Income Recovery of Thai Workers From the COVID-19 Pandemic

This paper examines how COVID-19 and the resultant lockdown affected Thai workers and how their income has recovered as of the end of 2020. We conducted three phases of telephone surveys to track the income dynamics of Thai workers during the months of May, August, and November 2020. The initial COVID-19 impact on Thai worker income was enormous and very broad. On average, Thai workers' income fell by 47.03%, and 69.7% suffered such a loss. Over the six months survey period, most Thai workers had just begun to stabilize their income, but only a few were actually able to recover. Quantile regression analysis revealed particular factors that influenced income recovery. For example, being a formal worker tended to help one's income to recover faster. Interestingly, COVID-19 assistance schemes from the government, although essential to those in need, had a negative impact on income recovery. On the other hand, the cheap loan policy seems to have been more effective as workers whose incomes were in the middle and the top quantiles experienced faster income recovery. © 2023 by De La Salle University.

Demystifying the Impact of COVID-19 on Structural Volatility of Indian Sectoral Market: A Quantile Regression Analysis

The present study explores the impact of COVID-19 on the volatility structure of the sectoral market in India. ARMA(p,q)- GJR-GARCH(1, 1)-std model is used to determine the daily conditional volatility for 13 selected sectors over the period starting from January 2020 to December 2021. The quantile regression model is employed to examine the changes in the structure of volatility in each sector over the pandemic duration. The results of the study show that the volatility of Metal, Oil–Gas and PSU are more sensitive to market volatility, whereas the volume of new COVID-19 cases exceeds the threshold limit, and no extreme spillover is observed from the market volatility. In addition to this, Bankex, Metal, Oil–Gas, Private Banks and Power sector volatility are more responsive to news sentiments during the period of increase in new COVID-19 cases. Furthermore, the results also reveal that news sentiments help to control the significant fluctuation in the sectoral market. © 2023 MDI.

Flight-to-quality or not? Evidence from China's green bond and green equity markets during COVID-19 crisis

This study investigates the time-varying interdependence relationships between green bonds and green equity returns in China before and during the COVID-19 period. The rolling-window Copula Quantile-on-Quantile regression method has been employed to capture the dynamic dependence structure of the asset returns. The empirical results are as follows: First, the green bond-green equity correlations have increased significantly during the COVID-19 pandemic era. Second, the heterogeneous dependencies across different quantiles show the time-varying information transmission mechanism between green financial markets depending on the market conditions. Specifically, the correlations have increased around median level given pandemic shocks and an opposite correlation movement can be found in extreme quantiles, supporting the ‘flight-to-quality' effect.

Semiparametric estimation of expected shortfall and its application in finance

Measuring risk effectively is crucial for managing risk in financial markets. The expected shortfall has become an increasingly popular metric for risk in recent years. How to estimate it is important in statistics and financial econometrics. Based on the single index quantile regression, we introduce a new semiparametric approach, namely, weighted single index quantile regression. We assess the performance of the proposed expected shortfall estimator with backtesting. Our simulation results indicate that the estimator has a good finite sample performance and often outperforms existing methods. By applying the new method to both a market index and individual stocks, we show that it not only exhibits the best performance but also reveals an insight about the effect of the COVID pandemic, that is, the pandemic increases the market risk.

Economic policy uncertainty, governance institutions and economic performance in Africa: are there regional differences?

In recent years, the global economy has witnessed several uncertainty-inducing events. However, empirical evidence in Africa on the effects of economic policy uncertainty (EPU) on economic activities remains scanty. Besides, the moderating effect of governance institutions on the uncertainty-economic performance relationship in Africa and the likelihood of regional differences in the response of economic activities to EPU on the continent are yet to be investigated. To address these gaps, we applied system GMM and quantile regressions on a panel of forty-seven African countries from 2010 to 2019. We find that while global EPU and EPUs from China, USA and Canada exert considerable influence on economic performance in Africa, the effects of domestic EPU and EPUs from Europe, UK, Japan, and Russia were negligible, suggesting that African economies are resilient to these sources of uncertainty shocks. We also find that governance institutions in Africa are not significantly moderating the uncertainty-economic performance relationship. However, our results highlighted regional differences in the response of economic activities to uncertainty, such that when compared to East and West Africa, economic performance in Central, North and Southern Africa is generally more resilient to global EPU and EPUs from China, USA, Europe and UK. We highlighted the policy implications of these findings.

Using Macrofinancial Models to Simulate Macroeconomic Developments During the COVID-19 Pandemic: The Case of Albania

The COVID-19 pandemic, and its impact on the economy tested the capacity of current macroeconomic models to forecast economic developments in turbulent times. In this article, we develop a linear macrofinancial model for Albania and examine whether it can predict the developments of key macroeconomic and financial variables during 2020-2021. To address increased uncertainty in the forecasts, we construct uncertainty bands with quantile regressions. The results indicate that, in general, a linear model is flexible enough to analyze non-linear events and may thus be used in abnormal times.

The disproportionate effect of COVID-19 on the labour market in South Korea

COVID-19 delivered distributional effects on the labour market. Therefore, policymakers concentrated on designing more efficient and effective fiscal supports to alleviate economic damages. To draw more specific pictures, this study estimates who faced harder impacts and how much they experienced economic difficulties in Korea using unconditional quantile regression and separating vulnerable sectors, such as face-to-face industries. The findings suggest that self-employed workers in the vulnerable industries at low-income percentiles presented the most severe damages. Second, self-employed workers in other sectors, temporary workers in the vulnerable industries, and low-income households also had serious impacts. Third, female workers, who have a primary child-care duty, experienced serious negative effects due to the regulation disparity between strict school closure measures and generous workplace restrictions. In this regard, financial supports should aim to target more damaged groups instead of universal benefits. Furthermore, it is also important to improve child-care services to mitigate gender inequality.

Herding behavior and systemic risk in global stock markets

This paper provides new evidence of herding due to non- and fundamental information in global equity markets. Using quantile regressions applied to daily data for 33 countries, we investigate herding during the Eurozone crisis, China's market crash in 2015–2016, in the aftermath of the Brexit vote and during the Covid-19 Pandemic. We find significant evidence of herding driven by non-fundamental information in case of negative tail market conditions for most countries. This study also investigates the relationship between herding and systemic risk, suggesting that herding due to fundamentals increases when systemic risk increases more than when driven by non-fundamentals. Granger causality tests and Johansen's vector error-correction model provide solid empirical evidence of a strong interrelationship between herding and systemic risk, entailing that herding behavior may be an ex-ante aspect of systemic risk, with a more relevant role played by herding based on fundamental information in increasing systemic risk.

The role of COVID-19 in herding: evidence from the Croatian stock market

This paper explores the role of the COVID-19 pandemic on the herding behaviour across market participants in the Croatian market. The analysis uses daily prices of the Croatian stock index, spanning the period January 2016 to December 2022. The hypothesis of the herding behaviour is tested through the quantile regression approach. The findings document no evidence of herding prior to the pandemic crisis. In contrast, herding is discovered during the COVID-19 period. The paper provides policymakers and investors with valuable information to draw significant measures in their investment portfolio management during crises and pandemics. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

COVID-19 pandemic and stock market performance: A comparative study of emerging economies.

The paper investigates the co-movement of COVID-19 pandemic and performance of stock markets of four emerging economies. The Quantile-on-Quantile regression model was applied to daily share prices of stock markets from March 13, 2020 to November 30, 2021 in these economies. The results indicate varied relationships across various quantiles of COVID-19 cases and share prices. Whilst both positive and negative relationships are established at different quantiles of share prices for Brazil and Kenya, negative co-movements are recorded for India and South Africa for all quantiles of share prices. The varying dependence between COVID-19 and stock markets provide critical insights to policy makers.

Does US Infectious Disease Equity Market Volatility Index Predict G7 Stock Returns? Evidence Beyond Symmetry

During the COVID-19 pandemic, Baker et al. (2020) [The unprecedented stock market reaction to COVID-19. The Review of Asset Pricing Studies, 10, 742–758.] proposed the infectious disease equity market volatility (ID-EMV) index, which tracks US equity market volatility caused by infectious diseases. We extended the literature by using this newly developed ID-EMV index to examine its asymmetric effect on the share market returns of the G7 countries, which include the United Kingdom, Italy, Japan, Germany, France, Canada, and the United States of America. Moreover, we used novel techniques like the quantile-on-quantile regression test, quantile cointegration test, and quantile unit root test. The quantile cointegration test indicates that the infectious disease EMV index is cointegrated with G7 stock returns. Moreover, the quantile-on-quantile regression technique reveals that the infectious disease index positively affects stock returns during bullish states of the stock markets. In contrast, it negatively affects stock returns during bearish states of the stock market returns. The negative effect of the bearish states implies that investors may discourage investments during the downturns of the economy, whereas they need to boost their investments during economic booms.

Wild Bootstrap-Based Bias Correction for Spatial Quantile Panel Data Models with Varying Coefficients

This paper studies quantile regression for spatial panel data models with varying coefficients, taking the time and location effects of the impacts of the covariates into account, i.e., the implications of covariates may change over time and location. Smoothing methods are employed for approximating varying coefficients, including B-spline and local polynomial approximation. A fixed-effects quantile regression (FEQR) estimator is typically biased in the presence of the spatial lag variable. The wild bootstrap method is employed to attenuate the estimation bias. Simulations are conducted to study the performance of the proposed method and show that the proposed methods are stable and efficient. Further, the estimators based on the B-spline method perform much better than those of the local polynomial approximation method, especially for location-varying coefficients. Real data about economic development in China are also analyzed to illustrate application of the proposed procedure.

Government Bonds and COVID-19. An International Evaluation Under Different Market States.

JEL CLASSIFICATION: C22, C51, F21, G12, G32, H12.

Model Diagnostics and Forecast Evaluation for Quantiles

Model diagnostics and forecast evaluation are closely related tasks, with the former concerning in-sample goodness (or lack) of fit and the latter addressing predictive performance out-of-sample. We review the ubiquitous setting in which forecasts are cast in the form of quantiles or quantile-bounded prediction intervals. We distinguish unconditional calibration, which corresponds to classical coverage criteria, from the stronger notion of conditional calibration, as can be visualized in quantile reliability diagrams. Consistent scoring functions-including, but not limited to, the widely used asymmetric piecewise linear score or pinball loss-provide for comparative assessment and ranking, and link to the coefficient of determination and skill scores.We illustrate the use of these tools on Engel's food expenditure data, the Global Energy Forecasting Competition 2014, and the US COVID-19 Forecast Hub.

Analysis of the Impact of Industrial Structure Upgrading and Energy Structure Optimization on Carbon Emission Reduction

In China, there has been a significant increase in carbon emissions in the new era. Therefore, evaluating the influence of industrial structure upgrades and energy structure optimization on reducing carbon emissions is the objective of this research. Based on the provincial panel data of 30 provinces and cities across China from 1997 to 2019, this paper builds up a fixed-effect panel quantile STIRPAT model to investigate the differences in the impact of industrial structure on carbon emission intensity at different quantile levels from the provincial perspective, and as a way of causality test, the mediation effect model is adopted to empirically test the transmission path of "industrial structure upgrading-energy structure optimization-carbon emission reduction". The research results show that: (1) Both industrial structure upgrades and energy structure optimization have significant inhibitory effects on carbon emissions, and there are regional heterogeneities. (2) The upgrading of industrial structure has a significant positive effect on optimizing energy structure. (3) The upgrading of industrial structure can not only directly restrain carbon emissions but also indirectly have a significant inhibitory effect on carbon emissions by promoting the optimization of energy structure. Based on the above conclusions, corresponding policy recommendations are proposed to provide suggestions for China to achieve the goal of carbon neutrality.

Determinants of International Tourism Inbound Receipts: The Quantile Regression Approach

Challenges in tourism arising from the COVID-19 pandemic, e.g., a decline in domestic and international travel, are forcing destinations to focus on improving tourism performance. Therefore, it is important for stakeholders to know the key determinants affecting tourism performance. This paper aims to find out whether a country's international tourism inbound receipts are determined by GDP, the number of international arrivals, and travel and tourism competitiveness. The proposed model for 125 countries is specific because we consider conditional quantiles of the dependent variable. The advantage of quantile regression is that it can determine whether individual percentiles of a dependent variable are more affected by independent variables than other percentiles of a dependent variable, which is then reflected in the change in regression coefficients. This study contributes to the existing literature that includes TTCI as an independent variable in tourism performance models.

Asymmetric effects and volatility transmission from metals markets to solar energy stocks: Evidence from DCC, ADCC, and quantile regression approach

Detrimental environmental repercussions have recently given rise to an interest in green investments. Although solar energy stocks are appealing assets for ethical investors, little is known about their dynamic correlations and linkages with metal (silicon, lithium, and rare earth) markets, particularly during economic events which is essential for hedging effectiveness and asset allocation. This study investigates the nexus between metal markets, oil price volatility (OVX), market sentiments (VIX), and solar energy markets using DCC, ADCC models, and the quantile regression approach. The results show both symmetric and asymmetric shock spillover between metals markets, VIX, OVX, and solar energy markets which are more prominent during COVID-19 pandemic, US-China trade frictions, and Russian invasion of Ukraine. For portfolio management, the hedging effectiveness of lithium stocks is highest, followed by silicon and rare earth metals. However, the hedge ratios are time-varying, and the variability is highest during US-China trade frictions. The quantile regression estimates reveal that lithium market is the most persistent determinant of solar energy stocks followed by silicon market even after segregating the periods into Paris Agreement and COVID-19 pandemic. Thus, lithium and silicon are driving markets of solar energy markets and can be a cause of omitted variable bias if stay unobserved. Nonetheless, there is little influence of VIX, rare earth metals, and OVX on solar energy stocks. Lastly, the estimations of threshold regression suggest that market sentiments change the association between metal markets and solar energy markets after the VIX reaches a certain threshold level. © 2023

A comparative analysis of cryptocurrency returns and economic policy uncertainty pre- and post-Covid-19

This study analyzes the impact of economic policy uncertainty (EPU) on cryptocurrency returns for a sample of 100 highly capitalized cryptocurrencies from January 2016 to May 2021. The results of the panel data analysis and quantile regression show that increases in global EPU have a positive impact on cryptocurrency returns for lower cryptocurrency returns quantiles and an adverse impact for upper quantiles. In line with the existing literature, the Covid-19 pandemic resulted in higher returns for cryptocurrencies. Inclusion of a Covid-19 dummy in the models strengthened the impact of EPU on cryptocurrency returns. Furthermore, the relationship between the change in EPU and cryptocurrency returns was direct in the pre-Covid-19 period but inverse in the post-Covid-19 period. These results imply that cryptocurrencies act more like traditional financial assets in the post-Covid-19 era. © 2023 Elsevier B.V.

Risk Measure between Exchange Rate and Oil Price during Crises: Evidence from Oil-Importing and Oil-Exporting Countries

This study investigates the risk spillover effect between the exchange rate of importing and exporting oil countries and the oil price. The analysis is supported by the utilization of a set of double-long memories. Thereafter, a multivariate GARCH type model is adopted to analyze the dynamic conditional correlations. Moreover, the Gumbel copula is employed to define the nonlinear structure of dependence and to evaluate the optimal portfolio. The conditional Value-at-Risk (CoVaR) is adopted as a risk measure. Findings indicate a long-run dependence and asymmetry of bidirectional risk spillover among oil price and exchange rate and confirm that the risk spillover intensity is different between the former and the latter. They show that the oil price has a stronger spillover effect in the case of oil exporting countries and the lowest spillover effect in the case of oil importing countries.