ON THE ROBUSTNESS OF RANGE‐BASED VOLATILITY ESTIMATORS

We empirically examine Parkinson's range‐based volatility estimate in the federal funds market, which is unique because institutional regulations create a predictable pattern in interday volatility. We find that range‐based volatility estimates and standard deviations produce the expected volatility pattern. We also find that at trading pressure points where microstructure noise should be greatest, range‐based estimates are less than the standard deviations. Thus, we support the argument that range‐based volatility estimates remove the upward bias created by microstructure noise. We find that the Parkinson method is the most efficient range‐based volatility measure among a set of alternates in this market.     

Agility,adaptability, and alignment: new capabilities for PSM in a post-pandemic world

Purchasing and Supply Management (PSM) was overwhelmed by a perfect storm during the Covid-19 emergency, in both the public and private sectors. Many organizations were caught unprepared, and it became evident that they did not have the capabilities needed to quickly react and respond to changes caused by the pandemic. This poses the questions of where and how PSM systems should evolve to better contribute to organizational responsiveness to crisis events in the future. In this Editorial, which introduces the “PSM learning from the pandemic: transforming for better crisis management” Notes and Debates special issue, the six contributions included in this issue are discussed. We relate the evidence included in the contribution to three critical capabilities of supply chains in the post-Covid-19 business environment – agility, adaptability, and alignment – and, in this editorial, we discuss how they can be conceptualized from a PSM perspective. From a theoretical perspective, we provide a starting point for future studies that want to focus on how these capabilities can be deployed in PSM, and how they impact the supply network design. From a managerial perspective, these definitions provide preliminary points of discussion on what organizations can do to enhance these capabilities in the future.     

The socio-economic determinants of COVID-19: A spatial analysis of German county level data

The study explores the association of socioeconomic, demographic, and health-related variables at the regional level with COVID-19 related cases and deaths in Germany during the so-called first wave through mid-June 2020. Multivariate spatial models include the 401 counties in Germany to account for regional interrelations and possible spillover effects. The case and death numbers are, for example, significantly positively associated with early cases from the beginning of the epidemic, the average age, the population density and the share of people employed in elderly care. By contrast, they are significantly negatively associated with the share of schoolchildren and children in day care as well as physician density. In addition, significant spillover effects on the case numbers of neighbouring regions were identified for certain variables, with a different sign than the overall effects, giving rise to further future analyses of the regional mechanisms of action of COVID-19 infection. The results complement the knowledge about COVID-19 infection beyond the clinical risk factors discussed so far by a socio-economic perspective at the ecological level.     

Adaptive market hypothesis: The story of the stock markets and COVID-19 pandemic

Since the level of markets’ information efficiency is key to profiteering by strategic players, Shocks; such as the COVID-19 pandemic, can play a role in the nature of markets’ information efficiency. The martingale difference and conditional heteroscedasticity tests are used to evaluate the Adaptive form of market efficiency for four (4) major stock market indexes in the top four affected economies during the COVID-19 pandemic (USA, Brazil, India, and Russia). Generally, based on the martingale difference spectral test, there is no evidence of a substantial change in the levels of market efficiency for the US and Brazilian stock markets in the short, medium, and long term. However, in the long term, the Indian stock markets became more information inefficient after the coronavirus outbreak while the Russian stock markets become more information efficient. Intuitively, these affect the forecastability and predictability of these markets’ prices and/or returns. Thereby, informing the strategic and trading actions of stock investors (including arbitrageurs) towards profit optimization, portfolio asset selection, portfolio asset adjustment, etc. Similar policy implications are further discussed.     

The impact of COVID-19 on the G7 stock markets: A time-frequency analysis

We examine the co-movement of the G7 stock returns with the numbers of confirmed COVID-19 cases and causalities based on daily data from December 31, 2019 to November 13, 2020. We employ the wavelet coherence approach to measure the impact of the numbers of confirmed cases and deaths on the G7 stock markets. Our findings reveal that both the number of confirmed COVID-19 cases and the number of deaths exhibit strong coherence with the G7 equity markets, although we find heterogeneous results for the Canadian and Japanese equity markets, in which the numbers of COVID-19 cases and the deaths exhibit only a weak relationship. This evidence is more pronounced in the long-term horizon rather than the short-term horizon. Moreover, the lead-lag relationship entails a mix of lead-lag relations across different countries. We present the implications of these findings for both policymakers and the international investment community.     

后疫情时代我国数字经济高质量发展战略对策

数字经济是有效统筹疫情防控和经济社会发展的重要路径.目前,我国数字经济总体规模不断扩大,产业数字化成为我国数字经济的主引擎,我国数字经济有望呈现快速且高质量发展态势.我国数字经济之所以取得巨大成就,主要归因于国家高度重视与审慎包容支持、数字经济综合市场优势显著以及拥有一批能力较强的数字企业等.但我国数字经济领域仍存在产...     

Researching the crowd: Implications on philanthropic crowdfunding and donor characteristics during a pandemic

New online forms of giving have appeared next to more traditional ways like door-to-door collections. One of these new forms is philanthropic crowdfunding: donation- and reward-based crowdfunding. Crowdfunding is a promising method for mobilising and recruiting donors who may be unreachable via traditional methods. We analysed online giving via crowdfunding, focusing on donor characteristics and giving behaviour before and during the COVID-19 pandemic. Our analysis comprises survey research (n = 2125) observing giving behaviour on an individual level for both donors and non-donors. Our contributions are twofold. First, we report on the characteristics of donors who give to crowdfunding sources and in relation to donors who give via a door-to-door (i.e., ‘traditional’) collection focusing on micro- rather than macro-level data. Second, we compare the giving behaviour via crowdfunding with references to door-to-door collections before and during the COVID-19 pandemic. We show that the percentage of individuals supporting crowdfunding did not increase between 2018 (11%) and 2020 (12%). Regarding the amount, donors donated 13% higher amounts in 2020, but the difference was not significant. Regarding the characteristics of donors, we find that social media has a substantive role in giving via crowdfunding irrespective of other personal markers such as age, education, income, and gender, while this is not relevant in the case of door-to-door collection. Moreover, most people give to crowdfunding projects that are connected to an acquaintance, which signals that familiarity with the person initiating the crowdfunding projects plays a role. We conclude that crowdfunding, relative to more traditional giving, focuses more on informal giving than formal giving. Such an understanding requires different strategies and stimuli to increase giving via crowdfunding.     

Industries' heterogeneous reactions during the COVID-19 outbreak: Evidence from Chinese stock markets

This study examines the heterogeneous effects of the COVID-19 outbreak on stock prices in China. We confirm what is already known, that the pandemic has had a significant negative impact on stock market returns. Additionally, we find, this effect is heterogeneous across industries. Second, fear sentiment can directly cause stock prices to fall and panic exacerbates the negative impact of the pandemic on stock returns. Third, and most importantly, we demonstrate the underlying mechanisms of four firm characteristics and find that those with high asset intensity, low labor intensity, high inventory-to-revenue ratio, and small market value are more negatively affected than others. For labor-intensive state-owned firms, in particular, stock performance worsened because of higher idle labor costs. Finally, we created an index to measure the relative position of an industry in the supply chain, which shows that downstream companies were more vulnerable to the effects of the pandemic.     

Evaluation of the treatment options for COVID-19 patients using generalized hesitant fuzzy- multi criteria decision making techniques

The breakout of the pandemic COVID-19 has affected numerous countries and territories worldwide. As COVID-19 specific medicines yet to be invented, at present the treatment is case specific, hence identification and evaluation of different prevalent treatment options based on various criteria and attributes are very important not only from the point of view of present pandemic but also for futuristic pandemic preparedness. The present study focuses on identifying, evaluation and ranking of treatment options using Multi Criteria Decision Making (MCDM). In this regard, the existing literature, doctors and scientist were interviewed to know the current treatment options in vogue and the scale of their importance with respect to the criteria. The criteria taken are side effect, regime cost, treatment duration, plasma stability, plasma turnover, time of suppression, ease of application, drug-drug interaction, compliance, fever, pneumonia, intensive care, organ failure, macrophage activation syndrome, hemophagocytic syndrome, pregnancy, kidney problem, age. This study extended Hesitant Fuzzy Set (HFS) to Generalized Hesitant Fuzzy Sets (GHFS). Generalized Hesitant Pentagonal Fuzzy Number (GHPFN) is developed. The properties of GHPFN are demonstrated. Two types of GHPFN has been described. The GHPFN (2nd type) along with MCDM tool Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) has been applied to rank the treatment options. The result of the study ranked ‘Hydroxychloroquine’ as the first alternative followed by, ‘Plasma Exchange’, ‘Tocilizumab’, ‘Remdesivir’ and ‘Favipravir’. To check the robustness and steadiness of the proposed methodology, comparative analysis and sensitivity analysis has been conducted.