Uncertainty Factors Research Articles

The Co-volatility of Uncertainty Shock on Chinese Exchange Rate: Based on Time-frequency Domain and Time-varying Method

In recent years, with the global epidemic, the Russia-Ukraine conflict, the great power game, and so on events, the international political and economic environment has become more and more unstable, the USDCNY exchange rate fluctuates significantly, and the external uncertainty risk shocks negatively hit the foreign exchange market. Based on this, this article expands global uncertainty risk shocks into four dimensions: global geopolitical risk, U.S. economic policy uncertainty, Chinese economic policy uncertainty, and VIX index, in addition to adding international WTI crude oil and gold price as additional variables, which are significantly correlated with both, to study the linkage among uncertainty shocks, commodity volatility and USDCNY exchange rate from the time-frequency domain. Furthermore, we analyze the time-varying spillover effects among variables at different frequency scales by decomposing the data into multiple scales using the Maximum Overlap Discrete Wavelet Transform (MODWT). Findings reveal that uncertainty factors and commodity prices exert distinct influence mechanisms on the USDCNY exchange rate under different cycles of frequency fluctuations. At various time-frequency domain volatility scales, VIX and WTI generate significant risk spillovers over the USDCNY exchange rate. Moreover, when global major events occur, time-varying spillover effects of variables are also heterogeneous. The research findings presented in this paper can provide valuable insights for policymakers and investors regarding the impact of international uncertainty shocks on the Chinese exchange rate across various short, medium, and long frequencies. This understanding can aid in managing uncertainty risks in decision-making processes, ultimately facilitating the efficient functioning of the foreign exchange market.

Evaluation of MUF uncertainty based on GUM method for benchmark bulk handling facility

The Republic of Korea is performing independent national inspections under the IAEA's State System of Accounting for and Control (SSAC), and developing an evaluation methodology for the material unaccounted for (MUF) to reinforce capabilities with the purpose of assessment for the accounting system of the facility handling bulk nuclear materials. In relation to this, a new approach for MUF evaluation was proposed in this study based on the guide to the expression of uncertainty in measurement (GUM). Both the conventional MUF evaluation method and the GUM method were applied to a hypothetical list of inventory items including material balance. Considering the ease of uncertainty propagation according to the GUM, it was assumed that independent uncertainty factors correspond to random factors, while correlated uncertainty factors correspond to systematic factors. The total MUF uncertainties were similar for both methods; however, it was verified that some uncertainties were affected by the measurement procedure in the GUM method. Furthermore, the GUM method was found to be more conducive to conducting a factor analysis for the MUF uncertainty. It was therefore concluded that application of the GUM approach could be beneficial in cases of national safeguard inspections where factor analysis is required for MUF assessment.

Hydrogen refueling station location optimization under uncertainty

Hydrogen refueling stations (HRS) are essential for the growth of the hydrogen energy and fuel cell vehicle industry, making the strategic decision of their location and scale critical. This study advances the HRS location optimization problem by integrating network planning and hydrogen scheduling optimization, a novel approach that addresses both where and how big HRSs should be, alongside managing supply relations and hydrogen transportation scheduling. Uniquely, it incorporates uncertainty factors in hydrogen demand, using a two-stage multi-period stochastic programming model designed to minimize both construction and operational costs under variable demand scenarios, while also ensuring customer satisfaction. Our main contributions include the development of this comprehensive model and the introduction of an efficient metaheuristic algorithm to solve it, significantly enhancing computational efficiency. The effectiveness of our approach is demonstrated through numerical experiments, and a sensitivity analysis provides crucial insights, underlining the practical implications for HRS infrastructure development. The findings not only offer a methodological advancement in HRS location selection but also deliver tangible results that can guide industry stakeholders in strategic decision-making, marking a significant step forward in the sustainable development of hydrogen energy infrastructure.

Estimation and usefulness of measurement uncertainty from sampling at different spatial scales: microns to kilometres

Uncertainty of measurement values (MU) is crucial to their reliable geochemical interpretation. MU can be estimated using the Duplicate Method, which requires the taking of a small proportion of duplicated samples, and can be applied at any spatial scale. The distance between the duplicated samples is selected to reflect the effect of analyte heterogeneity on the measurement result (i.e. estimated concentration) within each sampling target, at the particular scale of investigation. Three published case studies, at different spatial scales, are used to explain how the Duplicate Method can be applied to estimate MU. They also illustrate how MU can be used to improve geochemical interpretation and validate measurement procedures (that include sampling) by judging their fitness for purpose. At the kilometre scale, measurements from the GEMAS survey of agricultural soils across Europe are used to estimate their MU for the first time. The MU for 53 elements range from an uncertainty factor of 1.01 to over 10. The MU contributes more that 20% to the total variance for 8 of the 53 elements, showing that the measurement procedure was not fit for purpose in those cases. At the micron scale, measurements of oxygen isotopes in candidate quartz reference materials had MU that was dominated by its sampling component, caused by sometimes unacceptable heterogeneity. A third case study of Pb in soils at 12 UK sites showed that the Duplicate Method can also be used to quantify the heterogeneity (as factor 1.03 to 2.4), and that it can indicate different possible sources of an element.

A Review of Cyclic Imines in Shellfish: Worldwide Occurrence, Toxicity and Assessment of the Risk to Consumers.

Cyclic imines are a class of lipophilic shellfish toxins comprising gymnodimines, spirolides, pinnatoxins, portimines, pteriatoxins, prorocentrolides, spiro-prorocentrimine, symbiomines and kabirimine. They are structurally diverse, but all share an imine moiety as part of a bicyclic ring system. These compounds are produced by marine microalgal species and are characterized by the rapid death that they induce when injected into mice. Cyclic imines have been detected in a range of shellfish species collected from all over the world, which raises the question as to whether they present a food safety risk. The European Food Safety Authority (EFSA) considers them to be an emerging food safety issue, and in this review, the risk posed by these toxins to shellfish consumers is assessed by collating all available occurrence and toxicity data. Except for pinnatoxins, the risk posed to human health by the cyclic imines appears low, although this is based on only a limited dataset. For pinnatoxins, two different health-based guidance values have been proposed at which the concentration should not be exceeded in shellfish (268 and 23 µg PnTX/kg shellfish flesh), with the discrepancy caused by the application of different uncertainty factors. Pinnatoxins have been recorded globally in multiple shellfish species at concentrations of up to 54 times higher than the lower guidance figure. Despite this observation, pinnatoxins have not been associated with recorded human illness, so it appears that the lower guidance value may be conservative. However, there is insufficient data to generate a more robust guidance value, so additional occurrence data and toxicity information are needed.

Planning of renewable energy regional heating system based on demand side uncertainty

Effective planning of renewable energy district heating systems is essential to facilitate the energy transition and achieve environmental goals. In the face of demand-side uncertainty, the complexity of system planning increases significantly. To address this challenge, a new approach has been adopted to optimize heating systems by precisely analyzing and managing demand-side variables. Using simulation experiments to quantitatively assess uncertainty factors, it is found that room temperature, unit ventilation, equipment power, and lighting power had significant effects on system energy consumption, ranging from −17% to 12%. According to these factors, a series of optimization measures are formulated, and then a set of optimization scheme based on demand-side uncertainty is implemented. The experimental results show that the scheme can effectively reduce the daily power output, reduce the total cost input by 7.1%, and achieve a total emission reduction of 4.0% in environmental protection. The optimization strategy proposed in this study significantly reduces energy consumption and costs, and reduces carbon emissions, providing a new solution for the environmental protection and intelligent heating system.

Connectedness across meme assets and sectoral markets: Determinants and portfolio management

This study examines the dynamic connectedness and risk spillovers between meme coins, meme stocks, and sectoral markets under different investment horizons. Further, we explore the effects of global uncertainty and risk factors on patterns of dynamic connectedness as well as the benefits of portfolio diversification under different investment strategies. Empirical results indicate that the short-, medium-, and long-term connectedness among these markets are time-varying and are highly responsive to external shocks and stress periods, with the short-term spillover dominating both medium- and long-term spillovers. Furthermore, Meme stocks and coins are net receivers of shock, along with the energy, communication services, utilities, and real estate sectors, whereas other sectors are net transmitters. On the contrary, GameStop-GME becomes net shock transmitters in the medium and long run, while Shiba Inu is a net shock transmitter in the long run. We also show that Meme stocks and Meme coins have distinct features and characteristics from the sectoral markets. They are disconnected and minimally affected by the fluctuations in the sectoral market. Finally, empirical findings show that global risk factors reduce dynamic connectedness across time horizons. These results have several implications for investors, portfolio managers, and policymakers regarding investment strategies, asset allocation, and risk management.

Do economic uncertainty and persistence in housing prices matter on mortgage insurance?

In this paper, we employ an option-pricing model that considers the effects of autocorrelation, economic policy uncertainty, and macroeconomic conditions to derive closed-form formulas of mortgage insurance (MI). When fitting the U.S. housing and mortgage data, our pricing model produces significantly well-fitting MI market quotes. Then, we design a framework to measure the magnitudes of these three effects on MI valuation. The autocorrelation effect dominates economic uncertainty and macroeconomic effects. On average, the effects of autocorrelation, economic uncertainty, and macroeconomic factors increase the MI premium rate by 65.173 bps, 14.616 bps, and 12.114 bps, respectively. During periods of heightened monetary policy uncertainty, the magnitude of the economic uncertainty effect is greater than that of the macroeconomic effects. Moreover, the magnitude of the economic uncertainty effect increases rapidly for a higher loan-to-value ratio (LTV), particularly when the LTV exceeds a threshold of 0.8.

Uncertain Facts or Uncertain Values? Testing the Distinction Between Empirical and Normative Uncertainty in Moral Judgments.

People can be uncertain in their moral judgments. Philosophers have argued that such uncertainty can either refer to the underlying empirical facts (empirical uncertainty) or to the normative evaluation of these facts itself (normative uncertainty). Psychological investigations of this distinction, however, are rare. In this paper, we combined factor-analytical and experimental approaches to show that empirical and normative uncertainty describe two related but different psychological states. In Study 1, we asked N = 265 participants to describe a case of moral uncertainty and to rate different aspects of their uncertainty about this case. Across this wide range of moral scenarios, our items loaded onto three reliable factors: lack of information, unclear consequences, and normative uncertainty. In Study 2, we confirmed this factor structure using predefined stimulus material. N = 402 participants each rated eight scenarios that systematically varied in their degree of uncertainty regarding the consequences of the described actions and in the value conflict that was inherent to them. The empirical uncertainty factors were mainly affected by the introduction of uncertainty regarding consequences, and the normative uncertainty factor was mainly affected by the introduction of value conflict. Our studies provide evidence that the distinction between empirical and normative uncertainty accurately describes a psychological reality. We discuss the relevance of our findings for research on moral judgments and decision-making, and folk metaethics.

Научное обоснование среднегодовой предельно допустимой концентрации ванадия пентоксида в атмосферном воздухе по критериям допустимого риска

Relevance of this study is determined by the sanitary-epidemiological legislation of the Russian Federation with stipulated requirements to create hygiene standards for environmental factors ensuring their safety for people. These hygienic standards should guarantee absence of impermissible lifetime health risks. Divanadium pentoxide is a chemical that should be mandatorily regulated in ambient air under long-term exposure due to its wide prevalence and high toxicity. An average annual MPL for divanadium pentoxide was established by using systemic analysis of research literature and regulatory documents. According to selection results, three key epidemiological studies were taken for further analysis. They provided evidence of adverse effects produced by divanadium pentoxide on human health (the respiratory organs in particular) under chronic inhalation exposure. When analyzing a study design, we paid special attention to description of observation groups, values of exposure and nature of its effects, adverse health outcomes caused by exposure to divanadium pentoxide as well as to a type and a value of a point of departure. We calculated a value of the total (complex) uncertainty factor in order to establish an average annual maximum permissible level of the analyzed chemical. As a result, we suggest a scientifically substantiated (among other things, as per permissible health risk levels) average annual maximum permissible level for divanadium pentoxide, which equals 0.0001 mg/m3. This level is safe for human health under lifetime exposure. It is noteworthy that this level corresponds to ‘low uncertainty’, which indicates its high safety for human health.

Scientific substantiation of average annual maximum permissible level of vanadium pentoxide in ambient air as per permissible health risk

Relevance of this study is determined by the sanitary-epidemiological legislation of the Russian Federation with stipulated requirements to create hygiene standards for environmental factors ensuring their safety for people. These hygienic standards should guarantee absence of impermissible lifetime health risks. Divanadium pentoxide is a chemical that should be mandatorily regulated in ambient air under long-term exposure due to its wide prevalence and high toxicity. An average annual MPL for divanadium pentoxide was established by using systemic analysis of research literature and regulatory documents. According to selection results, three key epidemiological studies were taken for further analysis. They provided evidence of adverse effects produced by divanadium pentoxide on human health (the respiratory organs in particular) under chronic inhalation exposure. When analyzing a study design, we paid special attention to description of observation groups, values of exposure and nature of its effects, adverse health outcomes caused by exposure to divanadium pentoxide as well as to a type and a value of a point of departure. We calculated a value of the total (complex) uncertainty factor in order to establish an average annual maximum permissible level of the analyzed chemical. As a result, we suggest a scientifically substantiated (among other things, as per permissible health risk levels) average annual maximum permissible level for divanadium pentoxide, which equals 0.0001 mg/m3. This level is safe for human health under lifetime exposure. It is noteworthy that this level corresponds to ‘low uncertainty’, which indicates its high safety for human health.

Stochastic simulation of magnetically controlled convection in porous media with random porosity via Karhunen-Loève expansion and intrusive polynomial chaos expansion

Understanding and quantifying uncertainty factors are crucial for accurately predicting thermomagnetic convection phenomena. This study presented a mathematical model and algorithm framework for uncertainty analysis of thermomagnetic convection in porous media with random porosity. The proposed method combined Karhunen-Loève expansion and intrusive polynomial chaos expansion to represent the input random parameters and output response, respectively. The Galerkin projection method was employed to decouple the stochastic governing equations of thermomagnetic convection into deterministic governing equations that can be efficiently solved using the finite volume method. By solving the polynomial chaos expansion of the output response and employing the stochastic projection method to solve the associated decoupled governing equations, the temporal evolution and statistical characteristics of the output response were obtained. The study revealed that porosity uncertainty in the porous medium affects the thermomagnetic convection of paramagnetic fluid, exhibiting significant chaos effects. Monte Carlo simulations were performed to validate the accuracy of the proposed approach and demonstrate its computational efficiency compared to traditional Monte Carlo methods. This research provides valuable insights into the uncertainty analysis of thermomagnetic convection and offers a promising methodology for analyzing heat transfer in various porous media applications.

Mapping Interindividual Variability of Toxicodynamics Using High-Throughput Transcriptomics and Primary Human Hepatocytes from Fifty Donors.

Understanding the variability across the human population with respect to toxicodynamic responses after exposure to chemicals, such as environmental toxicants or drugs, is essential to define safety factors for risk assessment to protect the entire population. Activation of cellular stress response pathways are early adverse outcome pathway (AOP) key events of chemical-induced toxicity and would elucidate the estimation of population variability of toxicodynamic responses. We aimed to map the variability in cellular stress response activation in a large panel of primary human hepatocyte (PHH) donors to aid in the quantification of toxicodynamic interindividual variability to derive safety uncertainty factors. High-throughput transcriptomics of over 8,000 samples in total was performed covering a panel of 50 individual PHH donors upon 8 to 24 h exposure to broad concentration ranges of four different toxicological relevant stimuli: tunicamycin for the unfolded protein response (UPR), diethyl maleate for the oxidative stress response (OSR), cisplatin for the DNA damage response (DDR), and tumor necrosis factor alpha () for signaling. Using a population mixed-effect framework, the distribution of benchmark concentrations (BMCs) and maximum fold change were modeled to evaluate the influence of PHH donor panel size on the correct estimation of interindividual variability for the various stimuli. Transcriptome mapping allowed the investigation of the interindividual variability in concentration-dependent stress response activation, where the average of BMCs had a maximum difference of 864-, 13-, 13-, and 259-fold between different PHHs for UPR, OSR, DDR, and signaling-related genes, respectively. Population modeling revealed that small PHH panel sizes systematically underestimated the variance and gave low probabilities in estimating the correct human population variance. Estimated toxicodynamic variability factors of stress response activation in PHHs based on this dataset ranged between 1.6 and 6.3. Overall, by combining high-throughput transcriptomics and population modeling, improved understanding of interindividual variability in chemical-induced activation of toxicity relevant stress pathways across the human population using a large panel of plated cryopreserved PHHs was established, thereby contributing toward increasing the confidence of in vitro-based prediction of adverse responses, in particular hepatotoxicity. https://doi.org/10.1289/EHP11891.

Predicting the in vivo developmental toxicity of fenarimol from in vitro toxicity data using PBTK modelling-facilitated reverse dosimetry approach

In vitro methods are widely used in modern toxicological testing; however, the data cannot be directly employed for risk assessment. In vivo toxicity of chemicals can be predicted from in vitro data using physiologically based toxicokinetic (PBTK) modelling-facilitated reverse dosimetry (PBTK-RD). In this study, a minimal-PBTK model was constructed to predict the in-vivo kinetic profile of fenarimol (FNL) in rats and humans. The model was verified by comparing the observed and predicted pharmacokinetics of FNL for rats (calibrator) and further applied to humans. Using the PBTK-RD approach, the reported in vitro developmental toxicity data for FNL was translated to in vivo dose-response data to predict the assay equivalent oral dose in rats and humans. The predicted assay equivalent rat oral dose (36.46 mg/kg) was comparable to the literature reported in vivo BMD10 value (22.8 mg/kg). The model was also employed to derive the chemical-specific adjustment factor (CSAF) for interspecies toxicokinetics variability of FNL. Further, Monte Carlo simulations were performed to predict the population variability in the plasma concentration of FNL and to derive CSAF for intersubject human kinetic differences. The comparison of CSAF values for interspecies and intersubject toxicokinetic variability with their respective default values revealed that the applied uncertainty factors were adequately protective.

Economic Policy Uncertainty, Political Connections, and Total Factor Productivity in China

Purpose – This paper aims to elucidate the complex relationship between Economic Policy Uncertainty (EPU) and Total Factor Productivity (TFP) in the context of Chinese A-share listed companies, exploring how various factors influenced this relationship from 2008 to 2020. Design/Methodology/Approach – Our research design utilizes a comprehensive dataset from Chinese A-share listed companies across various sectors to explore the impact of Economic Policy Uncertainty (EPU) on Total Factor Productivity (TFP). We analyze a range of factors, including pollution intensity, manufacturing status, market indices, institutional attention, analyst coverage, internal control mechanisms, and bank shareholding. Findings – The findings provide a nuanced understanding of how Economic Policy Uncertainty (EPU) impacts Total Factor Productivity (TFP) across Chinese A-share listed companies. Our analysis reveals that the influence of EPU on TFP is not uniform but varies significantly across different contexts, underscored by a variety of moderating factors. Among these, institutional attention and analyst coverage emerge as critical elements that shape the dynamics between EPU and TFP. Institutional attention, indicating the extent to which firms are monitored by regulatory bodies and investment institutions, plays a pivotal role in mitigating the negative effects of EPU. Research Implications – The findings provide valuable insights for policymakers and industry stakeholders, emphasizing the importance of strategic alignment in managing economic uncertainties. The study contributes to understanding the dynamics of economic policy uncertainties in a complex market like China, helping inform more effective policy and management strategies.

Application of the crow search algorithm to achieve an optimal solution for a double link manipulator

Robotic manipulators are mechanical devices that are electronically controlled and have several linkages that allow them to do specific jobs under specified situations. Here, a double-link manipulator is explored and modified using uncertainty factors that impact the robot manipulator's performance in monitoring the angular position. Unlike, the standard Lagrangian technique is employed to derive the motion equations and calculate the performance of the Two Link Robot Manipulator. Because the motion equation involves a nonlinear system of ordinary differential equations, getting to the target place is challenging. Meta-heuristic search algorithms were standard methodologie to solve any optimization problem, mainly real-time problems. Meta-heuristic algorithms depend upon the natural behaviour of the animals or birds. This paper uses a Crow search algorithm to minimize the trajectory and position of the double link manipulator. It also demonstrates how the Crow search algorithm can easily be used to solve optimal control problems (OCP) with constraints in MATLAB simulation. The Crow search algorithm depends on the crow's behaviour of keeping their excess food in a safe place and getting it as needed. The Crow search algorithm (CSA) was compared with particle swarm optimization (PSO), differential evolution (DE), and Grey Wolf Optimization (GWO). The double link manipulator's fundamental Model's performance is computed to find a solution. The Crow search method has produced notable results in terms of performance, i.e. 25 % to 98 % better, compared to other algorithms such as DE, PSO, and GWO.

Predictive modeling of carbon emissions in Jiangsu Province's construction industry: An MEA-BP approach

Understanding and predicting carbon emissions in the construction industry is crucial for sustainable development planning. In this study, we employed the GM(1,1) model and the Mind Evolution Algorithm (MEA) to optimize the BP neural network model and forecast carbon emissions in Jiangsu Province from 2022 to 2026. Using data related to energy consumption and building material usage in the Jiangsu construction industry from 1995 to 2021, we estimated carbon emissions using the life cycle assessment method and IPCC carbon emission coefficient method. Additionally, we utilized the STIRPAT model and Principal Component Analysis (PCA) to select and reduce important variables, identifying three principal components. Subsequently, the MEA-BP model was employed to provide detailed forecasts of carbon emissions over the next five years, revealing a declining trend in carbon emissions from the construction industry. The study thoroughly analyzed the direct and indirect impacts of population growth, economic development, and industrial restructuring on carbon emissions, proposing targeted policy recommendations including sustainable urban planning and green building design. Future research could enhance the applicability and accuracy of the model by expanding the dataset and considering more uncertainty factors. This research offers valuable insights into the trends and influencing factors of carbon emissions in the construction industry of Jiangsu Province, providing guidance for policymakers and future research directions.

Discriminative Grandparental Investment in China : Evidence from an Undergraduate Questionnaire Study.

Many studies in Western societies show a pattern of discriminative grandparental investment as follows: maternal grandmothers (MGMs) > maternal grandfathers (MGFs) > paternal grandmothers (PGMs) > paternal grandfathers (PGFs). This pattern is in line with the expectation from evolutionary reasoning. Yet whether or not this pattern applies in China is in question. The present study was based on a questionnaire survey at a university in Central China (N = 1,195). Results show that (1) when grandparent-grandchild residential distance during grandchildren's childhood is controlled, in the case of grandsons and granddaughters as a whole and granddaughters only, both grandparental caregiving and grandchildren's emotional closeness to grandparents display a rank order of MGM > MGF > PGM > PGF, but in the case of grandsons only, this order is not statistically significant. (2) There are stable relationships between grandparental caregiving/grandchildren's emotional closeness and residential distance/similarity in appearance. (3) The effects of residential distance on either PGFs' or PGMs' caregiving exceed those on either MGFs' or MGMs'. (4) The PGF and PGM prefer grandsons to granddaughters in their caregiving, whereas the MGF and MGM do not have a sex preference, and (5) the fact that the PGF and PGM invest more in grandsons than in granddaughters does not depend on grandsons' duration of living in a rural area. Our results suggest that (1) in general, the Chinese display a pattern of differential grandparental investment predicted by an evolutionary perspective, (2) the evolutionary perspective that combines the two factors of paternal uncertainty and sex-specific reproductive strategies is applicable to grandparental investment in China, and (3) the traditional son-preference culture also plays some role in affecting grandparental investment in China, though the roles of culture and urban-rural cultural difference should not be exaggerated.

SIGNIFICANT FACTORS IN CALIBRATION FOR 60 CALIPERS: A EXPERIMENTAL DATA

Caliper calibration is essential for accurate dimensional measurements, especially in precision- driven industries. This research investigates the significant factors affecting the calibration of 60 calipers through experimental data analysis. The study systematically examines environmental conditions, instrument design, operator techniques, and technological advances as potential contributors to measurement uncertainties. The temperature variations explored reveal environmental factor fluctuations in caliper dimensions and their influence on subsequent measurement uncertainties. It was also observed that analysis of caliper structural integrity, manufacturing tolerances and overall design explained their roles in measurement variability. Sources of variability were identified by tested operator techniques. Empirical data and mathematical modeling were used to quantify and understand the uncertainty factors identified in the study. The findings provide valuable insight into precision measurement and calibration, along with practical recommendations for improving caliper measurement reliability in industrial settings. The study informs calibration practices, advancing the pursuit of dimensional measurement accuracy. Key Words: significant factors, experimental data, calibration of callipers, dimensional measurement, environmental conditions

Bitcoin halving and the integration of cryptocurrency and forex markets: An analysis of the higher-order moment spillovers

This paper introduces a flexible semi-nonparametric model, the so-called SNP-VSK model, to analyze the higher-order moment interactions among cryptocurrencies and the forex markets, i.e., accounting for conditional volatility, skewness, and kurtosis spillovers. These transmissions among cryptos are identified considering Bitcoin halving as a factor of price uncertainty. To study the degree of integration of cryptocurrency markets in the economy, spillover effects with the forex market are considered, showing that Bitcoin and Ethereum are the prime receivers of positive volatility transmission, a sign of integration with the currency markets, but also potential sources of financial instability.