Further evidence of contagion effect between the Chinese and the G20 stock markets during the COVID-19 pandemic: A time-varying copula approach

This paper examines the presence of a contagion effect between Chinese and G20 stock markets as well as its intensity over a recent period from 1(st) January 2013 to 7 April 2022. The empirical study is conducted using the time-varying copula approach. The obtained results show strong evidence of a contagion effect between China and all countries except United States America, Argentina and Turkey during the COVID-19 period. In particular, the Chinese stock market exhibits the highest level of dependence with the Asian and European stock markets in addition to the greatest variability in dependence. These findings are interesting and have important implications for several financial applications.

Bitcoin and money supply relationship: An analysis of selected country economies

Bitcoin has become quite known after the 2008 economic crisis and the COVID-19 health crisis. For some, these cryptocurrencies constitute rebellion against the existing system as governments encourage uncontrolled expansions in the money supply;for some others, it is a quick source of income. Undeniably, the volume of the crypto money market has grown considerably in recent years, regardless of the reasoning of the people who invest and trade in this field. At this point, one of the most important questions to be investigated is "what variables have caused the tremendous growth in the crypto money quantities in recent years?" This study tests the assumption that changes in cryptocurrencies are affected by changes in national currencies. Thus, the Bitcoin price is the dependent variable, and M1 monetary supply changes in the USA, European Union and Japanese economies are considered independent variables. The variables in this study were tested using the time-varying Granger causality method. The results obtained from this study confirm the philosophy of Bitcoin's emergence and the possibility that it can be a hedge against the inflationary effects of money, especially after the COVID-19 pandemic.

Pass-through with volatile exchange rates and inflation targeting

As central banks struggle against high inflation in the aftermath of the Covid-19 pandemic and the war in the Ukraine, it is essential to understand the open economy aspects of inflation determination. Using a Bayesian VAR with time-varying parameters and stochastic volatility, we analyze the behavior of pass-through across time and in relation to macroeconomic variables. Pass-through increases with the size of the volatility of the exchange rate and the level, variance and persistence of shocks to domestic prices, which is in line with theory. The persistence of exchange rate shocks is associated with higher pass-through only for observations with low inflation. Furthermore, the effect of inflation persistence on pass-through is much higher for exchange rate appreciations than for depreciations.

Considering the temporal interdependence of human mobility and COVID-19 concerning Indonesia's large-scale social distancing policies.

The year 2020 has marked the beginning of a new life in which humans must struggle and adapt to coexist with a new coronavirus, known as COVID-19. Population density is one of the most significant factors affecting the speed of COVID-19's spread, and it is closely related to human activity and movement. Therefore, many countries have implemented policies that restrict human movement to reduce the risk of transmission. This study aims to identify the temporal dependence between human mobility and virus transmission, indicated by the number of active cases, in the context of large-scale social restriction policies implemented by the Indonesian government. This analysis helps identify which government policies can significantly reduce the number of active COVID-19 cases in Indonesia. We conducted a temporal interdependency analysis using a time-varying Gaussian copula, where the parameter fluctuates throughout the observation. We use the percentage change in human mobility data and the number of active COVID-19 cases in Indonesia from March 28, 2020, to July 9, 2021. The results show that human mobility in public areas significantly influenced the number of active COVID-19 cases. Moreover, the temporal interdependencies between the two variables behaved differently according to the implementation period of large-scale social distancing policies. Among the five types of policies implemented in Indonesia, the policy that had the most significant influence on the number of active COVID-19 cases was several restrictions during the Implementation of Restrictions on Community Activities (Pelaksanaan Pembatasan Kegiatan Masyarakat/PPKM) period. We conclude that the strictness of rules restricting social activities generally affected the number of active COVID-19 cases, especially in the early days of the pandemic. Finally, the government can implement policies that are at least equivalent to the rules in PPKM if, in the future, cases of COVID-19 spike again.

A country-specific COVID-19 model

To dynamically measure COVID-19 transmissibility consistently normalized by population size in each country.A reduced-form model enhanced from the classical SIR is proposed to stochastically represent the Reproduction Number and Mortality Rate, directly measuring the combined effects of viral evolution and population behavioral response functions.Evidences are shown that this e(hanced)-SIR model has the power to fit country-specific empirical data, produce interpretable model parameters to be used for generating probabilistic scenarios adapted to the still unfolding pandemic.Stochastic processes embedded within compartmental epidemiological models can produce measurables and actionable information for surveillance and planning purposes.

LSTM-based Forecasting using Policy Stringency and Time-varying Parameters of the SIR Model for COVID-19

Accurate forecasting of the number of infections is an important task that can allow health care decision makers to allocate medical resources efficiently during a pandemic. Two approaches have been combined, a stochastic model by Vega et al. for modelling infectious disease and Long Short-Term Memory using COVID-19 data and government's policies. In the proposed model, LSTM functions as a nonlinear adaptive filter to modify the outputs of the SIR model for more accurate forecasts one to four weeks in the future. Our model outperforms most models among the CDC models using the United States data. We also applied the model on the Canadian data from two provinces, Saskatchewan and Ontario where it performs with a low mean absolute percentage error. © 2023 IEEE.

Can Consumer Credit Stimulate Consumer Confidence? Evidence from the Time-Varying Aspect

This study discusses the nexus between consumer credit (CC) and consumer confidence (CF) in the case of China with a bootstrap rolling-window causality test. The new empirical results demonstrate that CC improves CF in specific periods by loosening liquidity constraints and increasing consumer power temporarily. Meanwhile, a negative link is also found, which can be explained by policy adjustment and financial instabilities. On the contrary, CF negatively influences CC in some periods, reflecting consumers' attitudes toward the future would change borrowing behaviors. But this relationship would be disrupted by government intervention and public events such as the COVID-19 pandemic. The contribution is that time-varying, multiple-directional and dynamic causalities are captured, which enriches the theoretical framework between CC and CF. Therefore, the government must design and adjust loaning policies against specific circumstances and transmit positive signs to consumers. Future study needs to pay attention to different types of CC and try to reveal their heterogeneous influences on CF. In addition, the effect evaluation for CC policy is also another focus in the next research.

Connectedness between emerging stock markets, gold, cryptocurrencies, DeFi and NFT: Some new evidence from wavelet analysis

This paper examines the dynamic connectedness between Gulf countries and BRICS stocks markets with a sample of cryptocurrencies, as well as two newly developed digital assets, namely NFT and DeFi, and Gold. The period under examination spans from January 2019 until September 2022. Our analysis is based on wavelet coherence, which is a suitable methodology considering the nonlinear dynamics present in data. Our empirical results clearly identify nontrivial time-varying connectedness between different assets and the stock markets. Asymmetric patterns in the interconnections of newly developed digital assets, cryptocurrencies, Gold and emerging market indices are well-documented, especially during the advent of the health and political events. Our empirical findings have relevant implications for portfolio managers, investors and researchers about portfolio allocation, investment strategies and potential diversification benefits of NFT and DeFi digital assets. © 2023 The Author(s)

Effect of green bonds, oil prices, and COVID-19 on industrial CO2 emissions in the USA: Evidence from novel wavelet local multiple correlation approach

This study explores the effect of green bonds, oil prices, and the coronavirus disease 2019 (COVID-19) pandemic on industrial carbon dioxide (CO2) emissions. In this context, this study examines the United States of America (USA), which is the biggest economy in the world, uses weekly data between March 6, 2020 and September 30, 2022, and applies a novel wavelet local multiple correlation (WLMC) approach under time-varying and frequency-varying perspective. The novel empirical findings shows that (i) there is a strong negative (positive) co-movement between industrial CO2 emissions and green bonds in the short-run (long-run);(ii) there is a strong positive (negative) co-movement between industrial CO2 emissions and oil price in the medium-run (long-run);(iii) there is a strong negative (positive) co-movement between industrial CO2 emissions and the COVID-19 pandemic in the medium-run (long-run);(iv) the oil price is the dominant factor, whereas there are changing effect of the variables on each other at different times and frequencies;and (vi) overall, there are long-run asymmetric and dynamic correlations between industrial CO2 emissions and variables. Hence, the empirical results highlight the asymmetric, time-varying, and frequency-varying effects of green bonds, oil prices, and the COVID-19 pandemic on industrial CO2 emissions by presenting fresh and novel evidence. Moreover, the study proposes policy implications for the USA government. © The Author(s) 2023.

Systemic Risk of China's Financial Industry during the Spread of the COVID-19 Epidemic and the Breakdown of Crude Oil Negotiation

This research first adopts three indicators to measure the systemic risk of different financial industries in China. Second, we employ the Time Varying Parameter-Stochastic Volatility-Vector Auto Regression (TVP-SV-VAR) model to investigate the time-varying relationship among COVID-19 epidemic, crude oil price, and financial systemic risk. The results herein not only help us grasp the current level of systematic risk in China, but also can assist at improving the early warning risk indicators and enhance the risk management system. Lastly, this research can also help investors to make reasonable asset planning.

An Optimal Vaccine Allocation Model Considering Vaccine Hesitancy and Efficacy Rates Among Populations

Vaccines need to be urgently allocated in pandemics like the ongoing COVID-19 pandemic. In the literature, vaccines are optimally allocated using various mathematical models, including the extensively used Susceptible-Infected-Recovered epidemic model. However, these models do not account for the time duration concerning multi-dose vaccines, time duration from infection to recovery or death, the vaccine hesitancy (i.e., delay in acceptance or refusal of vaccination), and vaccine efficacy (i.e., the time-varying protection capability of the vaccine). To make the vaccine allocation model more applicable to reality, this paper presents an optimal model considering the above mentioned time duration concerning multi-dose vaccination, time duration from infection to recovery or death, hesitancy rates, efficacy levels, and also breakthrough rates - the rates at which individuals get infected after vaccination. This vaccine allocation model is constructed using a revised Susceptible-Infected-Recovered model. The concept of people∗week infections is introduced to measure the number of infected people within a certain time duration, and in this paper, the amount of people∗week infections is minimized by the proposed vaccine allocation model. Our case study of the New York State 2021 population of 19,840,000 shows that this optimal allocation method can avoid 0.05%2.75% people∗week infections than the baseline allocation method when 2 to 11 million vaccines are optimally allocated. In conclusion, the obtained optimal allocation method can effectively reduce people∗week infections and avoid vaccine waste when more vaccines are available. © 2013 IEEE.

The interconnectedness across risk appetite of distinct investor types in Borsa Istanbul

PurposeThis study aims to investigate the interconnectedness across the risk appetite of distinct investor types in Borsa Istanbul. This study also examines the causal impact of global implied volatility indices on the risk appetite of these investor groups.Design/methodology/approachThe authors use a novel time-varying frequency connectedness framework of Chatziantoniou et al. and a new time-varying Granger causality test with a recursive evolving procedure by Shi et al. over June 2008 and July 2022.FindingsThe results show a high level of interconnectedness across the risk appetite of different investor types. The sizable spillovers to domestic types of investors either occur from professional or foreign investors, indicating the long-term dominant effect of foreign and more qualified investors on the domestic investors in Borsa Istanbul. The authors provide significant evidence of causality from the global implied volatility to the Borsa Istanbul risk appetite indices, which are getting stronger after the COVID-19 outbreak.Originality/valueUnlike the previous studies, the authors analyze the risk appetite sub-indices of various types of investors to reveal behavioral distinctions and interconnectedness across them. The authors use a novel econometric framework to assess investors' risk appetite in different investment horizons in a time-varying system. Together with volatility index (VIX), the authors also use volatilities of oil (OVX), gold (GVZ) and currency (EVZ), considering the information transmission not only from stock markets but also energy, metals and currency markets. The present data set covers significant financial crises, socioeconomic events and the COVID-19 outbreak.

The time-varying impact of uncertainty on oil market fear: Does climate policy uncertainty matter?

Implied volatility index is a popular proxy for market fear. This paper uses the oil implied volatility index (OVX) to investigate the impact of different uncertainty measures on oil market fear. Our uncertainty measures consider multiple perspectives, specifically including climate policy uncertainty (CPU), geopolitical risk (GPR), economic policy uncertainty (EPU), and equity market volatility (EMV). Based on the time-varying parameter vector autoregression (TVP-VAR) model, our empirical results show that the impact of CPU, GPR, EPU, and EMV on OVX is time-varying and heterogeneous due to these uncertainty measures containing different information content. In particular, the CPU has become increasingly important for triggering oil market fear since the recent Paris Agreement. During the COVID-19 pandemic, CPU, EPU, and EMV, rather than GPR, play a prominent role in increasing oil market fear. © 2023 Elsevier Ltd

How do carbon, stock, and renewable energy markets interact: Evidence from Europe

In a low-carbon context, the connectedness among carbon, stock, and renewable energy markets has been strengthening. This study examines the effect of Brexit, the launch of the European Green Deal and the COVID-19 pandemic on the connectedness among carbon, stock, and renewable energy markets by employing Time Varying Parameter -Vector Auto Regression (TVP-VAR). First, equal interval impulse response analysis shows that in the short term, the renewable energy market suffers from a positive shock from the carbon market and this shock gradually decreases from the initial 1.6×10−3. In the long run, the connectivity between the carbon market and the stock market, and between the carbon market and the renewable energy market is almost 0. Second, we can conclude that the positive connectivity between stock market to carbon market and renewable energy market to carbon market is enhanced by COVID-19 in the short term, with values of 7.5×10−3 and 3.6×10−3 respectively. Finally, renewable energy market received a greater negative impact from the carbon market during COVID-19 than during the release of the European Green Deal, while Brexit allowed positive carbon price spillover to renewable energy price. © 2023 Elsevier Ltd

Analyzing the network structure of risk transmission among renewable, non-renewable energy and carbon markets

The linkage of renewable, non-renewable energy and carbon markets is increasing, and there is a complex network structure for the risk transmission among multiple markets. Based on the methods of network topology analysis and DY spillover index, this paper analyzes the time-varying spillover effect and network structure of risk transmission among renewable, non-renewable energy and carbon markets. The results show that: according to the static spillover index, there are significant spillover effects among renewable, non-renewable energy and carbon markets, and they are asymmetric. Moreover, the total spillover index further shows that the spillover effect between energy and carbon markets is time-varying, especially during the extreme events. Specifically, the net spillover index shows that the spillover effects among renewable, non-renewable energy and carbon markets are bidirectional, asymmetric and time-varying. Additionally, under the influence of various extreme events, the spillover effect and network structure of risk transmission among renewable, non-renewable energy and carbon markets are heterogeneous. Compared with the shale oil revolution and the Sino-US trade dispute, the influence of COVID-19 is more significant and complex, and it is long-term and comprehensive. Finally, some policy implications for preventing risk transmission and optimizing the energy structure to promote emission reduction are put forward. [ FROM AUTHOR] Copyright of Renewable Energy: An International Journal is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

DOES THE COVID-19 MEDIA COVERAGE AFFECT AH PREMIUM DISPARITY?

This article explores the dynamic causality between the COVID-19 Media Coverage Index (MCI) in China (Chinese mainland and Hong Kong) and the AH premium index (both price and volatility) by applying a novel time-varying causality technology. Our findings show that the MCIs in China do not significantly cause the log-prices of the AH premium index throughout the full sample period, whereas significantly positive and time-varying causalities from the MCIs in China to the volatilities of the AH premium index are detected. The results thus provide evidence that the change of the MCIs does not lead to a wider or narrower AH premium but unidirectionally causes the change of its volatilities. Furthermore, the effect of MCIs in Chinese mainland on the AH premium volatilities is more pronounced and stable compared to that in Hong Kong, which indicates that the AH premium disparity is more sensitive to the media coverage in the Chinese mainland than in Hong Kong. Finally, the causal relationship from the MCIs in China to the AH premium volatilities disappears after November 2021. Our results provide implications for policymakers to decrease the fluctuation of the AH premium by effectively guiding the trend of media coverage;the results also remind AH stock investors to pay more attention to the COVID-19 media coverage. © 2023, Bucharest University of Economic Studies. All rights reserved.

Research on the time-varying effects among green finance markets in China: A fresh evidence from multi-frequency scale perspective

Green finance is an essential instrument for achieving sustainable development. Objectively addressing correlations among different green finance markets is conducive to the risk management of investors and regulators. This paper presents evidence on the time-varying correlation effects and causality among the green bond market, green stock market, carbon market, and clean energy market in China at multi-frequency scales by combining the methods of Ensemble Empirical Mode Decomposition Method (EEMD), Dynamic Conditional Correlation (DCC) GARCH model, Time-Varying Parameter Vector Autoregression with Stochastic Volatility Model (TVP-VAR-SV), and Time-varying Causality Test. In general, the significant negative time-varying correlations among most green finance markets indicate a prominent benefit of risk hedging and portfolio diversification among green financial assets. In specific, for different time points and lag periods, the green finance market shock has obvious time-varying, positive and negative alternating effects in the short-term scales, while its time delay and persistence are more pronounced in the medium-term and long-term scales. Interestingly, a positive event shock will generate positive connectivity among most green finance markets, whereas a negative event including the China/U.S. trade friction and the COVID-19 pandemic may exacerbate the reverse linkage among green finance markets. Furthermore, the unidirectional causality of "green bond market - carbon market - green stock and clean energy markets” was established during 2018–2019. © 2023

A Time-Varying Copula Approach to Investigate the Dependence Structures of BRICS Stock Markets Before and After COVID-19

In this study, changes in the dependence structures of BRICS countries' stock markets before and after the World Health Organization's Covid-19 emergency declaration were examined using the time-varying (TV) copula method. The return series of the stock markets were divided into two periods, namely before COVID-19 (BC) and after COVID-19 (AC). The novel time-varying flexy copula (TVFC) proposed in the study provides a more flexible structure and produces better results than the TV single copula and the TV optimal copula in comparative analyses. In addition, risk spillovers between market indices were explored using the CoVaR-Copula method. According to the results, the interdependence coefficients of all countries were higher in the AC period than in the BC period with the exception of the China – South Africa pair. Moreover, the dependence coefficient of India with other BRICS countries was quite high compared to the other pairs. Based on the risk spillover results, it was concluded that the Chinese stock market index was the index least affected by the other BRICS countries in the AC period compared to the BC period.

Assessing epidemic curves for evidence of superspreading.

The expected number of secondary infections arising from each index case, referred to as the reproduction or R number, is a vital summary statistic for understanding and managing epidemic diseases. There are many methods for estimating R ; however, few explicitly model heterogeneous disease reproduction, which gives rise to superspreading within the population. We propose a parsimonious discrete-time branching process model for epidemic curves that incorporates heterogeneous individual reproduction numbers. Our Bayesian approach to inference illustrates that this heterogeneity results in less certainty on estimates of the time-varying cohort reproduction number R t . We apply these methods to a COVID-19 epidemic curve for the Republic of Ireland and find support for heterogeneous disease reproduction. Our analysis allows us to estimate the expected proportion of secondary infections attributable to the most infectious proportion of the population. For example, we estimate that the 20% most infectious index cases account for approximately 75%-98% of the expected secondary infections with 95% posterior probability. In addition, we highlight that heterogeneity is a vital consideration when estimating R t .

Analysis of the COVID-19 pandemic using a compartmental model with time-varying parameters fitted by a genetic algorithm.

Analyzing the COVID-19 pandemic is a critical factor in developing effective policies to deal with similar challenges in the future. However, many parameters (e.g., the actual number of infected people, the effectiveness of vaccination) are still subject to considerable debate because they are unobservable. To model a pandemic and estimate unobserved parameters, researchers use compartmental models. Most often, in such models, the transition rates are considered as constants, which allows simulating only one epidemiological wave. However, multiple waves have been reported for COVID-19 caused by different strains of the virus. This paper presents an approach based on the reconstruction of real distributions of transition rates using genetic algorithms, which makes it possible to create a model that describes several pandemic peaks. The model is fitted on registered COVID-19 cases in four countries with different pandemic control strategies (Germany, Sweden, UK, and US). Mean absolute percentage error (MAPE) was chosen as the objective function, the MAPE values of 2.168%, 2.096%, 1.208% and 1.703% were achieved for the listed countries, respectively. Simulation results are consistent with the empirical statistics of medical studies, which confirms the quality of the model. In addition to observables such as registered infected, the output of the model contains variables that cannot be measured directly. Among them are the proportion of the population protected by vaccines, the size of the exposed compartment, and the number of unregistered cases of COVID-19. According to the results, at the peak of the pandemic, between 14% (Sweden) and 25% (the UK) of the population were infected. At the same time, the number of unregistered cases exceeds the number of registered cases by 17 and 3.4 times, respectively. The average duration of the vaccine induced immune period is shorter than claimed by vaccine manufacturers, and the effectiveness of vaccination has declined sharply since the appearance of the Delta and Omicron strains. However, on average, vaccination reduces the risk of infection by about 65-70%.