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.)

The Granger Causality of Bahrain Stocks, Bitcoin, and Other Commodity Asset Returns: Evidence of Short-Term Return Spillover before and during the COVID-19 Pandemic

This study examines the tendency of short-term return spillover across Bahrain stocks, bitcoin, and other commodity assets factoring in the dynamic effect of the COVID-19 pandemic. The study employed vector autoregression (VAR) model using the daily returns of Bahrain All Shares Index, bitcoin, crude oil, and gold futures from January 2018 to March 2022. The results showed a persistent unidirectional short-term spillover of return from the Bahrain stock market to the futures gold market for both the period before and during the pandemic. Moreover, the results also showed that the significant positive shock in the bitcoin returns as granger-caused by the returns of the Bahrain stock market is only during the period before the pandemic. Finally, a significant negative contemporaneous short-term effect on the crude oil market returns can be statistically explained by the shocks in the Bahrain stock market only during the COVID-19 period. © 2023 IGI Global. All rights reserved.

Exploring the growth direction: the impact of exchange rate and purchasing managers index on economic growth in Sri Lanka.

Numerous studies have been conducted, globally and locally, on the impact of the exchange rate on economic growth. In the local context, only a handful of research have investigated this area of study to determine the extent to which the Purchasing Managers' Index influence economic growth with the exchange rate, with limited research have been performed in Sri Lanka. This study explores the impact of exchange rate and Purchasing Managers' Index on economic growth. Consequently, adopting an applied research methodology, the present study was based on secondary data published quarterly by the Central Bank of Sri Lanka reports and the Department of Census and Statistics of Sri Lanka from 2015 to 2021. The Vector autoregression model and Granger Causality Wald test were performed in this study. The empirical findings highlighted that economic growth and Purchasing Managers' Index have a significant negative impact on the economic growth, while the exchange rate had a significant positive impact on the economic growth. Furthermore, the exchange rate and the Purchasing Managers' Index did not help to predict the exchange rate. The implications of the study demonstrate the relevance of the exchange rate and manufacturing Purchasing Managers' Index as indicators of changes in overall economic growth activities at the macro level. The findings will assist the Sri Lankan Government, policymakers, and foreign investors for effective decision making.

Can the Narrow and Broad Money Supply Gap Be Used as an Investment Indicator for the Stock Market?

This study uses the golden cross and death cross formed by the gap between the narrow and broad money growth rates as threshold variables to estimate the threshold model and test the causal relationship between money supply and stock prices in eight emerging market economies (EMEs) in Asia;the sample periods are from January 2000 to December 2020. The results show a high-positive, bi-directional relationship between the money supply and stock prices in the golden cross regime. On the other hand, the money supply has a negative, one-way causality on stock prices in the death cross regime. We also conducted a robustness test during the COVID-19 spread, and the result shows that the mechanism still applies, but the effectiveness is reduced. Thus, our contribution is discovering the golden cross and death cross information formed by narrow and broad money, informing stock market investment.

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.

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

A Demand Systems Approach to Understanding Medium-Term Post-Pandemic Consumption Trends

Recent research has documented the immediate negative impact of the COVID-19 pandemic on household and business consumption, but there is still limited investigation into the medium-term effects in specific consumption categories. This paper addresses this gap using a vector autoregression analysis of a system of aggregated consumer final demand across Australia. We highlight the importance of studying a demand system, as opposed to investigating independent consumption categories, due to the interactive evolution of consumption during the pandemic. Modelling the paths of various consumption categories in response to shocks from one another, we find that, despite the large and abrupt shocks to consumption during the first two quarters of 2020, most categories reverted to pre-COVID levels when restrictions were lifted. Importantly, transportation had the largest and most persistent decline. Overall, shocks to sectors other than food, alcohol and education were outside the counterfactual forecast confidence intervals estimated based on pre-COVID information. © 2023 The Authors. Economic Papers;A journal of applied economics and policy published by John Wiley & Sons Australia, Ltd on behalf of The Economic Society of Australia.

Macroeconomic Effects of Covid-19 in a Commodity-Exporting Economy: Evidence from Mongolia

This article examines macroeconomic effects and transmission mechanisms of Covid-19 in Mongolia, a developing and commodity-exporting economy, by estimating a Bayesian structural vector autoregression on quarterly data. We find strong cross-border spillover effects of Covid-19 passing through changes in commodity markets and the Chinese economy. Our estimates suggest that China's GDP and copper price shocks respectively account for three-fifths and one-fifths of the drop in real GDP in 2020Q1. The recovery observed for 2020Q2-2021Q1 is primarily due to positive external shocks. However, disruptions in credit and labor markets have been sustained in the economy. Two-thirds of the fall in employment in 2021Q1 could be attributed to adverse labor demand shocks. We also reveal novel empirical evidence for the balance sheet channel of the exchange rate, the financial accelerator effects, and an indirect channel of wage shock to consumer price passing through bank credit.

Dynamic connectedness in commodity futures markets during Covid-19 in India: New evidence from a TVP-VAR extended joint connectedness approach

This paper presents a unique time-varying parameter vector autoregression (TVP-VAR) based extended joint connectedness approach to quantify the connectedness and transmission mechanism of shocks of nine commodities futures returns (namely;Gold and Silver from the category of precious metals;Copper, Lead, Zinc, Nickel and Aluminium from the category of base or industry metals;Natural Gas and Brent Crude Oil from energy sector) obtained from Multi Commodity Exchange of India Limited (MCX) from January 1, 2018 to December 31, 2021. This paper employs Balcilar et al. (2021)'s TVP-VAR extended joint connectedness approach, which combines the TVP-VAR connectedness approach of Antonakakis et al. (2020) with the joint spillover approach of Lastrapes and Wiesen (2021), to investigate the dynamic connectedness among the select commodity futures of interest. Our findings show that system-wide dynamic connectedness varies over time and is driven by economic events. The pandemic shocks appear to have an impact on system-wide dynamic connectedness, which peaks during the COVID-19 pandemic. Crude oil and zinc are the primary net shock transmitters, whereas gold and silver are the primary net shock receivers. We also discovered that the role of aluminum in shock transmitters and shock receivers changed during the course of the investigation. Pairwise connectivity, on the other hand, shows that Zinc, Copper, Nickel, and Crude oil are the key drivers of gold price changes, explaining the network's high degree of interconnectivity. During the study period, it was also discovered that silver has a significant influence on gold. Furthermore, in comparison to natural gas, gold's spillover activity is still relatively modest (on a scale), indicating that gold is less sensitive to market innovations. © 2023 Elsevier Ltd

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

IMPACTS OF COVID-19 NON-PHARMACEUTICAL INTERVENTIONS ON TRADE FLOWS: A GLOBAL PANEL VECTOR AUTOREGRESSION ANALYSIS

The COVID-19 outbreak has contributed to a tremendous global decline in international trade flows. The rapid spread of the disease and the control measures implemented by governments to contain the virus have led to serious consequences for the global economy. The pandemic has affected the international movement of people, goods, and services. Currently, the systematic quantitative research investigating the effects of specific non-pharmaceutical intervention policy clusters on country-level international trade flows, remains limited. In this study, the Panel Vector Autoregression (PVAR) method was conducted using country-level panel data collected from various international sources including the United Nations, World Bank, and University of Oxford. The results show that stringent COVID-19 closure, social distancing, and containment measures and health-related measures, had significant negative impacts on trade flows. In contrast, economic support measures showed significant positive effects on trade. In summary, the findings suggest that policymakers should maintain less stringent containment measures related to public closure and movement restrictions and stimulate economic activities through economic support policies in order to minimize losses in trade flows during the pandemic. © 2023,ABAC Journal. All Rights Reserved.

Capital flows to emerging economies and global risk aversion during the COVID-19 pandemic

This paper analyses recent changes in the relative importance of the determinants of capital flows to emerging market economies. For this purpose, we estimate vector autoregressive (VAR) models for the period 2009–2021. Based on these models, we estimate the effects on debt flows from shocks to their determinants. Then, we quantify the contribution of each of the variables included in the model to explain the evolution of these flows in each month of the sample through a historical decomposition analysis. The main results indicate that the contribution of global risk aversion to explain the evolution of debt flows increased during March 2020 compared to the past, although its relative importance has decreased since, particularly as central banks in systemically important economies restored liquidity and the performance of financial markets improved. © 2023 John Wiley & Sons Ltd.

Leveraging growth spillovers to navigate CARICOM trade relations in the post-COVID-19 global space

Purpose: The phenomenon of growth spillover occurs because of domestic shocks, global shocks and shocks to a foreign country or region, and these are transmitted through specific channels. This study investigates the strength of the economic linkages between Caribbean Community (CARICOM) economies and its main traditional partners, including the European Union (EU-27), and emerging trading partners, such as China, with a view to determining the presence and extent of spillover growth which results from the interdependence among these economies. The paper hypothesizes that the presence of these spillovers can be leveraged to chart the future for the region's integration in the global sphere. Design/methodology/approach: Based on the existing theoretical and empirical literature, a structural vector autoregressive (SVAR) model was developed and employed to examine the strength of the economic linkages between CARICOM economies and its main trading partners, such as the United States (US), the United Kingdom (UK) and the EU-27, alongside some of the non-traditional partners such as China. This method has been widely used by institutions, such as the International Monetary Fund (IMF) and World Bank, to profile economic linkages between economies. To this end, the methodology was formulated based on the IMF Spillover Reports which were produced from 2011 to 2015. Findings: The model suggests that positive spillovers are likely to occur from continued deepened integration with the US, EU-27 and the UK, as traditional trade partners, but that opportunities also exist from a deliberate deepening of relations with non-traditional trade partners, for example, China. This becomes even more apparent when CARICOM is separated into categories consisting of more developed countries (MDCs) and less developed countries (LDCs). In addition, from the perspective of any trading partner, such as those in the EU-27, this research is relevant and timely as it contributes to the landscape of literature, which can be utilized for the purpose of negotiating parameters of trade and integration arrangements. Research limitations/implications: This study adds to the literature on evaluating the direction for deepened integration of CARICOM economies, both with selected traditional and non-traditional trade partners as the region pilots recovery in a post-pandemic global space. Practical implications: Policymakers can use the results of this study to leverage economic spillovers as a basis for determining which trade partners offer the most significant growth benefits as the region recovers from the COVID-19 pandemic and it will also assist in steering regional integration. This result also implies that over time, the comparative advantage structure of CARICOM member countries' export profile should change to reflect the import profile of its trade partners. To this end, this study can be used to inform and better position the respective trade and industrial development policies of countries in the Caribbean region as they attempt to deepen integration regionally and internationally. From the perspective of the partner, traditional trading relationships such as those which exist with European countries, such as the CARIFORUM-EU Economic Partnership Agreement, can be more deliberately utilized given the geographic benefits on offer with deepened relationships with economies in the Caribbean. Further, this research can also be a point of departure for future research. Originality/value: This study is among the few empirical works that examine spillover effects as a strategy for rebuilding economic growth in the post-COVID 19 era. This study adds to the literature on evaluating the direction for deepened integration of CARICOM economies, both with selected traditional and non-traditional trade partners as the region navigates recovery in a post-pandemic global space. © 2023, Emerald Publishing Limited.

COVID-19 effects on the S&P 500 index

This paper investigates the effects of the coronavirus disease 2019 (COVID-19) cases in the US on the S&P 500 Index using daily data covering the period between 21st January, 2020 and 10th August, 2021. The investigation is achieved by using a structural vector autoregression model, where a measure of the global economic activity and the spread between 10-year treasury constant maturity and the federal funds rate are also included. The empirical results suggest that having (Formula presented.) of an increase in cumulative daily COVID-19 cases in the US results in about (Formula presented.) of a cumulative reduction in the S&P 500 Index after 1 day and about (Formula presented.) of a reduction after 1 week. Historical decomposition of the S&P 500 Index further suggests that the negative effects of COVID-19 cases in the US on the S&P 500 Index have been mostly observed during March 2020. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

Economic forecasting with an agent-based model

We develop the first agent-based model (ABM) that can compete with benchmark VAR and DSGE models in out-of-sample forecasting of macro variables. Our ABM for a small open economy uses micro and macro data from national accounts, sector accounts, input–output tables, government statistics, and census and business demography data. The model incorporates all economic activities as classified by the European System of Accounts (ESA 2010) and includes all economic sectors populated with millions of heterogeneous agents. In addition to being a competitive model framework for forecasts of aggregate variables, the detailed structure of the ABM allows for a breakdown into sector-level forecasts. Using this detailed structure, we demonstrate the ABM by forecasting the medium-run macroeconomic effects of lockdown measures taken in Austria to combat the COVID-19 pandemic. Potential applications of the model include stress-testing and predicting the effects of monetary or fiscal macroeconomic policies. © 2022 The Author(s)

On the volatility of WTI crude oil prices: A time-varying approach with stochastic volatility

This study investigates the impacts of crude oil-market-specific fundamental factors and financial indicators on the realized volatility of West Texas Intermediate (WTI) crude oil price. A time-varying parameter vector autoregression model with stochastic volatility (TVP-VAR-SV) is applied to weekly data series spanning January 2008 to October 2021. It is found that the WTI oil price volatility responds positively to a shock in oil production, oil inventories, the US dollar index, and VIX but negatively to a shock in the US economic activity. The response to the EPU index was initially positive and then turned slightly negative before fading away. The VIX index has the most significant effect. Furthermore, the time-varying nature of the response of the WTI realized oil price volatility is evident. Extreme effects materialize during economic recessions and crises, especially during the COVID-19 pandemic. The findings can improve our understanding of the time-varying nature and determinants of WTI oil price volatility. © 2022

Quantile time-frequency connectedness between cryptocurrency volatility and renewable energy volatility during the COVID-19 pandemic and Ukraine-Russia conflicts

Our article employs a quantile vector autoregression (QVAR) to identify the connectedness of seven variables from April 1, 2019, to June 13, 2022, in order to examine the relationships between crypto volatility and energy volatility. Our findings reveal that the dynamic connectedness is approximately 25% in the short term and approximately 9% in the long term. The 50% quantile equates to the overall average connectedness of the entire period, according to dynamic net total directional connectedness over a quantile, which also indicates that connectedness is very intense for both highly positive changes (above the 80% quantile) and crypto and energy volatility (below the 20% quantile). With the exception of the early 2022 period when the Crypto Volatility Index transmits a net of shocks because of the Ukraine-Russia Conflict, dynamic net total directional connectedness implies that in the short term, the Crypto Volatility Index acts as a net shock receiver across time. While this indicator is a net shock receiver for long-term dynamics, wind energy is a net shock transmitter during the short term. Green bonds are a short-term net shock receiver. This role is valid in the long term. Clean energy and solar energy are the long-term net transmitters of shocks;nevertheless, the series is always and only momentarily a net receiver of shocks because of the short-term dynamics. Natural gas and crude oil play roles in both two quantiles. Dynamic net pairwise directional connectedness over a quantile suggests that uncertain events like the COVID-19 epidemic or Ukraine-Russia Conflict influence cryptocurrency volatility and renewable energy volatility. © 2022 Elsevier Ltd

INDONESIA'S FINANCIAL STRESS EVENTS AND MACROECONOMIC DYNAMICS

In this study, we use a Markov-Switching Bayesian Vector AutoRegression model to investigate the episodic relationship between financial stress and the key macroeconomic variables in the case of Indonesia. We find different nature of relationships among Indonesia's real sector variables (household consumption expenditure and consumer price index), financial sector variables (interbank money market rate) and the policy variable (broad money supply during the times of high and low financial stress). Regime changes occurred on several occasions, including during the 2008 global financial crisis period and at the beginning of the COVID-19 pandemic. © 2022 Authors. All rights reserved.

Improved prediction of new COVID-19 cases using a simple vector autoregressive model: evidence from seven New York state counties.

With the rapid spread of COVID-19, there is an urgent need for a framework to accurately predict COVID-19 transmission. Recent epidemiological studies have found that a prominent feature of COVID-19 is its ability to be transmitted before symptoms occur, which is generally not the case for seasonal influenza and severe acute respiratory syndrome. Several COVID-19 predictive epidemiological models have been proposed; however, they share a common drawback - they are unable to capture the unique asymptomatic nature of COVID-19 transmission. Here, we propose vector autoregression (VAR) as an epidemiological county-level prediction model that captures this unique aspect of COVID-19 transmission by introducing newly infected cases in other counties as lagged explanatory variables. Using the number of new COVID-19 cases in seven New York State counties, we predicted new COVID-19 cases in the counties over the next 4 weeks. We then compared our prediction results with those of 11 other state-of-the-art prediction models proposed by leading research institutes and academic groups. The results showed that VAR prediction is superior to other epidemiological prediction models in terms of the root mean square error of prediction. Thus, we strongly recommend the simple VAR model as a framework to accurately predict COVID-19 transmission.