Uncertainty Index Research Articles

Divergent jump characteristics in brown and green cryptocurrencies: The role of energy-related uncertainty

We examine jump, positive-jump, negative-jump, and co-jump behaviors in brown (energy-intensive) and green (energy-efficient) cryptocurrency markets. We further investigate how the newly proposed energy-related uncertainty index (EUI) correlates with jumps and co-jumps of these two types of cryptocurrencies. Our results show that jumps have decreased in recent years, signaling an improvement in market efficiency. Brown cryptocurrencies are more likely to jump than green cryptocurrencies, especially in negative jumps. When more than two cryptocurrencies experienced jumps concurrently, most co-jumps were in a negative direction (80%), with a large proportion being contributed by brown cryptocurrencies (61.7%). Finally, the higher the level of EUI, the higher (lower) the likelihood of a positive (negative) jump in brown (green) cryptocurrencies; however, co-jumps' likelihood is positively correlated with EUI for only brown cryptocurrencies. These results provide crucial insights into revealing jump dynamics, guiding tail risk, and linking energy-related uncertainty to jump behavior in brown and green cryptocurrency markets.

Asymmetric effects of world uncertainty index on the demand for money in Nigeria

Asymmetric effects of world uncertainty index on the demand for money in Nigeria

On the partial impact of uncertainties on the nexus between macroeconomic fundamentals in West Africa

We investigate the degree of interconnectedness between stock returns and exchange rate returns, and the influence of some selected global uncertainty indices on such a relationship within a time-frequency domain in West Africa through the bi and partial wavelet approaches. The analysis was based on monthly observations from February 2013 to June 2023. The results highlight a negative correlation between stock return and exchange rates. The partial wavelet analysis evidence a significant effect of the global economic policy uncertainty, the implied oil market volatility, and the United States volatility index in driving the co-movements observed in the currency and stock markets. We also find a significant impact of the stock market on the currency market, underscoring the need for robust stock market policies. It is recommended that policymakers prioritize strategies aimed at boosting stock market stability and depth which can positively affect the currency markets. The significant influence of global uncertainties or shocks should not be disregarded in the formulation of policies regarding exchange rates and stock return integration at various investment horizons.

Research on China insurance demand forecasting: Based on mixed frequency data model.

In this paper, we introduce the mixed-frequency data model (MIDAS) to China's insurance demand forecasting. We select the monthly indicators Consumer Confidence Index (CCI), China Economic Policy Uncertainty Index (EPU), Consumer Price Index (PPI), and quarterly indicator Depth of Insurance (TID) to construct a Mixed Data Sampling (MIDAS) regression model, which is used to study the impact and forecasting effect of CCI, EPU, and PPI on China's insurance demand. To ensure forecasting accuracy, we investigate the forecasting effects of the MIDAS models with different weighting functions, forecasting windows, and a combination of forecasting methods, and use the selected optimal MIDAS models to forecast the short-term insurance demand in China. The experimental results show that the MIDAS model has good forecasting performance, especially in short-term forecasting. Rolling window and recursive identification prediction can improve the prediction accuracy, and the combination prediction makes the results more robust. Consumer confidence is the main factor influencing the demand for insurance during the COVID-19 period, and the demand for insurance is most sensitive to changes in consumer confidence. Shortly, China's insurance demand is expected to return to the pre-COVID-19 level by 2023Q2, showing positive development. The findings of the study provide new ideas for China's insurance policymaking.

How do climate policy uncertainty and renewable energy and clean technology stock prices co-move? evidence from Canada

AbstractThis work probes the dynamic co-movement between the Climate Policy Uncertainty Index (CPU) and the Renewable Energy and Clean Technology Index (RECT) employing the novel wavelet power spectrum (WPS) and wavelet coherence (WC) approaches for monthly data between 2013 and 2022. Using the wavelet approach enables us to observe the causality direction from both time and frequency dimensions and also to help detect the causal linkage in the short-medium and long-term horizons. This is the first study aiming to perform this relationship from both time and frequency dimensions. Remarkably, findings reveal that: i) CPU seems only volatile in 2019 and 2021 in the short run; (ii) there was significant volatility in the RECT in the short and long terms (SLT) between 2018 and 2022; (iii) RECT significantly caused the CPU between 2014 and 2018; iv) after 2019, CPU started to cause RECT in the short and medium terms (SMT).

Impact of climate risk on energy market risk spillover: Evidence from dynamic heterogeneous network analysis

Using meteorological monitoring data from eight climate disasters across the globe, the Climate Policy Uncertainty (CPU) Index, and the financial data of eight energy markets between 2010 and 2020, this study employs the fixed base extreme difference entropy method to construct a Climate Risk Index (CRI) and investigates the impact of climate risk on the risk spillover among energy markets based on dynamic heterogeneous network analysis. The results confirm that the CRI fluctuated periodically and exhibited an upward trend during the sample period. In terms of risk spillover, the oil, geothermal, and fuel cell markets act as risk receivers, whereas the gas, wind, and bioenergy markets functions as risk emitters. Climate risk significantly increases the network complexity between energy markets and the risk spillover of gas, solar, water, and wind markets but decreases the risk spillover of oil, bioenergy, fuel cells, and geothermal markets. Additionally, the fossil fuel market is more sensitive to climate risk than is the renewable energy market.

Energy market uncertainties and exchange rate volatility: A GARCH-MIDAS approach

In this paper, we employ the generalized autoregressive conditional heteroscedasticity-mixed data sampling (GARCH-MIDAS) framework to forecast the daily volatility of 19 dollar-based exchange rate returns based on monthly metrics of oil price uncertainty (OPU), and relatively broader global and country-specific energy market-related uncertainty indexes (EUI). We find that the global EUIs tend to perform better than the OPU, highlighting the need to look beyond the oil market to capture energy related uncertainties. The country-specific EUIs outperform the benchmark in a statistically significant manner for at least 14 currencies across the short-, medium-, and long-term forecasting horizons.

Why do firms list their shares in the US? The role of political uncertainty

PurposeThis paper investigates the effect of political uncertainty on the decision to cross-list in the United States (US).Design/methodology/approachTo reach our paper aim, we use a sample of 589 non-US firms cross-listed in the US for the period from 2000 to 2019. We perform logit regression and use several political uncertainty proxies, including US election presidential years, political voting margin and the political uncertainty index from Baker et al. (2002), as a continuous measure of general political condition (Francis et al., 2021).FindingsWe find the following results. Non-US firms are less likely to cross-list their shares when US political uncertainty is high. We also find that the decision to cross-list is driven by price informativeness as a channel that can explain the role of political uncertainty. Our results are robust to the endogeneity concern. In addition, we find that political administration (Democrats vs Republicans) significantly affects the decision to cross-list. More particularly, we show that firms are more likely to cross-list their shares in the US when Democrats win the elections. Moreover, we find that cross-listed firms exhibit lower valuation compared to their non-cross-listed peers when US political uncertainty is high.Originality/valueUsing a unified framework of non-US firms cross-listed in the US, this paper contributes to different strands of the literature. Our first main contribution adds to the literature on cross-listing by providing, in our knowledge, the first evidence regarding the relation between cross-listing and political uncertainty. We add to the existing literature by showing that US political uncertainty significantly determines the decision to cross-list and value creation for cross-listed firms. Whether and how managers alter their strategic decision behavior in such settings is less clear. Hence, our paper contributes to the literature by documenting how political uncertainty impacts cross-listing decision and shapes management guidance decisions. Second, this study joins a growing body of literature that examines the real impact of economic policy uncertainty (EPU) on economic outcomes. We provide empirical evidence suggesting that cross-listed firms exhibit lower valuation during period of high political uncertainty due to decreased price informativeness.

Economic Policy Uncertainty and Firms’ Non-GAAP Reporting

Using the US economic policy uncertainty (EPU) index developed by Baker, S. R., Bloom, N., & Davis, S. J. [2016. Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593–1636. https://doi.org/10.1093/qje/qjw024], we examine the impact of policy uncertainty on firms’ non-GAAP reporting practices. We show that EPU increases both the likelihood and quality of non-GAAP earnings disclosures. These effects are more pronounced for firms with higher index ownership and for those with less informative GAAP earnings. Furthermore, we show that non-GAAP disclosures provide tangible economic benefit during periods of increasing EPU, including compensating for the reduced informativeness of GAAP earnings and reducing information asymmetry. Overall, our evidence suggests that non-GAAP reporting is a means by which managers communicate value-relevant information about their firms’ performance to investors during periods of high EPU.

National culture and corporate investment: does uncertainty matter?

PurposeOur purpose is to explore how culture’s impact on investment is depending on uncertainty levels.Design/methodology/approachThis study investigates the interplay between national culture, uncertainty, and corporate investment decisions. Focusing on the uncertainty avoidance dimension (HUA) from Hofstede’s culture framework and utilizing the World Uncertainty Index (WUI) as a measure of uncertainty, this research explores how culture’s impact on investment is depending on uncertainty levels.FindingsOur results reveal that high HUA countries lower long-term investment during periods of heightened uncertainty, particularly in riskier investments like R&D, rather than capital expenditure. This relation is more pronounced for smaller firms. The findings suggest that HUA is associated with less risk taking, primarily when uncertainty is high. Furthermore, we demonstrate that the interaction between HUA and uncertainty exerts more significant and consistent effects on corporate investment than other cultural dimensions, religion, and various formal institutions, contrary to prevailing literature.Originality/valueThis study looks at the relationship between national culture and corporate investment under ambiguity, and what are the implications for risk taking. If national culture is related to riskier investments, such as R&D, relative to safer investments, like capital expenditure this would imply that risk taking is explaining the relationship between national culture and corporate investment. This relation should be clear during uncertain times. This is the first study to include the moderating effects of the level of uncertainty on the relation between national culture and corporate investment (or financial decisions in general).

Automated segmentation of MRI white matter hyperintensities in 8,421 patients with acute ischemic stroke.

To date, only a few small studies have attempted deep learning-based automatic segmentation of white matter hyperintensity (WMH) lesions in patients with cerebral infarction, which is complicated because stroke-related lesions can obscure WMH borders. We developed and validated deep learning algorithms to segment WMH lesions accurately in patients with cerebral infarction, using multisite datasets involving 8,421 patients with acute ischemic stroke. We included 8,421 stroke patients from 9 centers in Korea. 2D UNet and SE-Unet models were trained using 2,408 FLAIR MRI from 3 hospitals and validated using 6,013 FLAIR MRIs from 6 hospitals. WMH segmentation performance was assessed by calculating DSC, correlation coefficient, and concordance correlation coefficient compared to a human-segmented gold standard. In addition, we obtained an uncertainty index that represents overall ambiguity in the voxel classification for WMH segmentation in each patient based on the Kullback-Leibler divergence. In the training dataset, the mean age was 67.4±13.0 years and 60.4% were men. The mean (95% CI) DSCs for Unet in internal testing and external validation were respectively 0.659 (0.649-0.669) and 0.710 (0.707-0.714), which were slightly lower than the reliability between humans (DSC=0.744; 95% CI=0.738-0.751; P=.031). Compared with the Unet, the SE-Unet demonstrated better performance, achieving a mean DSC of 0.675 (0.666-0.685; P<.001) in the internal testing and 0.722 (0.719-0.726; P<.001) in the external validation; moreover, it achieved high DSC values (ranging from 0.672 to 0.744) across multiple validation datasets. We observed a significant correlation between WMH volumes that were segmented automatically and manually for the Unet (r=0.917, P<.0001) and even stronger for the SE-Unet (r=0.933, P<.0001). The SE-Unet also attained a high concordance correlation coefficient (ranging from 0.841 to 0.956) in external test datasets. In addition, the uncertainty indices in the majority of patients (86%) in the external datasets were below 0.35, with an average DSC of 0.744 in these patients. We developed and validated deep learning algorithms to segment WMH in patients with acute cerebral infarction using the largest-ever MRI datasets. In addition, we showed that the uncertainty index can be used to identify cases where automatic WMH segmentation is less accurate and requires human review. WMH = white matter hyperintensity; CNN = convolutional neural networks; SE = squeeze-and-excitation; KL = Kullback-Leibler; ReLU = rectified linear unit; LKW = last known well; mRS = modified Rankin Scale; NIHSS = National Institute of Health Stroke Scale; LAA = large artery atherosclerosis; SVO = small vessel occlusion; CE = cardioembolism.

‘Making text talk’: The minutes of the Central Bank of Brazil and the real economy

This paper investigates the relationship between the views expressed in the minutes of the meetings of the Central Bank of Brazil's Monetary Policy Committee (COPOM) and the real economy. It applies various linguistic machine learning algorithms to construct different measures of the uncertainty contained in the minutes of the COPOM. To achieve this, we first infer the content of the paragraphs of the minutes with Latent Dirichlet Allocation (LDA). Secondly, we build an uncertainty index for the minutes with Word Embedding and K-Means. Thirdly, we create two topic-uncertainty indices. The first topic-uncertainty index is constructed from paragraphs with a higher probability of topics related to general economic conditions. The second topic-uncertainty index is built from paragraphs with a higher probability of topics related to inflation and the monetary policy decision. Then, via a Structural VAR, we explore the lasting effects of these uncertainty indices on some Brazilian macroeconomic variables. Our results show that an unexpected increase in the minutes' uncertainty leads to a depreciation of the exchange rate and a decline in industrial production and retail trade. Moreover, we show that a positive shock to the general economic conditions topic-uncertainty index leads to higher inflation, whereas a positive shock to the inflation and monetary policy decision topic-uncertainty index leads to lower inflation.

Bayesian multivariate receptor model and convolutional neural network to identify quantitative sources and spatial distributions of potentially toxic elements in soils: A case study in Qingzhou City, China

Determining sources and spatial distributions of potentially toxic elements (PTEs) is a crucial issue of soil pollution survey. However, uncertainty estimation for source contributions remains lack, and accurate spatial prediction is still challenging. Robust Bayesian multivariate receptor model (RBMRM) was applied to the soil dataset of Qingzhou City (8 PTEs in 429 samples), to calculate source contributions with uncertainties. Multi-task convolutional neural network (MTCNN) was proposed to predict spatial distributions of soil PTEs. RBMRM afforded three sources, consistent with US-EPA positive matrix factorization. Natural source dominated As, Cr, Cu, and Ni contents (78.5 %∼86.1 %), and contributed 37.1 %, 61.0 %, and 65.9 % of Cd, Pb, and Zn, exhibiting low uncertainties with uncertainty index (UI) < 26.7 %. Industrial, traffic, and agricultural sources had significant influences on Cd, Pb, and Zn (30.2 %∼61.9 %), with UI < 39.3 %. Hg originated dominantly from atmosphere deposition (99.1 %), with relatively high uncertainties (UI=87.7 %). MTCNN acquired satisfactory accuracies, with R2 of 0.357–0.896 and nRMSE of 0.092–0.366. Spatial distributions of As, Cd, Cr, Cu, Ni, Pb, and Zn were influenced by parent materials. Cd, Hg, Pb, and Zn showed significant hotspot in urban area. This work conducted a new approach exploration, and practical implications for soil pollution regulation were proposed.

Citizens' cultural values and urban green spaces in Colombia: An experimental analysis

Urban green spaces (UGS), such as urban forests, green corridors, and parks, are vital for societal well-being and environmental health. Despite extensive research on the economic value of parks, our understanding of how cultural values affect the appreciation of urban green spaces within parks remains limited. To address this, we conducted a Discrete Choice Experiment (DCE) focusing on revitalizing a key urban park in Colombia and developed four cultural indexes. Our investigation revealed that the majority of these indexes had a significant impact on households' preferences for increased access to such areas. Specifically, households' utility increases significantly when they have more urban green spaces available, and when their indexes for environmental collectivity, long-term orientation, and femininity increase simultaneously. However, the uncertainty index showed no significant effect on household utility. Based on these findings, we recommend specific policy actions. Supporting environmental collectivity through volunteer programs and community projects aimed at conserving urban green spaces is crucial. It is essential to promote environmental education and raise awareness of pro-environmental measures for future generations. Additionally, it is important to promote gender equality through awareness-raising, education, and policies that ensure equitable participation in decision-making.

Influence of geological uncertainty and soil spatial variability on tunnel deformation and their importance evaluation

In geotechnical engineering, geological uncertainty and the inherent spatial variability of soil properties are two primary sources of uncertainty, significantly impacting embedded geo-structures. While the influence of the soil spatial variability and geological uncertainty has been separately investigated, the coupling effect of these two types of uncertainties on tunnel is still limited. Meanwhile, little effort has been devoted to quantifying the degree of importance of these two types of uncertainties. This study quantitatively evaluates the effects of geological uncertainty and soil spatial variability on tunnels, and first assess the importance of these two uncertainties based on a novel approach. Improved coupled Markov chain model was utilized to simulate the geological uncertainty based on sparse and limited boreholes. The coefficient of variation (COV) was adopted to characterize the different levels of soil spatial variability in this study. The results illuminated the role of geological uncertainty and spatial variability in tunnel probabilistic analysis by comparing different boreholes schemes and COV. In addition, the geological uncertainty index (GUI) was presented to quantitatively estimate the degree of uncertainty in the simulated strata. A novel approach for evaluating the significance of geological uncertainty and soil spatial variability on tunnel was proposed. Based on the Kendall correlation coefficient, the classification was also given, divided into high, strong, moderate, weak and no correlation. The geological uncertainty should be considered in high and strong correlation groups. Both two types of uncertainties should be simultaneously taken into account in moderate and weak correlation divisions.

Assessing dynamic co-movement of news based uncertainty indices and distance-to -default of global FinTech firms

Contemporary literature does not offer evidence on the causal relationship of default risk and investor sentiments for FinTech sector. We bridge this gap by assessing the dynamic co-movement between Distance-to-Default of leading global FinTech firms and three uncertainty indices namely Twitter Market Uncertainty Index-ENG, Twitter Economic Uncertainty Index-ENG, and US daily news-based Economic Policy. The sample period is from 07 April 2016–30 June 2021. The estimations are performed using Wavelet based analytical framework. The results reveal high dynamic co-movement of Twitter based uncertainty indices with Distance-to-Default of FinTech firms. However, there is very little co-movement between economic policy uncertainty and FinTech firms’ stability. The novelty of this work lies in the utilization of National University of Singapore’s Distance-to-Default measure, which is available in daily frequency and capable of performing dynamic data analysis. Our results offer maiden evidence of linkage of default risk and news based uncertainty indices.

A multi-view ensemble clustering approach using joint entropy

Multi-view clustering can capture complementary and consistent information from different views, which is a core research topic in the fields of machine learning and pattern recognition. However, existing multi-view clustering methods ignore the quality of each base clustering cluster, which may affect the clustering performance. In this study, a multi-view ensemble clustering approach is proposed using joint entropy to evaluate the base clustering clusters. Firstly, an uncertainty index of base clustering clusters is defined using joint entropy which characterizes the importance and quality of each cluster. Secondly, a weighted co-association matrix is constructed using the uncertainty index, and useless entries are removed from the matrix, making the co-association matrix more reasonable. Thirdly, a candidate clusters selection strategy based on the stability index and a Multi-view Ensemble Clustering (MvEC-DoS) algorithm is proposed. In the end, experiments on five benchmark datasets validate the efficacy of our approach compared to other state-of-the-art methods.

The impact of cryptocurrency-related cyberattacks on return, volatility, and trading volume of cryptocurrencies and traditional financial assets

We investigate the impact of cryptocurrency-related cyberattacks on the cryptocurrency market and traditional financial markets. The dataset consists of historical cyberattack data and trading data for twenty cryptocurrencies, three cryptocurrency uncertainty indices, five payment companies, four stock indices, a commodity index, and gold. We find that cyberattacks are associated with negative returns, increased volatility, and increased trading volume not only for the cryptocurrencies but also for the payment companies, the financial and technology sectors, and the general stock market. However, the impact of cyberattacks on cryptocurrencies has been decreasing over time, while the impact on payment companies and the financial sector has been increasing. Moreover, gold prices have shown a positive response to these cyberattacks. These results underscore the need for enhanced cybersecurity measures in the fintech sector and may inform both policymakers and market participants.

Can Economic, Geopolitical and Energy Uncertainty Indices Predict Bitcoin Energy Consumption? New Evidence from a Machine Learning Approach

This paper explores the predictive power of economic and energy policy uncertainty indices and geopolitical risks for bitcoin’s energy consumption. Three machine learning tools, SVR (scikit-learn 1.5.0),CatBoost 1.2.5 and XGboost 2.1.0, are used to evaluate the complex relationship between uncertainty indices and bitcoin’s energy consumption. Results reveal that the XGboost model outperforms both SVR and CatBoost in terms of accuracy and convergence. Furthermore, the feature importance analysis performed by the Shapley additive explanation (SHAP) method indicates that all uncertainty indices exhibit a significant capacity to predict bitcoin’s future energy consumption. Moreover, SHAP values suggest that economic policy uncertainty captures valuable predictive information from the energy uncertainty indices and geopolitical risks that affect bitcoin’s energy consumption.

Monetary policy and uncertainty spillovers: Evidence from a wavelet and frequency connectedness analysis

Interconnected financial markets, especially those stemming from changes in monetary policy or shifts in financial, political, or geopolitical conditions, ease the transmission of spillovers. Understanding these dynamics is crucial for policymakers and investors. Therefore, identifying cross-country spillovers across time and frequency domains, along with assessing the role of monetary policy during periods of economic instability and escalating geopolitical risk, holds significant importance. This study contributes to the literature by revealing important aspects of network connectedness among monetary policy and uncertainty indices, in terms of frequency and time horizon, for the US and the UK, over the period 2014–2023. First, we find stronger interdependence between the series in the long term, with short-medium run connectedness prevailing during specific periods and events, such as Brexit, the COVID-19 pandemic, and the Russian-Ukrainian War. Second, shadow short rates often receive spillovers from financial stress in the short-medium term but transmit spillovers in the long term, implying the stabilizing influence of monetary policy in the long run. Third, geopolitical risk is responsible for most of the spillovers reflected in financial and economic uncertainty in 2022, and finally, the US emerges as the dominant transmitter of spillovers to the UK.