Bayesian nonlinear expectation for time series modelling and its application to Bitcoin.

This paper proposes a two-stage approach to parametric nonlinear time series modelling in discrete time with the objective of incorporating uncertainty or misspecification in the conditional mean and volatility. At the first stage, a reference or approximating time series model is specified and estimated. At the second stage, Bayesian nonlinear expectations are introduced to incorporate model uncertainty or misspecification in prediction via specifying a family of alternative models. The Bayesian nonlinear expectations for prediction are constructed from closed-form Bayesian credible intervals evaluated using conjugate priors and residuals of the estimated approximating model. Using real Bitcoin data including some periods of Covid 19, applications of the proposed method to forecasting and risk evaluation of Bitcoin are discussed via three major parametric nonlinear time series models, namely the self-exciting threshold autoregressive model, the generalized autoregressive conditional heteroscedasticity model and the stochastic volatility model. Supplementary Information: The online version contains supplementary material available at 10.1007/s00181-022-02255-z.

Firm performance in uncertain times: the importance of networking capabilities

PurposeAlthough a body of studies investigates how networking capabilities (NCs) form and maintain interorganizational relationships that affect firm performance, little is known about this relationship in crisis contexts. This article explores managers' perceptions of environmental uncertainties and how this perception influences NC development and subsequent firm performance, especially during the COVID-19 crisis.Design/methodology/approachThe authors used a quantitative research approach to complete this objective, utilizing primary data from a survey of North American firms (N = 212), mostly (62.3%) small- and medium-sized. Data were analyzed via the partial least squares structural equation modeling technique.FindingsThe authors found that managers' perception of environmental uncertainties positively impacts the NCs to initiate and develop relationships, which is associated with better firm performance during crises. The capability to initiate and develop relationships supports the firm's access to relevant resources that may be converted into business performance.Originality/valueBy analyzing managers' perceptions of environmental uncertainties and the development of NCs, the study results expand upon previous research by highlighting that starting new relationships and developing existing ones may be an efficient managerial response immediately after a crisis occurs.

Firm-level Business Uncertainty and the Predictability of the Aggregate U.S. Stock Market Volatility during the COVID-19 Pandemic

In this paper, we analyze the predictive role of firm-level business expectations and uncertainties derived from a panel survey of U.S. 1,750 business executives from 50 states for the realized variance (sum of daily squared log-returns over a month) of the S&P500 index over the monthly period of September 2016 to July 2021. Unlike standard models, our predictive framework adopts a time-varying approach due to the existence of multiple structural breaks in the relationship between volatility and the predictors in the model, which in turn leads to statistically insignificant causal effects in a constant parameter set-up. Our time-varying results suggest that the predictive power of firm-level business uncertainty is concentrated during the early part of the sample associated with the U.S.-China trade war and towards the end of our data coverage in the wake of the outbreak of the COVID-19 pandemic. Since in-sample predictability does not guarantee the same over an out-sample, we also conducted a full-fledged forecasting exercise to show that subjective expectations and uncertainties associated with sales growth rates and employment produce statistically significant predictability gains over January 2020 to July 2021, given an in-sample of September 2016 to December 2019. Our results suggest that subjective measures of business uncertainty at the firm level indeed capture predictive information regarding aggregate stock market uncertainty, which has important implications for investors and economic projections at the policy level.

The Assessment of Medication Effects in Omicron Patients through MADM Approach Based on Distance Measures of Interval-Valued Fuzzy Hypersoft Set.

Omicron, so-called COVID-2, is an emerging variant of COVID-19 which is proved to be the most fatal amongst the other variants such as alpha, beta and gamma variants (α, ß, γ variants) due to its stern and perilous nature. It has caused hazardous effects globally in a very short span of time. The diagnosis and medication of Omicron patients are both challenging undertakings for researchers (medical experts) due to the involvement of various uncertainties and the vagueness of its altering behavior. In this study, an algebraic approach, interval-valued fuzzy hypersoft set (iv-FHSS), is employed to assess the conditions of patients after the application of suitable medication. Firstly, the distance measures between two iv-FHSSs are formulated with a brief description some of its properties, then a multi-attribute decision-making framework is designed through the proposal of an algorithm. This framework consists of three phases of medication. In the first phase, the Omicron-diagnosed patients are shortlisted and an iv-FHSS is constructed for such patients and then they are medicated. Another iv-FHSS is constructed after their first medication. Similarly, the relevant iv-FHSSs are constructed after second and third medications in other phases. The distance measures of these post-medication-based iv-FHSSs are computed with pre-medication-based iv-FHSS and the monotone pattern of distance measures are analyzed. It is observed that a decreasing pattern of computed distance measures assures that the medication is working well and the patients are recovering. In case of an increasing pattern, the medication is changed and the same procedure is repeated for the assessment of its effects. This approach is reliable due to the consideration of parameters (symptoms) and sub parameters (sub symptoms) jointly as multi-argument approximations.

Do commodity assets hedge uncertainties? What we learn from the recent turbulence period?

This study analyses the impact of different uncertainties on commodity markets to assess commodity markets' hedging or safe-haven properties. Using time-varying dynamic conditional correlation and wavelet-based Quantile-on-Quantile regression models, our findings show that, both before and during the COVID-19 crisis, soybeans and clean energy stocks offer strong safe-haven opportunities against cryptocurrency price uncertainty and geopolitical risks (GPR). Soybean markets weakly hedge cryptocurrency policy uncertainty, US economic policy uncertainty, and crude oil volatility. In addition, GSCI commodity and crude oil also offer a weak safe-haven property against cryptocurrency uncertainties and GPR. Consistent with earlier studies, our findings indicate that safe-haven traits can alter across frequencies and quantiles. Our findings have significant implications for investors and regulators in hedging and making proper decisions, respectively, under diverse uncertain circumstances.

Dynamic Impacts of External Uncertainties on the Stability of the Food Supply Chain: Evidence from China.

The food supply chain operates in a complex and dynamic external environment, and the external uncertainties from natural and socio-economic environment pose great challenges to the development of the food industry. In particular, the COVID-19 pandemic and Russia-Ukraine conflict have further exacerbated the vulnerability of the global food supply chain. Analyzing the dynamic impacts of external uncertainties on the stability of food supply chain is central to guaranteeing the sustainable security of food supply. Based on the division of food supply chain and the classification of external uncertainties, the TVP-FAVAR-SV model was constructed to explore the dynamic impacts of external uncertainties on food supply chain. It was found that the impacts of external uncertainty elements were significantly different, the combination of different external uncertainty elements aggravated or reduced the risks of food supply chain. And some uncertainty elements had both positive and negative impacts in the whole sample period, as the magnitude and direction of the impacts of various uncertainties in different periods had time-varying characteristics.

Investigating the impacts of COVID-19 safety measures and related uncertainties among socially vulnerable groups in Lagos megacity.

The emergence of the unique coronavirus disease (COVID-19), associated safety measures and impacts have been experienced differently across various sociodemographic and livelihood groups. As a result of the impacts of the COVID-19 restrictions, this study examined experiences and livelihood uncertainties from socially vulnerable groups. One hundred and fifty responses (150) were recorded from residents in Iwaya, and Makoko areas within Lagos Mainland Local Government Area of Lagos state. Complete lockdown or stay-at-home orders, compulsory face masks in public spaces, curfews, physical and social distancing and restriction of inter-state movements are some of the precautionary/safety measures introduced by the Government and enforced by security agents. The findings show that curfews and restriction of inter-state movements were two of the safety measures that had no or reduced impact (p-values > 0.01) on the respondents' means of livelihood. Our results reveal that because a larger percentage of male participants are self-employed and owned personal businesses they were more affected by COVID-19 restrictions than females. 42.7% (64) of females and 57.3% (86) of males reported COVID-19-related anxieties and stress from fear of starvation, and contracting the virus, to impacts on money/finances, slow sales and businesses, food supply, job loss, erratic power supply affecting work from home options. 54.7% of respondents had more than 5 people living together, while 84.7% of housing types (128) are bungalows with several rooms inhabited by an average of three to four people per household. Increased stress, fear of hunger, loss of jobs and source of income were some of the negative impacts resulting from the introduction of the COVID-19 safety measures which adversely affected occupations like traders, people engaged in fishing activities, painters, carpenters, hairdressers and barbers, printers and bricklayers. Our work provides insights into the effects of the COVID-19-safety measures and subjective impact across vulnerable groups and occupations.