Impact of Covid-19 on economic recovery: empirical analysis from China and global economies

This research aims to utilize quarterly global VAR data from April 1, 2020, to September 30, 2021, to assess the influence of the economic recovery of China following the COVID-19 outbreak on global economies. China is one of the first big economies globally to show indications of recovery following the COVID-19 pandemic. The nation's economic growth has the biggest long-term influence on middle-income nations (0.17%) followed by low- and middle-income economies (0.16%) and high-income economies (0.16%) (0.15%). The chain reaction of China's economic growth is most visible in high-income nations (0.11–0.45%), followed by middle-income countries (0.08–0.33%) and low-income countries (0.02–0.05%). Our findings show that the post-COVID-19 economic rebound in China will mostly benefit middle-income nations, with low- and middle-income countries following closely after. After COVID-19, the influence of the economic recovery of China is most visible in the rise of energy consumption in high-income nations, followed by middle-income economies. It is also worth noting that the influence of China's economic expansion on low- and middle-income economies does not always imply a rise in energy consumption. Overall, China's economic recovery has a significantly stronger influence on other countries' economic development than other countries' energy consumption has on other economies' growth.

Distribution of credit-risk concentration in particular sectors of the economy, and economic capital before and during the COVID-19 pandemic

The aim of the work underpinning this paper has been to track the evolution of tail risk in banks' NPL portfolios present under normal and worst conditions (before and during the pandemic of COVID-19), and to estimate the impact of sector concentration risk on amounts of economic capital. Results further allowed for analysis of different sectors with a view to determining which is riskiest. The study makes use of a multi-factor structural model, given that each sector is affected by a different systematic risk factor, with the assets of borrowers from the same sector thus correlated markedly, even as correlations between sectors are low. The research has in fact sought the further development of methodology proposed by Düllmann and Masschelein in 2006—in the direction of improved accuracy of economic-capital estimates, thanks to alternate means of mapping out the sectoral factor correlation matrix. The empirical analysis was based on individual data from Prudential Reporting under the National Bank of Poland, as well as market data. Results reveal an increase in tail risk through the 2015–2017 period, as followed by the onset of a decline. Where the paper's second aim is concerned, there is found to be support for the idea that economic capital may be increased where sector concentration in the portfolio of a bank is accounted for. Tail risk is found to be concentrated in the sectors of construction and real estate, with accommodation and food services becoming more volatile during the pandemic. A channel for risk transfer between the financial and corporate sectors is thus found to exist. Thanks to the work done we have a better understanding of the impact of sectoral concentration of individual banks' lending activities on level of risk, with the possibility of this gaining application as stress tests are conducted, and as supervisory recommendations from Poland's Financial Supervision Authority are formulated.

Can we learn lessons from the past? COVID‐19 crisis and corporate governance responses

The coronavirus (COVID‐19) pandemic has seriously threatened the lives of the people. The pandemic has also threatened the survival of the firms, which has drawn the attention of policymakers and corporate governance practitioners around the world. In this study, we focus on how corporate governance practices can help firms to survive during COVID‐19 crisis. For this purpose, we take insights from prior crises by reviewing leading business journals articles and identify key corporate governance mechanisms that could potentially be effective in the ongoing COVID‐19 crisis. Our review of a large body of literature highlights several governance mechanisms that may help firms to cope with COVID‐19 crisis. These governance attributes include risk management committees, board diversity, independent directors, foreign investors, institutional ownership, ownership concentration, CEO's dual roles, block ownership, and family ownership. We provide several policy implications after reviewing the corporate governance literature. Our review illustrates that firms may be subject to at least one of the identified governance mechanisms and they may learn how these governance attributes can be effective in the COVID‐19 crisis. Our review illustrates that independent risk management committees, institutional ownership, board independence, blockholders, and family ownership are some of the essential and effective governance mechanisms compared to other governance attributes during COVID‐19 crisis.

Emerging market debt and the COVID‐19 pandemic: A time–frequency analysis of spreads and total returns dynamics

We apply wavelet analyses to study the impact of COVID‐19 pandemic on the performance of emerging market bonds, in both investment grade and high yield ranges of creditworthiness. Our results show varying level of coherence ranging from low, medium and high between the Coronavirus Media Coverage index and the price moves of the emerging market USD‐denominated debt. We attribute the intervals of low coherence levels to the diversification potential during a systemic pandemic such as COVID‐19 of investments in bonds issued by developing economies. We document differences in patterns exhibited by various indices describing behaviour of option‐adjusted spreads and total returns as a function of credit quality of issuers form emerging market economies. We report well‐defined zones of the regime switching between the lead and lag roles of the emerging market bonds vis‐à‐vis the media coverage.

The profitability of pair trading strategy in stock markets: Evidence from Toronto stock exchange

Market practitioners and speculators attempt to make benefits from the existence of market price gaps and profit opportunities by arbitrage strategies. Although some investors trade stocks based on the available financial and fundamental information of a particular share, there are others who make profits by risk hedging and swing trading opportunities. One of these strategies is pairs trading, which is a sub‐category of statistical arbitrage. Pairs trading can assure reasonably a risk‐free profit gaining. This paper aims to make a hypothetical portfolio composed of pairs of stocks by exploring a significant association between their prices in the Toronto Stock Exchange, TSX. We compare the profitability of distance, co‐integration, and copula functions as the pair's selection and trading strategy devices in TSX over January 2017 to June 2020. Our results show that the highest profitability comes from trading by the copula method. Our time frame includes two heterogeneous pre and post COVID‐19 periods. Although the financial markets are struggling with a hard situation over the COVID‐19 days, the performance of the methodologies is not affected by the crisis.

Sectoral volatility spillovers and their determinants in Vietnam

Using the vector autoregression (VAR) connectedness approach, this paper investigates dynamic volatility spillovers across 14 sectors in Vietnam's stock market over the period 2012–2021. The study also explores the differences in sectoral spillovers before and after the outbreak of Covid-19 pandemic. Additionally, the paper also investigates the effects of the current pandemic and macroeconomic fundamentals on intersectoral connectedness in Vietnam. Our findings show that volatility transmission across sectors fluctuates significantly over the research period and spikes during the Covid-19 pandemic. The total spillover index is approximately 64.23 per cent, indicating that volatility spillovers across the Vietnamese sectors are substantial. The risks from the stock market appear to spread quickly and easily across sectors in Vietnam. Among these 14 sectors, food, fisheries, and oil and gas act as net senders of risks while real estate and pharmacy are the greatest receivers of risk. The findings also confirm that the commerce, transportation, manufacturing, and service sectors are more sensitive to the Covid-19 pandemic crisis than other sectors in Vietnam. Furthermore, the empirical results show that an increase in daily Covid-19 infections increases volatility spillover across sectors. Policy implications have emerged based on these findings from this paper for the Vietnamese government and other emerging countries.

AUTOMATIZACION Y PANDEMIA: AMENAZAS SOBRE EL EMPLEO EN AMERICA LATINA *

En este trabajo estudiamos el riesgo de automatización, la inviabilidad del teletrabajo y el riesgo de contagio por proximidad física en las seis economías más grandes de América Latina. Encontramos que son los trabajadores con bajo nivel educativo, informales, y de bajos salarios los más expuestos a este tipo de riesgos. La automatización y las crisis sanitarias conllevan potenciales efectos desigualadores. Adicionalmente, el trabajo aporta evidencia acerca de interacciones entre ambos riesgos. La pérdida de empleo en situaciones de pandemia puede ser más acelerada en ocupaciones donde el trabajo remoto es menos viable y el riesgo de automatización es mayor.Alternate :We study the risk of automation, the unfeasibility of teleworking and the risk of contagion due to physical proximity in the six largest economies in Latin America. We find that workers with low education, informal, and low-wage levels are the most exposed to this type of risk. Automation and health crises may have significant unequalizing effects. In addition, this work provides evidence on interactions between the two risks. Job loss in pandemic situations may be more accelerated in occupations where remote work is less feasible and the risk of automation is higher.

The impact of COVID‐19 on unemployment rate: An intelligent based unemployment rate prediction in selected countries of Europe

Unemployment remains a major cause for both developed and developing nations, due to which they lose their financial and economic impact as a whole. Unemployment rate prediction achieved researcher attention from a fast few years. The intention of doing our research is to examine the impact of the coronavirus on the unemployment rate. Accurately predicting the unemployment rate is a stimulating job for policymakers, which plays an imperative role in a country's financial and financial development planning. Classical time series models such as ARIMA models and advanced non‐linear time series methods be previously hired for unemployment rate prediction. It is known to us that mostly these data sets are non‐linear as well as non‐stationary. Consequently, a random error can be produced by a distinct time series prediction model. Our research considers hybrid prediction approaches supported by linear and non‐linear models to preserve forecast the unemployment rates much precisely. These hybrid approaches of the unemployment rate can advance their estimates by reproducing the unemployment ratio irregularity. These models' appliance is exposed to six unemployment rate statistics sets from Europe's selected countries, specifically France, Spain, Belgium, Turkey, Italy and Germany. Among these hybrid models, the hybrid ARIMA‐ARNN forecasting model performed well for France, Belgium, Turkey and Germany, whereas hybrid ARIMA‐SVM performed outclass for Spain and Italy. Furthermore, these models are used for the best future prediction. Results show that the unemployment rate will be higher in the coming years, which is the consequence of the coronavirus, and it will take at least 5 years to overcome the impact of COVID‐19 in these countries.