Time-varying Behavior Research Articles

Dynamic Modeling of a HeXe-Cooled Mobile Nuclear Reactor with Closed Brayton Cycle

Helium-xenon (HeXe)-cooled mobile nuclear reactors have promising potential in future low-carbon energy systems. However, there is currently a lack of fast and reliable tools for analyzing the complicated dynamic characteristics of such systems. In this study, we developed a comprehensive dynamic modeling approach for a HeXe-cooled nuclear power system coupled with a closed Brayton cycle (CBC). The system’s key components, including the reactor, printed circuit heat exchanger (PCHE), and turbomachinery, are lumped-modeled to capture their time-varying behavior. A step-solving algorithm that incorporates HeXe mass conservation iteration is designed. The verification results demonstrate that the dynamic program is robust and reliable, with each time step converging within 25 iterations and the HeXe mass remaining within the range of 3.755 ± 0.01 kg throughout the simulation meeting the law of mass conservation. Then, a 1500 s frozen start-up simulation for the coupled system is conducted, in which the CBC is started in the first 500 s by increasing the main shaft speed to 40% of the rated value, and then the reactor is started by inserting external reactivity between 500 and 800 s. Both the dynamic process and the steady-state performance after the start-up are analyzed. The results show that the system achieved a stable electrical output of 5.7 MWe with a thermal efficiency of 32.5%. This study lays a solid foundation for future work aimed at improving the overall efficiency and performance of HeXe-cooled nuclear power systems.

Research on freeze resistance and life prediction of Desert sand–Crushed stone fine aggregate concrete

Despite the global abundance of Desert sand (DS), its application in engineering remains limited. This application can help alleviate challenges related to high unit costs and the scarcity of natural fine aggregate supplies. Freeze resistance experiments were conducted on C30 concrete incorporating varying proportions of DS, including 0 %, 25 %, 50 %, 75 %, and 100 %. DS was mixed with Tuff crushed stone fine aggregate (Manufactured sand, referred to as MS). The freeze durability of the composite fine aggregate concrete was comprehensively evaluated from three perspectives: surface morphology, a macroscopic analysis including mass loss and relative dynamic elastic modulus (RDEM), and microscopic examinations encompassing pore structure observation and scanning electron microscope (SEM) imaging. Utilizing the damage degree index of the dynamic elastic modulus and the Weibull distribution model, the life prediction process was conducted to elucidate the time-varying behavior of DS-MS concrete. According to the test findings, the concrete specimen DS25-MS75 demonstrates the highest freeze resistance. The mass loss rate shows a pattern of initial decrease followed by an increase with higher desert sand admixture, whereas the RDEM initially increases and then decreases. Optimal desert sand admixture levels can prevent and delay concrete degradation, thereby enhancing its freeze resistance. Excessive desert sand content leads to increased water demand during the curing process, resulting in elevated porosity and facilitating the formation of voids. Under the action of freezing and thawing cycles in water, DS25 %-MS75 % concrete has the longest expected service life with an actual life of 101 years, while DS100 %-MS0 % concrete has the shortest service life with an actual life of only 54.5 years.

Graph neural network-based multiscale thermal modeling for heterogeneous materials with complex structures

This study presents a novel GNN-based model for multiscale thermal characterization of heterogeneous materials with complex microstructures. It incorporates relationships between composition, structure, and thermal transport across multiple scales, leveraging the structure of GNNs. Results demonstrate enhanced performance of the GNN-based model compared with existing methods. Specifically, the GNN model improved thermal conductivity prediction with a mean absolute error of 0.18 W/mK (15 % improvement over the Bayesian neural network-based model) and provided a 30x speedup in computation time. The model successfully connected atomic-scale interactions to macroscale properties, achieving less than 5 % error in predicting effective thermal conductivity from one scale to another. Furthermore, it demonstrated the ability to capture nonlinear thermal transport phenomena with 92 % accuracy. The model also improved the prediction of the transient thermal responses by 20 %, accurately capturing the time-varying behaviour of materials. This work also proved the model’s ability to handle new material systems with transfer learning, achieving 88 % accuracy. Finally, Bayesian neural networks integrated into the model provided uncertainty quantification, with 95 % of experimental measurements falling within the predicted bounds. This work demonstrated the ability of the framework to handle complex microstructures, nonlinear phenomena, and transient responses while maintaining a computationally efficient model that can enable real-time applications in design and optimization needed in modern materials innovation. It represents a powerful tool for data-driven multiscale mechanics that could accelerate materials research and development for the aerospace, electronics cooling, and additive manufacturing industries.

Developed square-root cubature Kalman filter-based solution for improving power system state estimation with unknown inputs and non-Gaussian noise

Understanding the ever-changing dynamics of power systems is crucial, and dynamic state estimation (DSE) plays a vital role in achieving this. However, traditional nonlinear Kalman filters (NKFs) face limitations: lack of access to control inputs and presence of non-Gaussian noise in measurements, impacting their accuracy and robustness. This research introduces a novel robust DSE method that tackles these challenges head-on. For the first time in DSE, it leverages the predictive power of Holt-Winters Triple Exponential Smoothing to model the time-varying behavior of control inputs. This innovative approach allows for the simultaneous estimation of dynamic state variables such as the rotor angle and rotor speed changes, as well as transient voltages and control inputs like mechanical input torque and excitation voltage, even in the presence of non-Gaussian noise. Furthermore, the method employs modified projection statistics and a Cauchy function. This unique combination effectively bounds the influence of observation outliers while maintaining high statistical estimation efficiency. This innovative approach utilizes a square cubature Kalman filter (SCKF) for enhanced numerical stability. Extensive simulations under various anomalous conditions demonstrate the method's superior accuracy and efficiency in estimating the state vector. These results highlight its potential to significantly improve power system estimation and pave the way for real-time applications.

Multi-state Markovian-random walk adaptive filter for time-varying block sparse system identification

This paper presents a higher order multi-state Markov model for block sparsity model of a system impulse response. To capture the time-varying behavior of the system, a random walk model for temporal evolution is incorporated; resulting in a two-dimensional multi-state Markov-Random walk model across spatial and temporal domains. Additionally, a block batch data structure is utilized for the adaptive filter design. The proposed adaptive filter is aimed to find the Maximum A Posteriori (MAP) estimator of the batch time-varying impulse response. The associated optimization problem is demonstrated to be convex and efficiently solvable through the Steepest-Descent (SD) approach. Furthermore, the final recursion of the proposed adaptive filter is derived and a mean convergence analysis of the algorithm is provided. Simulation results show the effectiveness of the proposed algorithm under severe time-varying conditions of the system impulse response, where alternative algorithms may exhibit divergence or poor convergence. Synthetic experiments and real-world experiments using acoustic channel estimation further validate the superiority of the proposed algorithm.

Calendar anomalies’ adaptiveness in exchange rates: evidence from the concordance coefficient and AR-GARCH tests

PurposeThis research aims to examine the time-varying behavior of the Weekend, Turn-of-the-Month, January, and Halloween effects in eight foreign exchange rates against the U.S. dollar from the Adaptive Market Hypothesis (AMH) perspective. It also explores whether these anomalies can generate excess returns compared to a buy-and-hold strategy.Design/methodology/approachUsing daily return data from January 2004 to December 2023 in a rolling-window framework, the study employs the Concordance Coefficient test and AR-GARCH models to assess the time-varying behavior of four calendar anomalies. It also assesses the statistical significance of the trading strategies implied by these anomalies using t-tests and applies F-tests for subperiod analysis.FindingsThe results reveal a generalized time-varying presence of calendar anomalies in emerging currencies and, to a lesser extent, developed currencies. However, the trading strategies implied by these anomalies generally did not show statistical significance, except for the Turn-of-the-Month effect, which exhibited statistically significant unprofitability.Originality/valueThe study pioneers an analysis of five calendar anomalies across various currencies from the standpoint of the AMH and proposes case-specific explanations for their occurrence. It also examines the potential for the anomalies’ implied trading strategies to generate excess returns compared to a straightforward buy-and-hold strategy. Additionally, the study introduces the recently developed Concordance Coefficient test as a valuable alternative to other non-parametric methods.

Return and volatility connectedness between agricultural tokens and us equity sectors

This study investigates the return and volatility interconnectedness between emerging digital assets, specifically agricultural tokens, and U.S. equity sectors using the TVP-VAR model. Analyzing data from August 7, 2020, to January 2, 2024, the findings indicate modest interconnections between agricultural tokens and U.S. sectors, with time-varying behavior. Notably, return interconnectedness is generally stronger than volatility, except at the sample period's outset, where volatility dominates. Return spillovers predominantly drive the connectedness system, though agricultural tokens uniquely act as net recipients of both return and volatility spillovers, while U.S. equity sectors mainly transmit spillovers. Optimal portfolio analysis, utilizing portfolio weights and hedge ratios, reveals that incorporating agricultural tokens offers portfolio diversification benefits and enhances hedging performance. Investors are advised to frequently adjust portfolios to maximize diversification and hedging gains. These findings provide significant portfolio implications for policymakers, market participants, and investors.

Prediction on the time-varying behavior of tunnel segment gaskets under compression

The compression force is a critical indicator for assessing the waterproofing capability of ethylene propylene diene monomer (EPDM) rubber gaskets, and the relaxation of EPDM gaskets under compression is a key factor contributing to water leakage in shield tunnels. In this study, detailed accelerated aging tests of gaskets with different precompression openings were conducted. Analytical method and simulation method for predicting the long-term performance of EPDM gaskets considering their different compression states were proposed. First, the compression curve of the gasket was divided into three stages to analyze the mechanism of compression force degradation in depth. Under the action of a compression force, the unrecoverable deformation caused by open holes shortened stage III of the compression curve, which constituted the primary factor influencing gasket compression force relaxation. In addition, the Arrhenius equation was used to establish a conversion relationship between the accelerated aging test conditions and actual service time, and an analytical expression (AE) method for predicting the compression force degradation pattern of gaskets was proposed. Finally, a finite element (FE) method based on the relaxation constitutive model was proposed, enabling simulation of deteriorating tunnel segment gasket performance under complex compression states. Then, the effectiveness and accuracy of the proposed relaxation model were validated by comparing the compression force curves and deformation states obtained from experiments and simulations. Based on the time-varying relaxation constitutive parameters, the variation in the compression force of EPDM gaskets with different joint openings over time was simulated. The results show that the minimum compression force retention rates of gaskets after 100 years of service predicted by the AE method and FE method are 28.5 % and 30.6 %, respectively. This study provides two effective means for predicting the long-term mechanical performance of compressed EPDM gaskets, offering pivotal insights for long-term waterproofing design of shield tunnel joints.

“What’s yours is mine”: Partners’ everyday emotional experiences and cortisol in older adult couples

The existing literature consistently finds that emotional experiences and cortisol secretion are linked at the within-person level. Further, relationship partners tend to covary in emotional experience, and in cortisol secretion. However, we are only beginning to understand whether and how an individuals’ emotions are linked to their relationship partners’ cortisol secretion. In this project, we harmonized data from three intensive measurement studies originating from Canada and Germany to investigate the daily dynamics of emotions and cortisol within 321 older adult couples (age range=56–87 years). Three-level multilevel models accounted for the nested structure of the data (repeated assessments within individuals within couples). Actor-Partner Interdependence Models were used to examine the effect of own emotional experiences (actor effects) and partner emotional experiences (partner effects) on momentary and daily cortisol secretion. Adjusting for age, sex, education, comorbidities, assay version, diurnal cortisol rhythm, time spent together, medication, and time-varying behaviors that may increase cortisol secretion, results suggest that higher relationship partner’s positive emotions are linked with lower momentary cortisol and total daily cortisol. Further, this association was stronger for older participants and those who reported higher relationship satisfaction. We did not find within-couple links between negative emotions and cortisol. Overall, our results suggest that one’s relationship partner’s positive emotional experience may be a protective factor for their physiological responding, and that these more fleeting and day-to-day fluctuations may accumulate over time, contributing to overall relationship satisfaction.

Tail Risk Dynamics under Price-Limited Constraint: A Censored Autoregressive Conditional Fréchet Model.

This paper proposes a novel censored autoregressive conditional Fréchet (CAcF) model with a flexible evolution scheme for the time-varying parameters, which allows deciphering tail risk dynamics constrained by price limits from the viewpoints of different risk preferences. The proposed model can well accommodate many important empirical characteristics of financial data, such as heavy-tailedness, volatility clustering, extreme event clustering, and price limits. We then investigate tail risk dynamics via the CAcF model in the price-limited stock markets, taking entropic value at risk (EVaR) as a risk measurement. Our findings suggest that tail risk will be seriously underestimated in price-limited stock markets when the censored property of limit prices is ignored. Additionally, the evidence from the Chinese Taiwan stock market shows that widening price limits would lead to a decrease in the incidence of extreme events (hitting limit-down) but a significant increase in tail risk. Moreover, we find that investors with different risk preferences may make opposing decisions about an extreme event. In summary, the empirical results reveal the effectiveness of our model in interpreting and predicting time-varying tail behaviors in price-limited stock markets, providing a new tool for financial risk management.

Futures Leads the Spot but Why not so When Market in Shocks? A Time-Varying Price Discovery of Indian Precious Metals

The study empirically measures the average and time-varying contribution of the spot and futures market to the price discovery of benchmark Indian precious metals from June 2005 to March 2023. The two most popular precious metals in India, i.e., Gold and Silver, were selected for the study. The average contribution is measured through VECM (Vector Error Correction Model) and CFW (common factor weights). Additionally, for getting time-varying contribution, VECM is reformulated into state space form and Kalman filter and smoothening are applied for computing time-varying CFW. Findings reveal that price discovery depicts time-varying behaviour during the study period. While most of the time, futures lead the spot, in case of any shocking news, the spot market dominates the futures as noise trading happens in the futures market for both precious metals, i.e., Gold and Silver. Furthermore, futures market volume, volatility, and COVID-19 negatively affect the price discovery in both precious metals.

Time-dependent model for two-phase flow in ultra-high water-cut reservoirs: Time-varying permeability and relative permeability

For the ultra-high water-cut reservoirs, after long-term water injection exploitation, the physical properties of the reservoir change and the heterogeneity of the reservoir becomes increasingly severe, which further aggravates the spatial difference of the flow field. In this study, the displacement experiments were employed to investigate the variations in core permeability, porosity, and relative permeability after a large amount of water injection. A relative permeability endpoint model was proposed by utilizing the alternating conditional expectation (ACE) transformation to describe the variation in relative permeability based on the experimental data. Based on the time dependent models for permeability and relative permeability, the traditional oil-water two-phase model was improved and discretized using the mimetic finite difference method (MFD). The two cases were launched to confirm the validation of the proposed model. The impact of time-varying physical features on reservoir production performance was studied in a real water flooding reservoir. The experimental results indicate that the overall relative permeability curve shifts to the right as water injection increases. This shift corresponds to a transition towards a more hydrophilic wettability and a decrease in residual oil saturation. The endpoint model demonstrates excellent accuracy and can be applied to time-varying simulations of reservoir physics. The impact of variations in permeability and relative permeability on the reservoir production performance yields two distinct outcomes. The time-varying permeability of the reservoir results in intensified water channeling and poor development effects. On the other hand, the time-varying relative permeability enhances the oil phase seepage capacity, facilitating oil displacement. The comprehensive time-varying behavior is the result of the combined influence of these two parameters, which closely resemble the actual conditions observed in oil field exploitation. The time-varying simulation technique of reservoir physical properties proposed in this paper can continuously and stably characterize the dynamic changes of reservoir physical properties during water drive development. This approach ensures the reliability of the simulation results regarding residual oil distribution.

A Systematic Approach of Global Sensitivity Analysis and Its Application to a Model for the Quantification of Resilience of Interconnected Critical Infrastructures

We consider a model for the resilience analysis of interconnected critical infrastructures (ICIs) that describes the dependencies among the subsystems within the ICIs and their time-varying behavior. The model response is a function of uncertain inputs comprising ICIs design parameters and failure magnitudes of vulnerable elements in the system, etc. In this methodological paper, we present a systematic approach based on an innovative blend of methods to perform a sensitivity analysis for identifying the most relevant variables affecting the system resilience at different stages, during a disruptive event. The methods considered include the following: the use of the graphical representation of Cusunoro curves for a visualization of the impact of an input on the resilience metric and an understanding of whether the associated dependence is monotonic, increasing, or decreasing; the introduction of an ensemble of indicators related to different properties of the resilience metric to allow the prioritization of variable importance and avoid false negatives, meaning to regard a variable as non-influential when, instead, it plays a relevant role in the determination of the model response; the calculation of first-order variance-based sensitivity indices to have an appreciation on the relevance of interactions when inputs are independent; and a data approach to visually identify relevant second-order interactions. All the sensitivity methods considered are performed on a provided sample, and do not require additional model evaluations. They allow the analyst to post-process the data to extract, simultaneously, several desirable insights. The systematic approach proposed to apply these methods allows us to identify the model input variables and parameters that are not very relevant, while it enables the identification of the relevant ones which allows prioritizing interventions on the vulnerable elements of the system for its resilience at different stages during a disruptive event. Given the methodological nature of the work, a simplified infrastructure model describing an interconnected gas network and electric power grid is taken as case study: this allows us to show that the approach is straightforward to understand and implement, and the results obtained show the usefulness of the approach in providing meaningful insights that can be used by stakeholders and decision makers to inform strategies for the improvement of system resilience. By the application of the simplified ICIs model to the case study, it is shown that the approach can be straightforwardly implemented to identify the most relevant variables on system resilience and obtain the most important subsystems. The key factors which affect system resilience in multiple initial failures scenarios are found; this allows us to identify the key resilience improvement measurements, and their priorities.

Space wandering in the rodent default mode network

The default mode network (DMN) is a large-scale brain network known to be suppressed during a wide range of cognitive tasks. However, our comprehension of its role in naturalistic and unconstrained behaviors has remained elusive because most research on the DMN has been conducted within the restrictive confines of MRI scanners. Here, we use multisite GCaMP (a genetically encoded calcium indicator) fiber photometry with simultaneous videography to probe DMN function in awake, freely exploring rats. We examined neural dynamics in three core DMN nodes-the retrosplenial cortex, cingulate cortex, and prelimbic cortex-as well as the anterior insula node of the salience network, and their association with the rats' spatial exploration behaviors. We found that DMN nodes displayed a hierarchical functional organization during spatial exploration, characterized by stronger coupling with each other than with the anterior insula. Crucially, these DMN nodes encoded the kinematics of spatial exploration, including linear and angular velocity. Additionally, we identified latent brain states that encoded distinct patterns of time-varying exploration behaviors and found that higher linear velocity was associated with enhanced DMN activity, heightened synchronization among DMN nodes, and increased anticorrelation between the DMN and anterior insula. Our findings highlight the involvement of the DMN in collectively and dynamically encoding spatial exploration in a real-world setting. Our findings challenge the notion that the DMN is primarily a "task-negative" network disengaged from the external world. By illuminating the DMN's role in naturalistic behaviors, our study underscores the importance of investigating brain network function in ecologically valid contexts.

Tracking Modal Parameters of Structures Online Using Recursive Stochastic Subspace Identification under Ambient Excitations

Continuous and autonomous system identification is an alternative to regular inspection during operations, which is essential for structural integrity management (SIM) as well as structural health monitoring (SHM). In this regard, online (or real-time) system identification techniques that have recently received considerable attention can be used to assess the current condition and performance during operations and, in the meantime, can be utilized to detect any damage or deterioration. For example, stochastic subspace identification (SSI), based on recursive formulation, has proven its capability in tracking modal parameters as well as time-variant dynamic behaviors. This study proposes the implementation of recursive SSI (RSSI) using the matrix inversion lemma to track slow time-varying parameter changes under ambient excitations. Subsequently, some investigations for practical implementation are examined and discussed. For verifying the reliability of SHM applications based on the proposed methods, two datasets measured from different experiments are exploited to identify the modal parameters reclusively. The results from both numerical simulations and experimental investigations demonstrated the effectiveness of tracking the modal parameters exhibiting time-varying dynamic characteristics under white noise excitations (or ambient excitations).

Transient topology optimization for efficient design of actively cooled microvascular materials

Microvascular materials containing internal microchannels are able to achieve multi-functionality by flowing different fluids through vasculature. Active cooling is one application to protect structural components and devices from thermal overload, which is critical to modern technology including electric vehicle battery packaging and solar panels on space probes. Creating thermally efficient vascular network designs requires state-of-the-art computational tools. Prior optimization schemes have only considered steady-state cooling, rendering a knowledge gap for time-varying heat transfer behavior. In this study, a transient topology optimization framework is presented to maximize the active-cooling performance and mitigate computational cost. Here, we optimize the channel layout so that coolant flowing within the vascular network can remove heat quickly and also provide a lower steady-state temperature. An objective function for this new transient formulation is proposed that minimizes the area beneath the average temperature versus time curve to simultaneously reduce the temperature and cooling time. The thermal response of the system is obtained through a transient Geometric Reduced Order Finite Element Model (GRO-FEM). The model is verified via a conjugate heat transfer simulation in commercial software and validated by an active-cooling experiment conducted on a 3D-printed microvascular metal. A transient sensitivity analysis is derived to provide the optimizer with analytical gradients of the objective function for further computational efficiency. Example problems are solved demonstrating the method’s ability to enhance cooling performance along with a comparison of transient versus steady-state optimization results. In this comparison, both the steady-state and transient frameworks delivered different designs with similar performance characteristics for the problems considered in this study. This latest computational framework provides a new thermal regulation toolbox for microvascular material designers.

The impact of the Russo-Ukraine conflicts on price discovery on the financial markets

ABSTRACT This paper investigates the response of financial markets to two significant events within the Russo-Ukraine conflicts: the November 2013 Euromaidan protest and the February 2022 Russian invasion of Ukraine. Employing diverse methods, including event studies, price discovery models, and time-varying parameter regression, we analyse abnormal returns, price discovery, time-varying herding behaviour, and market dynamics across various financial instruments, such as stock indices, currencies, and commodities. Overall, the Russian invasion of Ukraine had a more pronounced impact on financial markets compared to the Euromaidan protest. Moreover, during both events, the US dollar and yen consistently functioned as safe-haven assets. The findings also reveal variations in the responses of different assets to the conflicts, indicating potential effects on market efficiency, investor perceptions, and expectations.

Study on time-varying characteristics and flow behavior of microencapsulated polymer

Due to the challenges posed by high injection pressure and significant viscosity shear loss, conventional polymer flooding methods are unable to effectively meet the urgent demand for enhanced oil recovery in medium- and low-permeability reservoirs. Microencapsulated polymer flooding is a promising solution to address the aforementioned challenges due to its advantages of easy wellbore injection, shear resistance, and delayed thickening. However, there is a lack of research on the release behavior and time-dependent characteristics of microencapsulated polymer and its impact on flow behavior in reservoirs. To address this gap, we conducted a series of experiments, including thermal stability tests, microfluidic chip experiments, and micro-scale oil displacement experiments, to reveal the time-varying behavior generated by the triggered release of microencapsulated polymers. The results showed that microencapsulated polymer can be fully triggered after 8 days of aging at 85 °C, with a viscosity exceeding the initial viscosity by more than 25 times. The release mechanism of the polymer aligned with the Ritger-Peppas model, transitioning from super Case-II transport to anomalous transport, and finally to Fickian diffusion with increasing temperature. During the triggering process, the presence of particles stabilized shear and improved oil displacement, particularly in small pore throats. Higher triggering degrees of the solution resulted in greater flow resistance within the microchannel, effectively suppressing fluid fingering. The oil displacement effectiveness of fully triggered microencapsulated polymer was comparable to that of conventional polymers. This study provides insights into the flow behavior of microencapsulated polymer at a microscopic level, offering valuable references for its application in the field.

Coupling quantitative ultrasound using echo envelop statistics and shear wave propagation to provide new image contrast of mimicked liver lesions

In quantitative ultrasound, biomarkers are sensitive to the scatterers’ positioning and number density, and therefore, shear wave (SW) propagation may modify their time-varying behaviors and provide new image contrast. Herein, we considered echo envelop statistics by using one parameter of the homodyned-K distribution (HKD): the diffuse-to-total signal power ratio 1/(κ+1). A liver lesion-mimicking phantom was simulated with an inclusion and a background of elastic moduli of 40 and 4 kPa, and scatterers’ number densities per resolution cell of 15 and 5, respectively. Ten sets of spatially correlated scatterers were created, and a plane SW was simulated to reposition time and spatially varying scatterers according to the SW motion. Ultrasound images were simulated using MUST with added Gaussian noise to attain 20-dB SNR. Dynamic and static analyses were made over 30 frames with and without SW motion, respectively. Validation was based on the contrast of 1/(κ+1) between the inclusion and the background. HKD image contrast increased with SW motion with values from 0.12 ± 0.10 to 0.48 ± 0.16 (no units) (p < 0.001). Results suggest that the variation in scatterers’ organization under SW propagation may provide new information over its static counterpart to enhance the contrast of liver lesions.

Evidence of Geopolitical Risk and Economic Policy Uncertainty on Equity Prices in Emerging Markets

This paper presents evidence of the impact of changes in downside risk , economic policy uncertainty ( ), the geopolitical risk ( ) in six Asian equity markets while controlling for COVIDE-19. Applying the month data for the period of 1990.01– 2021.12 to Asian markets, evidence shows that national stock returns are negatively correlated with and , suggesting that these risk/uncertainty factors harmfully affect stock markets. Evidence shows that a rise in U.S. geopolitical risk adversely spillovers to Asia stock markets. Spreads of COVID-19 damage local stock returns via interacting with changes in EPU and GPR. Purpose: Present evidence of COVIDE-19 and the impact of changes in downside risk , economic policy uncertainty ( ), the geopolitical risk ( ) with different forms of nonlinear specifications in six Asian equity markets. Design/methodology/approach: The rolling regression method is used to derive time-varying coefficients of investors’ response to changes of policy uncertainty, which is sensitive to react to other uncertainty variables. Findings: Evidence shows that national stock returns are negatively correlated with and , suggesting that these risk/uncertainty factors adversely affect local stock markets. In addition, the evidence shows that a rise in U.S. geopolitical risk ( ) can adversely spillover to domestic stock markets. A spillover effect is found from COVID_19 development. Research limitations/implications: The findings are based on data from six Asian markets. More data may be collected, which would expand the analysis to a broader coverage of the global market. Practical implications: Evidence suggests that in addition to the risk from financial markets, the geopolitical risk (GPR) and COVID-19 are important factors in determining whether a risk premium should be received. From a policymaker’s point of view, geopolitical stability is the foundation for maintaining financial market stability. Originality/value: This paper presents evidence on the time-varying behavior of investors’ response to In addition to and the , the nonlinear components of , U.S. and interaction to COVID-19 have a harmful effect on national stock prices in Asian markets.