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.)

Different Moments Create Different Spillovers: A Study of Commodity Markets

Although the spillover effects of return and volatility risk across commodity markets have been demonstrated, evidence of extreme risk spillovers is limited. Using an autoregressive conditional density model, this study estimates the conditional skewness of nine S&P Goldman Sachs Commodity indices and then applies the Diebold-Yilmaz TVP-VAR-based approach to investigate the higher moment spillovers across commodity markets. Our findings provide evidence of extreme risk transfers from one commodity index to another. Among three energy indices including crude oil, natural gas and gasoil, crude oil transmits the most return, volatility risk and extreme risk to the agricultural indices and precious metal indices. Furthermore, our results confirm that spillovers in all three moments were significantly strengthened by extreme events such as the September 11 attacks, the global financial crisis, the food price crisis, the violent shock of international oil prices and the coronavirus disease of 2019. However, different events may have different impacts on spillovers. Finally, the results indicate that return spillover and skewness are affected by extreme events with almost the same intensity and direction for most periods.

Unraveling the COVID-19 Pandemic's Impact on South Korea's Macroeconomy: Unearthing Novel Transmission Channels within the Energy Sector and Production Technologies

As a consequence of the COVID-19 pandemic, Korea's economy has experienced significant setbacks. Thus, this article examines the implications of the COVID-19 pandemic on Korea's key macroeconomic indicators via the transmission channels of oil prices and production technology. Using Bayesian estimation and impulse response functions for empirical investigation, the results suggest that the COVID-19 pandemic has intensified the reduction in firm production, consumption of oil-based goods, employment, and investment. Increasingly, households rely on non-oil goods rather than oil-based ones. Similarly, the results suggest that the drop in production technology levels brought on by the COVID-19 pandemic has a stronger impact on business output and investment but a lesser influence on household employment. The COVID-19 pandemic has led to a decline in household non-oil consumption as well as household and business consumption of oil-based goods. To sum up, the existing Korean literature on this issue might be improved by including the findings offered in this article. [ FROM AUTHOR] Copyright of Energies (19961073) is the property of MDPI 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.)

Analyzing the Stock Volatility Spillovers in Chinese Financial and Economic Sectors

By regarding the Chinese financial and economic sectors as a system, this article studies the stock volatility spillover in the system and explores its effects on the overall performance of the macroeconomy in China. The recent outbreak of COVID-19, U.S.-China trade friction, and three historical financial turbulences are involved to distinguish the changes in the spillover in these distinct crises, which has seldom been unveiled in the literature. By considering that the stock volatility spillover may vary over distinct timescales, the spillovers are disclosed through innovatively constructing the multi-scale spillover networks, followed by connectedness computation, based on variational mode decomposition (VMD) and generalized vector autoregression (GVAR) process. Our empirical analysis first demonstrates the different levels of increases in the total sectoral volatility spillover and changes in the roles of the sectors in the system under the aforementioned crises. Besides, the increases in the sectoral spillover in the long-term are verified to negatively impact the macroeconomy and can thereby act as warning signals.

Energy cryptocurrencies: Assessing connectedness with other asset classes

We investigate connectedness between energy cryptocurrencies and common asset classes, including oil, using TVP-VAR modeling, evidencing that energy cryptocurrencies, as diversifiers, normally have strong connections with bitcoin and nothing else. However, their connectedness to other assets changes rapidly during shocks such as COVID-19 and the start of the Russian-Ukraine war. Connectedness spiked in April 2020, when WTI oil prices fell to negative pricing. Economic policy uncertainty, Twitter-based uncertainty, and infectious disease-related uncertainty all have significant impact on the system's total connectedness. Energy cryptocurrencies, while normally diversifiers, are highly sensitive to shocks and changes in uncertainty.

Multiroom Speech Emotion Recognition

Automated audio systems, such as speech emotion recognition, can benefit from the ability to work from another room. No research has yet been conducted on the effectiveness of such systems when the sound source originates in a different room than the target system, and the sound has to travel between the rooms through the wall. New advancements in room-impulse-response generators enable a large-scale simulation of audio sources from adjacent rooms and integration into a training dataset. Such a capability improves the performance of data-driven methods such as deep learning. This paper presents the first evaluation of multiroom speech emotion recognition systems. The isolating policies due to COVID-19 presented many cases of isolated individuals suffering emotional difficulties, where such capabilities would be very beneficial. We perform training, with and without an audio simulation generator, and compare the results of three different models on real data recorded in a real multiroom audio scene. We show that models trained without the new generator achieve poor results when presented with multiroom data. We proceed to show that augmentation using the new generator improves the performances for all three models. Our results demonstrate the advantage of using such a generator. Furthermore, testing with two different deep learning architectures shows that the generator improves the results independently of the given architecture.

COVID-19 and connectedness between Sustainable and Islamic equity markets

Because of the increasing importance of and demand for ethical investment, this paper investigates the dynamics of connectedness between sustainable and Islamic investment in nineteen countries that represent developed and emerging financial markets worldwide. To this end, we apply models proposed by Diebold and Yilmaz and Barunik and Krehlik to explore the overall and frequency-based connectedness between selected ethical investments. Our results reveal evidence of a moderate to strong intra country-level connectedness between sustainable and Is-lamic investment and limited cross-country connectedness between ethical investments. The time-varying connectedness analysis suggests enhanced connectedness during periods of market-wide turmoil, such as the European debt crisis, the Chinese financial crisis, and the COVID-19 pandemic. Moreover, the COVID-19 subsample analysis shows an enhanced and idiosyncratic country-level and cross-country connectedness structure between ethical investments, indicating the evolving nature of the relationship between sustainable and Islamic investment. Copyright (c) 2022 Borsa Istanbul Anonim S,irketi . Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Building a GVAR Model for the Russian Economy

The relationship between the economies of various countries and their dependence on the world markets indicate that for econometric analysis of the impact of external shocks on a particular economy, it is necessary to use a model of the global economy. The aim of this paper is to build a global vector autoregression model (GVAR), including Russia as one of the regions, and to obtain the impact of some external economic shocks on Russian macroeconomic indicators. We build a model that includes 41 of the world's major economies, including Russia, and the oil market. The special features of our model are structural shifts in the dynamics of Russian output and the new specification of oil supply and oil demand. Impulse response functions are used to obtain quantitative estimates. In this paper, we analyze the reaction of outputs, oil production volumes and oil prices in response to the output shocks of China and the United States. In response to the negative shock of output in the world's leading economies, outputs in the rest of the world declined for at least the first year after the shock. There was also a significant decline in oil prices and no significant change in oil production volumes in most countries. In addition, as part of the conditional forecast, we estimated the impact of the decline in global demand due to the Covid-19 pandemic on the Russian GDP as 1,3% drop. The rest of the decline in Russian GDP can be attributed to the internal effects of the pandemic (lockdown). We also obtained a scenario forecast of the dynamics of Russian GDP depending on a decrease in trade and Russian oil price discount, within which the fall in Russian output could reach 3.3% in 2022. © 2023 Publishing House of the Higher School of Economics. All rights reserved.

Trading Risk Spillover Mechanism of Rare Earth in China: New Perspective Based on Time-Varying Connectedness Approach

Our research contributes a new point of view on China's rare earth dynamic risk spillover measurement;this was performed by combining complex network and multivariate nonlinear Granger causality to construct the time-varying connectedness complex network and analyze the formation mechanism using the impulse response. First, our empirical research found that for the dynamic characteristics of China's rare earth market, due to instability, uncertainty, and geopolitical decisions, disruption can be captured well by the TVP-VAR-SV model. Second, except for praseodymium, oxides are all risk takers and are more affected by the impact of other assets, which means that the composite index and catalysts are main sources of risk spillovers in China's rare earth trading complex network system. Third, from the perspective of macroeconomic variables, there are significant multivariate nonlinear impacts on the total connectedness index of China's rare earth market, and they exhibit asymmetric shock characteristics. These findings indicate that the overall linkage of the risk contagion in China's rare earth trading market is strong. Strengthening the interconnections among the rare earth assets is of important practical significance. Empirical results also provide policy recommendations for establishing trading risk protection measures under macro-prudential supervision. Especially for investors and regulators, rare earth oxides are important assets for risk mitigation. When rare earth systemic trading risk occur, the allocation of oxide rare earth assets can hedge part of the trading risk.

Bayesian VARs of the U.S. economy before and during the pandemic

We compare the forecasting performance of small and large Bayesian vector-autoregressive (BVAR) models for the United States. We do the forecast evaluation of the competing models for the sample that ends before the pandemic and for the sample that contains the pandemic period. The findings document that these models can be used for structural analysis and generate credible impulse response functions. Furthermore, the results indicate that there are only small gains from the application of a large BVAR model compared to a small BVAR model. © 2023, The Author(s).

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

Defying the Odds: Remittances during the COVID-19 Pandemic

This paper provides an early assessment of the dynamics and drivers of remittances during the COVID-19 pandemic, using a newly compiled monthly remittance dataset for a sample of 52 countries, of which 16 countries have bilateral remittance data. The paper documents a strong resilience in remittance flows, notwithstanding an unprecedent global recession triggered by the pandemic. Using the local projection approach to estimate the impulse response functions of remittance flows during January 2020–December 2020, the paper provides evidence that: (i) remittances responded positively to COVID-19 infection rates in migrant home countries, underscoring its role as an important automatic stabilizer;(ii) stricter containment measures have the unintended consequence of dampening remittances;and (iii) a shift from informal to formal remittance channels due to travel restrictions appears to have also played a role in the surge in formal remittances. Lastly, the size of the fiscal stimulus in the host country is positively associated with remittance flows to migrants' home country as the fiscal response cushioned the economic impact of the pandemic. [ FROM AUTHOR] Copyright of Journal of Development Studies is the property of Routledge 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.)

Directional predictability and volatility spillover effect from stock market indexes to Bitcoin: evidence from developed and emerging markets

PurposeThis paper aims to quantify the volatility spillover impact and the directional predictability from stock market indexes to Bitcoin.Design/methodology/approachDaily data of 15 developed and 15 emerging stock markets are used for the period March 2017–December 2021.;The author uses vector autoregressive (VAR) model, Granger causality test and impulse response function (IRF) to estimate the results of the study.FindingsEmpirical results show a significant unidirectional volatility spillover impact from emerging markets to Bitcoin and only six stock markets are powerful predictors of Bitcoin return in the short term. Additionally, there is no a difference between developed and developing markets regarding the directional predictability however there is difference in the reaction of Bitcoin return to shocks in the emerging markets compared to developed ones.Originality/valueThe paper proposes different econometric techniques from prior research and presents a comparative analysis between developed and emerging markets.

The Impact of the COVID-19 Pandemic on the Volatility of Cryptocurrencies

This study aimed to investigate the interactions between Bitcoin to euro, gold, and STOXX50 during the period of COVID-19. First, a bibliometric analysis based on the R package was applied to highlight the research trends in the field during the period of the COVID-19 pandemic. While investigating the effects of the pandemic on Bitcoin, the number of cases of COVID-19 was used as a proxy. Using daily data for the period 1 March 2020 to 3 March 2020 and based on a vector autoregressive model, impulse response, and variance decomposition were utilized to analyze the dynamic relationships among the variables. The results revealed that the COVID-19 cases and gold hurt the exchange rate of Bitcoin to euro, while there was great volatility regarding the response of Bitcoin to a shock of STOXX50. The Granger causality test was constructed to investigate the relationships among the variables. The results show the presence of unidirectional causality running from new cases to STOXX50 and from STOXX50 to gold. This study contributes to the existing scholarly research into the dynamic relationships that appeared among Bitcoin, gold, and STOXX50 in a period of great uncertainty. Finally, the findings have significant implications for investors, who are interested in diversifying their portfolios. © 2023 by the authors.

Volatility Spillovers Among the Three Places Across the Taiwan Strait: Evidence from a BEKK‐CARR Approach

This study introduces a new BEKK‐CARR model to explore the volatility spillover effects among mainland China, Hong Kong, and Taiwan stock markets during the COVID‐19 pandemic. We also extend the approach of Diebold and Yilmaz (2009, 2012) to infer a brand‐new volatility spillover index to discuss the bi‐directional volatility transmission. Our results show that the trading information flow among these three markets has changed significantly as a result of the COVID‐19 pandemic. The strength of volatility spillover is increasing during this momentous period. The Hong Kong stock market plays a pivotal role in volatility transmission. The values for half‐lives by exogenous shocks keep relatively low during the pandemic period. A reasonable explanation is that the trading information transmissions among stock markets are quicker than in the non‐pandemic period.

The US-China trade war and the volatility linkages between energy and agricultural commodities

Any disruptive changes in the competitive environment, such as the U.S.-China trade war, may influence the price volatility of crude oil and agricultural commodities. This study examines the volatility linkage between crude oil and agricultural commodity markets in the context of the U.S.-China trade war and compares the impact of the trade war with that of other exogenous shocks. The results show that the volatility of soybeans exhibits the highest level of responsiveness to the U.S.-China trade war - which is not surprising given that the U.S. agribusiness trade to China is dominated by soybeans - followed by coffee and cotton. The sizes and dynamics of the impacts of shocks are largely commodity-specific. Notably, the trade war impacts most agricultural commodities more extensively than other exogenous shocks, including the global financial crisis and the COVID-19 pandemic and associated recession. These findings matter not only for the decision-making of investors and portfolio managers but also for commodity-exporting and importing countries because changes in the volatility dynamics of crude oil and agricultural commodities often impact export revenues and import expenditures and consequently feed through exports to the global supply chain under exogenous shocks such as the U.S.-China trade war. © 2023 The Authors

Building a GVAR Model for the Russian Economy

The relationship between the economies of various countries and their dependence on the world markets indicate that for econometric analysis of the impact of external shocks on a particular economy, it is necessary to use a model of the global economy. The aim of this paper is to build a global vector autoregression model (GVAR), including Russia as one of the regions, and to obtain the impact of some external economic shocks on Russian macroeconomic indicators. We build a model that includes 41 of the world's major economies, including Russia, and the oil market. The special features of our model are structural shifts in the dynamics of Russian output and the new specification of oil supply and oil demand. Impulse response functions are used to obtain quantitative estimates. In this paper, we analyze the reaction of outputs, oil production volumes and oil prices in response to the output shocks of China and the United States. In response to the negative shock of output in the world's leading economies, outputs in the rest of the world declined for at least the first year after the shock. There was also a significant decline in oil prices and no significant change in oil production volumes in most countries. In addition, as part of the conditional forecast, we estimated the impact of the decline in global demand due to the Covid-19 pandemic on the Russian GDP as 1,3% drop. The rest of the decline in Russian GDP can be attributed to the internal effects of the pandemic (lockdown). We also obtained a scenario forecast of the dynamics of Russian GDP depending on a decrease in trade and Russian oil price discount, within which the fall in Russian output could reach 3.3% in 2022. © 2023 Publishing House of the Higher School of Economics. All rights reserved.

Dynamic impacts of online investor sentiment on international crude oil prices

In recent years, international crude oil prices have been subject to unusually high fluctuations due to the ravages of the COVID-19 epidemic. Under such extreme market conditions, online investor sentiment can strengthen the correlation between oil price changes and external events. We use a (rolling-window) structural vector autoregression method to investigate the dynamic impact of online investor sentiment on WTI crude oil prices before and after the COVID-19 pandemic across multiple topics of price, supply, demand, and so on, which aims to explore the fluctuation mechanism driven by sentiment and the price changes triggered by public health events. The proposed aspect-level sentiment analysis approach can effectively distinguish and measure sentiment scores of different aspects of the oil market. Our results show that the constructed oil price prosperity index contributes 49.84% to the long-term fluctuations of WTI oil price, ranking first among the influencing factors considered. In addition, the peak value of impulse shocks to WTI oil prices rose from 6.47% to 8.40% during the period of dramatic price volatility caused by the epidemic. The results sketch the mechanisms by which investor sentiment can affect crude oil prices, which help policymakers and investors protect against extreme risks in the oil market. © 2023 Elsevier Ltd

Information and volatility linkages between real estate, equity, bond and money markets in Australia

PurposeThe purpose of this paper is to examine information and volatility linkages among real estate, equity, bond and money markets in Australia.Design/methodology/approachA novel rational expectations framework of financial contagion (Kodres and Pritsker, 2002), along with a combination of robust statistical methods including simple and dynamic correlations and generalized impulse response (Fereidouni et al., 2014) have been employed using data covering three dynamic pre-pandemic economic cycles, namely, global financial crisis (GFC) period, pre-pandemic housing boom and pre-pandemic housing downturn from 2008 (February) to 2019 (December).FindingsResults reveal information linkages across real estate, equity, bond and money markets through correlations in return and volatilities of these series. Finding indicates that the three financial markets (equity, bond and money markets) are interdependent and integrated through information and volatility linkages during the GFC period and pre-pandemic housing downturn period. Financial markets have stronger associations with real estate market during pre-pandemic housing boom. The findings contribute to the general notion that the performances of three financial markets are closely related to the "boom” phase of the real estate cycle.Originality/valueThis research provides an extension of existing literature regarding the information and volatility contagion of the expanded set of core investment markets in Australia. The findings could assist household buyers and investors in designing strategic investment portfolios/hedging strategies and minimizing asset specific risks through diversification over short-term and long-term. In addition, results could support the maintenance, growth and development of a combination of competitive balanced investment markets including real estate, equity, bond and money markets in post-pandemic economy.

Effects of Energy Prices Shocks on Global Inflation: A Panel Structural VAR Approach

Global supply shock suffered massive disruption because of COVID-19 in the last few years. Such a shock is accompanied by an energy price surge caused by the war in Ukraine. We study the effects of energy price shocks (common, idiosyncratic) on inflation due to energy price issues. We set up a panel structural VAR (PSVAR) model to study whether energy price shocks exhibit long memory properties (persistence) having permanent (long-run) effects on global inflation. The model is modelled under Cholesky and Blanchard-Quah restrictions. We calculate medians, averages, and interquartile impulse response functions with confidence interval quantiles following bootstrapping procedure. We see energy shock impact on headline inflation last 2.5 years (slow mean-reversion) reaching pre-crisis level. © 2022 by the authors.