talks, poster sessions, and provided means for virtual social activities.

To keep faith with homoskedasticity or to go back to heteroskedasticity? The case of FATANG stocks.

Did the pattern of US stock market volatility change due to COVID-19 or have the US stock markets been less volatile despite the pandemic shock? And as for tech stocks, are they even less volatile than the market overall? In this paper, we provide evidence in favor of a "quietness" in the stock markets, interrupted by COVID-19, by analyzing dispersion, skewness and kurtosis characteristics of the empirical distribution of nine returns series that include individual FATANG stocks (FAANG: Facebook, Amazon, Apple, Netflix and Google; plus Tesla) and US indices (S&P 500, DJIA and NASDAQ). In comparison with the years before, the daily average return after COVID-19 was 6.48, 2.58 and 2.34 times higher for Tesla, Apple and NASDAQ, respectively. In terms of volatility, the increase was more pronounced in the three stock indices when compared to the individual FATANG stocks. This paper also puts forward a new methodology based on semi-variance and semi-kurtosis. While the value of the ratio between semi-kurtosis and kurtosis is always higher than 70% for the three US stock indices, in the case of stocks the opposite is true, which highlights the importance of large positive returns when compared to negative ones. Structural breaks and conditional heteroskedasticity are also analyzed by considering the traditional symmetrical and asymmetrical GARCH models. We show that in the most recent past, despite the COVID-19 pandemic, the FATANG tech stocks are characterized mostly by conditional homoskedasticity, while the returns of US stock indices are characterized mainly by conditional heteroskedasticity.