Volatility Spillovers between Commodity and Financial Markets

The paper analyzes price volatility spillovers between commodity and financial markets order to investigate the interconnectedness and market integration and their potential in port-risk diversification. The paper analyzes gold and silver prices, oil prices, and the exchange of the Euro and British pound using the Diebold-Yilmaz spillover index methodology for-frequency weekly data from 1988 to 2020. The total spillovers between commodities and exchange rates were found to be 25.7% and the volatility spillover index during the analyzed period mostly ranged between 25% and 50% with extremes during the global financial crisis and during COVID-19 pandemic. This indicates a strong integration of commodity and financial markets, especially in crisis periods. Also, the results of the work suggest that silver price movements are affected by spillovers from other markets and therefore silver can be used to diversify risks. contribution of the paper to the existing literature is as follows: Firstly, the analysis of transmis-processes showed significant volatility spillovers between commodity markets and exchange indicating the existence of integration between different markets. Furthermore, a long period time is analyzed and the dynamic analysis shows intensified volatility spillovers in global crises periods. Secondly, the results of the analysis can help professional forecasters in forecasting and financial analysts to provide a comprehensive investment analysis. Managers and investors can thus design optimal protection instruments against unwanted movements in the financial and commod-markets. Investors benefit from portfolio diversification, and the information content obtained volatility spillover analysis can be used to assess potential determinants of future risk-adjusted returns, which would help them make investment decisions.

Could Cryptocurrency Policy Uncertainty Facilitate US Carbon Neutrality?

Investigating the essential impact of the cryptocurrency market on carbon emissions is significant for the U.S. to realize carbon neutrality. This exploration employs low-frequency vector auto-regression (LF-VAR) and mixed-frequency VAR (MF-VAR) models to capture the complicated interrelationship between cryptocurrency policy uncertainty (CPU) and carbon emission (CE) and to answer the question of whether cryptocurrency policy uncertainty could facilitate U.S. carbon neutrality. By comparison, the MF-VAR model possesses a higher explanatory power than the LF-VAR model;the former's impulse response indicates a negative CPU effect on CE, suggesting that cryptocurrency policy uncertainty is a promoter for the U.S. to realize the goal of carbon neutrality. In turn, CE positively impacts CPU, revealing that mass carbon emissions would raise public and national concerns about the environmental damages caused by cryptocurrency transactions and mining. Furthermore, CPU also has a mediation effect on CE;that is, CPU could affect CE through the oil price (OP). In the context of a more uncertain cryptocurrency market, valuable insights for the U.S. could be offered to realize carbon neutrality by reducing the traditional energy consumption and carbon emissions of cryptocurrency trading and mining.

Return and volatility spillovers among global assets: Comparing health crisis with geopolitical crisis

This study investigates the dynamic mechanism across equity, cryptocurrency, and commodity markets before and during health and geopolitical crisis (Covid-19 and the Ukrainian war). We apply the (TVP-VAR) based extended joint connectedness methodology, to understand return and volatility connectedness of financial markets for 2010–2023 period. The empirical results indicate that spillovers were particularly high during the Covid-19 and Russia-Ukraine war. First, health and geopolitical risks considerably impact the return and volatility system. Second, the value of total joint connectedness during the COVID-19 period was greater than during Russia-Ukraine war crisis. Also, evidence suggests that Commodity markets, received the highest shocks from other markets after Russia-Ukraine war and wheat was the main commodity receiving chocks from both health and geopolitical crisis. Our findings indicate that spillover channels differ depending on the type of crisis. Specifically, low-frequency components are the main transmission channels during the health crisis, whereas high-frequency components are the main transmission channels during the geopolitical crisis. Finally, results indicate that, cryptocurrency markets played some minor role in transmitting risks between markets. Our results are important in understanding how assets affect return and volatility spillover during geopolitical and health crises and are of particular importance to policymakers, market regulators, investors, and portfolio managers.

Assessing the impact on freight transport and treating uncertainty in freight transport modelling in the Covid pandemic: the case of Greece

This paper contains an investigation of the COVID-19 impacts on freight flows and the handling of uncertainty in freight forecasting models, based on data from Greece. It collects and analyses, over a 7-year period before and during the pandemic, data for freight transport operations and some related factors in order to macroscopically examine any statistically significant changes in their values over time. This period wasjudged necessary in order to establish the pattern of fluctuations in the relevant data during the non-pandemic years and thus make the visual comparison with the previous period and the years during the pandemic, more clear. First, the paper tests the impact of the pandemic as expressed by the number of daily COVID-19 cases on freight flow variables in order to find the dynamic behavior of these variables and trace their reactions over time. This analysis is made by using the Vector Autoregressive Model (VAR). By implementing VAR modelling, we analyzed the dynamic relationship between freight transport volumes and other factors such as GDP, the industrial production index, exporting transactions and the number of coronavirus cases. The main result of the model analysis and the employment of impulse response functions revealed that the unexpected shock of COVID has a negative reaction to the economy and the freight transport volumes and a rather shortterm limited duration disruption effect on the growth of exports as well as on the industrial production index, of approximately eight months. Secondly, the paper discusses how, unpredicted events like the pandemic, influence the uncertainty inherent in freight transport modelling and formulates a novel freight modelling framework procedure based on scenario building, regular monitoring and data updates on a permanent basis. [ FROM AUTHOR] Copyright of Advances in Transportation Studies is the property of Advances in Transportation Studies 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.)

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.

Understanding Uncertainty Shocks in Uruguay Through VAR Modeling

This study introduces a first set of uncertainty indexes for Uruguay (a newspaper-based index and a composite index-based) to analyze how economic uncertainty impacts domestic variables in a small and open economy such as Uruguay, which is exposed to international, regional, and local uncertainty. The analysis covers approximately 15 years and uses the vector autoregressive methodological framework. The main findings suggest that economic uncertainty significantly impacts the real economy and does not impact the nominal variables. These findings which differentiate from other results found in the empirical literature, can be associated with the stability of the Uruguayan economy and the strong institutions, which may help mitigate external shocks. To assess the capability of the proposed uncertainty model to predict macroeconomic variables, we evaluate its predictive performance within the last major uncertainty shock due to the COVID-19 pandemic. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

The stock market and NO2 emissions effects of COVID‐19 around the world

In this paper, we study the impact of the coronavirus disease 2019 pandemic in estimated panel vector autoregression models for 92 countries. The large cross‐section of countries allows us to shed light on the heterogeneity of the responses of stock markets and nitrogen dioxide emissions as high‐frequency measures of economic activity. We quantify the effect of the number of infections and four dimensions of policy measures: (1) containment and closure, (2) movement restrictions, (3) economic support, and (4) adjustments of health systems. Our main findings show that a surprise increase in the number of infections triggers a drop in our two measures of economic activity. Propping up economic support measures, in contrast, raises stock returns and emissions and, thus, contributes to the economic recovery. We also document vast differences in the responses across subsets of countries and between the first and the second wave of infections.

Do Automated Market Makers in DeFi Ecosystem Exhibit Time-Varying Connectedness during Stressed Events?

We investigate the connectedness of automated market makers (AMM) that play a pivotal role in liquidity and ease of operations in the decentralized exchange (DEX). By applying the TVP-VAR model, our findings show higher level of connectivity during periods of turmoil (such as Delta, Omicron variants of SARS-Covid, and the Russia Ukraine conflict). Furthermore, risk transmission/reception is found to be independent of the platform on which they typically run (Ethereum based AMMs were both emitters as well as receivers). Pancake (a Binance based AMM) and Perpetual Protocol (Ethereum based AMM) emerged as moderate to high receivers of risk transmission, whereas all of the other AMMs, including Ethereum, were found to be risk emitters at varying degrees. We argue that AMMs typically depend on the underlying smart contracts. If the contract is flexible, AMMs can vary (either receiver or emitter), otherwise AMMs behave in tandem. © 2023 by the authors.

The impact of uncertainty on money demand in the UK, US and Euro area

We estimate money demand functions for the UK, the Euro area and the US using Divisia monetary aggregates and investigate the extent to which the uncertainty caused by Brexit and Covid have affected these relationships. Our cointegrated VAR analysis shows that for all three economies Brexit and/or Covid have had some impact on the stability of money demand functions. We find that including a measure of stock market volatility in the money demand specifications helps re-establish stability of the models, particularly for the UK and the Euro area. We also explore the uncertainty and money demand relationship in the context of a Markov-switching model. We find that the effect of uncertainty on the demand for money is more pronounced during periods of heightened uncertainty. The findings of this study lend support to studies calling for Divisia aggregates to be given a more prominent role in policymaking, especially when interest rates are in the zero lower bound environment and are less informative about the stance of monetary policy.

Fiscal deficits and the socioeconomic consequences of rebalancing: Insights from a TVP‐VAR with stochastic volatility

This article connects two salient economic features: (i) Fiscal shocks have asymmetric effects across business cycle phases (Gechert, Horn, & Paetz, 2019);(ii) the unemployment‐output trade‐off is time varying and may be unstable. The intertwined dynamic behaviour of fiscal deficit shocks and the unemployment‐output trade‐off is studied in this article using a time‐varying parameter (TVP) vector autoregression (VAR) with stochastic volatility techniques applied to the analysis of data from Canada, France, Germany, Japan, Spain, Sweden, United Kingdom and the United States of America. We confirm the trade‐off heterogeneity across country, and its time‐varying nature across time, showing in addition its fluctuation around a long‐run reference value. We document significant short‐run impacts of fiscal shocks on the unemployment‐output trade‐off which, based on the experience of the Global Financial Crisis, becomes larger in periods of economic turmoil. Policy‐wise, the rebalancing of public finances may have unexpected adverse effects on job creation if implemented during slumps, precisely when the labour market sensitivity with respect to the performance of the product market is likely to be more acute. This message is particularly relevant in the aftermath of the Covid‐19 pandemic.

Dihydroisocoumarins of Hydrangea macrophylla var. thunbergia inhibit binding of the SARS-CoV-2 spike protein to ACE2.

Binding of the spike protein of severe acute respiratory syndrome coronavirus 2 to the cognate angiotensin-converting enzyme 2 receptor is the initial step in the viral infection process. In this study, we screened an in-house extract library to identify food materials with inhibitory activity against this binding using enzyme-linked immunosorbent assays and attempted to ascertain their active constituents. Hydrangea macrophylla var. thunbergia leaves were identified as candidate materials. Its active compounds were purified using conventional chromatographic methods and identified as naringenin, dihydroisocoumarins, hydrangenol, and phyllodulcin, which have affinities for angiotensin-converting enzyme 2 receptor and inhibit angiotensin-converting enzyme 2 receptor-spike S1 binding. Given that boiled water extracts of H. macrophylla leaves are commonly consumed as sweet tea in Japan, we speculated that this tea could be used as a potential natural resource to reduce the risk of severe acute respiratory syndrome coronavirus 2 infection.

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.

Integration and risk transmission in the market for crude oil: New evidence from a time-varying parameter frequency connectedness approach

In this study, we introduce a novel time-varying parameter vector autoregressive frequency connectedness approach to obtain refined measures of the frequency transmission mechanism and dynamic integration among six well-established crude oil benchmarks. The period of investigation ranges from May 14th, 1996 to December 3rd, 2020 and focuses on the differences between short-term (1–5 days) and long-term (6–100 days) crude oil volatility connectedness. Findings are suggestive of relatively strong co-movements among crude oil volatility over time. For most part of the sample period, connectedness occurs in the short-run;nonetheless, starting approximately in 2010, long-run connectedness gains much prominence until at least the end of 2015. Long-run connectedness is also prevalent at the beginning of 2020 caused by the COVID-19 pandemic. We opine that periods of increased long-run connectedness relate to deeper changes in the market for crude oil that bring about new dynamics and associations within the specific network.

The effects of central bank extraordinary measures on financial conditions: Evidence from Mexico

This paper analyses the effects of the extraordinary measures implemented by the Central Bank of Mexico during the COVID-19 pandemic on financial conditions. For this purpose, we estimate a factor-augmented vector autoregressive model for the period 2001–2021. Based on this model, we construct a Financial Conditions Index, estimate the response of this indicator and its components from a shock to the outstanding amount of these measures, and conduct a counterfactual exercise to further analyse the effect of the aforementioned measures. The main results indicate that these extraordinary measures seem to have contributed to improve financial conditions. In particular, we find that if these measures had not been implemented, the sovereign risk premium, the 10-year government bond yield, the slope of the yield curve, and the long- and short-term yield spreads between Mexico and the US would have been higher by around 56, 31, 27, 37, and 49 basis points in December 2020, respectively. At the same time, the Mexican peso/US dollar exchange rate and its volatility would have been higher by 5 and 2 percentage points, respectively. In turn, the Mexican stock market index would have been lower by 10 percentage points. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Dynamic linkages between shipping and commodity markets: Evidence from a novel asymmetric time-frequency method

This study extends the existing literature in this area by examining the connectedness and shock spillover between commodity and shipping markets using a new novel time-varying frequency and quantile connectedness method developed by Chatziantoniou et al (2022) based on B&K (2018) and Ando et al (2018). Connectedness and shock transmission between the markets were analysed with daily data covering July 4, 2012 to July 20, 2022. A major value added of this study to the existing literature is the examination of the asymmetric effect of commodity price changes (or return) on the connectedness of the markets. Mean-frequency total connectedness analysis indicates that, the overall shipping market (BDI) is both the transmitter (to) and receiver of the highest shock from the entire market connectedness system. In the short-term, the agricultural markets dominate as both the transmitters and receivers of the major shocks to and from the entire market system, while in the medium-term, the shipping markets dominate as both the transmitters and receivers of the largest shocks to the entire market system. However, in the long-term, connectedness and shock propagation were very low. The time-varying quantile analysis reveals that, connectedness was very strong before, during and after COVID-19 at the bearish and bullish market conditions. Further, the time-varying frequency connectedness analysis shows that, although total connectedness is relatively high overtime, it was propelled by short-term dynamics. Metal markets are connected among themselves, and with both agricultural and shipping markets. Agricultural markets are connected among themselves, and with shipping markets, which are only connected among themselves. There is evidence of the asymmetric effect of commodity return dynamics on the connectedness of the markets. Some important policy recommendations were drawn from the findings.

Blockchain market and eco-friendly financial assets: Dynamic price correlation, connectedness and spillovers with portfolio implications

Since the emergence of blockchain technology, several digital assets such as cryptocurrencies, DeFi, and NFTs have gained considerable attention from investors and policymakers. However, the blockchain market has significant negative ramifications for the environment that may transmit shocks towards eco-friendly financial assets. We use the rolling window wavelet correlation (RWWC) model and the quantile-based time-varying (QVAR) connectedness framework to analyze the dynamic price correlation and connectedness between the blockchain market and green (eco-friendly) financial assets. As a representative of the blockchain market, we use the price returns of four cryptocurrencies, DeFi, and NFTs. For green equities, we use the MSCI Global Environment Price Index and the S&P Green Bond Price Index. We find a low correlation between the blockchain market and green financial assets before the outbreak of COVID-19 and a strong correlation during the COVID-19 and the Russia-Ukraine war. The quantile VAR results show symmetric connectedness of the examined and identical spillovers between extremely positive and strongly negative returns. Green bonds and stocks are the system's major shock receivers. The transmission network results imply major shock transmissions are driven by short-term frequency, whereas there is a lower transmission in the long-term. © 2023 Elsevier Inc.

Analyzing the emotional impact of COVID-19 with Twitter data: Lessons from a B-VAR analysis on Italy.

The novel coronavirus 2019 revolutionized the way of living and the communication of people making social media a popular tool to express concerns and perceptions. Starting from this context we built an original database based on the Twitter users' emotions shown in the early weeks of the pandemic in Italy. Specifically, using a single index we measured the feelings of four groups of stakeholders (journalists, people, doctors, and politicians), in three groups of Italian regions (0,1,2), grouped according to the impact of the COVID-19 crises as defined by the Conte Government Ministerial Decree (8th March 2020). We then applied B-VAR techniques to analyze the sentiment relationships between the groups of stakeholders in every Region Groups. Results show a high influence of doctors at the beginning of the epidemic in the Group that includes most of Italian regions (Group 0), and in Lombardy that has been the region of Italy hit the most by the pandemic (Group 2). Our outcomes suggest that, given the role played by stakeholders and the COVID-19 magnitude, health policy interventions based on communication strategies may be used as best practices to develop regional mitigation plans for the containment and contrast of epidemiological emergencies.

Exploring the short-term and long-term linkages between carbon price and influence factors considering COVID-19 impact.

Because of global lock-downs caused by the unexpected COVID-19, the interactions between emission trading and related markets have changed significantly compared to the pre-COVID-19 period. Considering the pandemic effect, this paper established an integrated system to identify the relationship trajectories between carbon trading market and impact factors. A noise-assisted multivariate empirical mode decomposition (N-A MEMD) method was utilized to simultaneously decompose the original multi-dimensional time series into intrinsic mode functions (IMFs), after which the Lempel-Ziv (LZ) complexity algorithm was applied to reconstruct the IMFs into high-frequency (HF), low-frequency (LF), and trend modules. Vector autoregression (VAR) and vector error correction (VEC) models were then used to systematically simulate the correlations. The time span was split into pre-COVID-19 and post-COVID-19 periods for comparison, and the mobility trends data during the outbreak period released by the Apple company was chosen to reflect the pandemic effects. The empirical analysis results revealed the energy prices, macroeconomic index, and exchange rate are the main external impact factors of carbon price in the short term. Summarizing from the cointegration models over the long term, the market stability reserve (MSR) mechanism was found to have ability on stabilizing the carbon price under the epidemic shock. Furthermore, the COVID-19 was found to complicate the relationships between carbon price and influence factors, which resulted in fluctuating markets.

Sustainability and stability: Will ESG investment reduce the return and volatility spillover effects across the Chinese financial market?

This study provides a preliminary investigation of the relationship between sustainability and stability by investigating the impact of ESG investment on the return and volatility spillover effects in the major Chinese financial markets, including the stock, bond, interbank, and foreign exchange markets. We adopted both the TVP-VAR and DY methods to calculate the time-varying total, directional, and pairwise spillover indices. We examined the impact of ESG investment on financial market stability by comparing the spillover effects when ESG investment, represented by the ESG stock index, is considered with those without special consideration on the ESG investment, represented by the general stock index. The results show that when the ESG stock index replaces the general stock index, the total, directional, and pairwise spillover effects in the Chinese financial market generally decrease. Meanwhile, we find that although the overall Chinese financial market spillover index is around 13%, it is occasionally quite volatile. In particular, the markets were hugely uncertain in 2013 and 2020 due to the disequilibrium of supply and demand conditions in the money market and the considerable shocks created by the COVID-19 pandemic. We support the idea that, while the Chinese government develops its green finance, for instance, by advocating for ESG investment, it simultaneously builds a more stabilized financial market. In other words, sustainability and stability are positively correlated and can be achieved together. The reason for this is that ESG investment supports a long-run investment strategy by reducing excessive short-run speculation activities in the Chinese stock market, which accounts for the volatile property of the market since it was launched.

The impact of central bank digital currency news on the stock and cryptocurrency markets: Evidence from the TVP-VAR model

This study employs a non-linear framework to investigate the impacts of central bank digital currency (CBDC) news on the financial and cryptocurrency markets. The time-varying vector autoregressive (TVP-VAR) model developed by Primiceri (2005) is estimated based on weekly data from the first week of January 2015 to the last week of December 2021. The vector of endogenous variables in the VAR estimation contains the Central Bank Digital Currency uncertainty index (CBDCU), cryptocurrency policy uncertainty index, S&P 500 index, VIX, and Bitcoin price. The TVP-VAR model's time-varying responses demonstrated that the reactions of the cryptocurrency market to central bank digital currency announcements vary remarkably over time. The impacts of the CBDC shocks on the financial market have been increasingly visible during the COVID-19 pandemic. According to the time-varying forecast error decompositions, CBDCU and VIX shocks have accounted for most of the variance in cryptocurrency uncertainty and Bitcoin return shocks, notably during the COVID-19 period.