Risk transmission between equity market of China and its trading partners: new evidence from various financial crises

PurposeThe authors examine the volatility connections between the equity markets of China and its trading partners from developed and emerging markets during the various crises episodes (i.e. the Asian Crisis of 1997, the Global Financial Crisis, the Chinese Market Crash of 2015 and the COVID-19 outbreak).Design/methodology/approachThe authors use the GARCH and Wavelet approaches to estimate causalities and connectedness.FindingsAccording to the findings, China and developed equity markets are connected via risk transmission in the long term across various crisis episodes. In contrast, China and emerging equity markets are linked in short and long terms. The authors observe that China leads the stock markets of India, Indonesia and Malaysia at higher frequencies. Even China influences the French, Japanese and American equity markets despite the Chinese crisis. Finally, these causality findings reveal a bi-directional causality among China and its developed trading partners over short- and long-time scales. The connectedness varies across crisis episodes and frequency (short and long run). The study's findings provide helpful information for portfolio hedging, especially during various crises.Originality/valueThe authors examine the volatility connections between the equity markets of China and its trading partners from developed and emerging markets during the various crisis episodes (i.e. the Asian Crisis of 1997, the Global Financial Crisis, the Chinese Market Crash of 2015 and the COVID-19 outbreak). Previously, none of the studies have examined the connectedness between Chinese and its trading partners' equity markets during these all crises.

A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices

The ongoing Russia–Ukraine conflict has exacerbated the global crisis of natural gas supply, particularly in Europe. During the winter season, major importers of liquefied natural gas (LNG), such as South Korea and Japan, were directly affected by fluctuating spot LNG prices. This study aimed to use machine learning (ML) to predict the Japan Korea Marker (JKM), a spot LNG price index, to reduce price fluctuation risks for LNG importers such as the Korean Gas Corporation (KOGAS). Hence, price prediction models were developed based on long short-term memory (LSTM), artificial neural network (ANN), and support vector machine (SVM) algorithms, which were used for time series data prediction. Eighty-seven variables were collected for JKM prediction, of which eight were selected for modeling. Four scenarios (scenarios A, B, C, and D) were devised and tested to analyze the effect of each variable on the performance of the models. Among the eight variables, JKM, national balancing point (NBP), and Brent price indexes demonstrated the largest effects on the performance of the ML models. In contrast, the variable of LNG import volume in China had the least effect. The LSTM model showed a mean absolute error (MAE) of 0.195, making it the best-performing algorithm. However, the LSTM model demonstrated a decreased in performance of at least 57% during the COVID-19 period, which raises concerns regarding the reliability of the test results obtained during that time. The study compared the ML models' prediction performances with those of the traditional statistical model, autoregressive integrated moving averages (ARIMA), to verify their effectiveness. The comparison results showed that the LSTM model's performance deviated by an MAE of 15–22%, which can be attributed to the constraints of the small dataset size and conceptual structural differences between the ML and ARIMA models. However, if a sufficiently large dataset can be secured for training, the ML model is expected to perform better than the ARIMA. Additionally, separate tests were conducted to predict the trends of JKM fluctuations and comprehensively validate the practicality of the ML models. Based on the test results, LSTM model, identified as the optimal ML algorithm, achieved a performance of 53% during the regular period and 57% d during the abnormal period (i.e., COVID-19). Subject matter experts agreed that the performance of the ML models could be improved through additional studies, ultimately reducing the risk of price fluctuations when purchasing spot LNG. [ 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.)

Lead the African way!

PurposeCountries in Africa have undergone an unprecedented transformation that has shaped the continent as they move ahead from the clutches of colonialism. The evolution of leadership and how organisations function optimally has given rise to the review of leadership approaches and practices, revolutionising its position in the global markets. With the recent spate of global suffering from the pandemic, the formal and traditional work structures are becoming transient. At the same time, the economic consequences of the Russo-Ukrainian crisis have catastrophic effects globally.Design/methodology/approachThe research was conducted via a systematic review of scientific sources from various academic websites. Eligibility criteria were defined with the agreement of pertinent themes and concepts.FindingsBy evaluating and analysing characteristics and success indicators from the blend of leadership competencies ascertained from Afrocentric principles in response to African associated problems, Africa can cement its leadership concepts without following the global north principles. These philosophies are resilient enough to contend with a range of VUCA (volatility, uncertainty, complexity and ambiguity) complexities, including the impact of the recent global pandemic of immeasurable proportions and the prospect of war as the Russo-Ukrainian conflict intensifies.Originality/valueWithin the African environment, there is a greater focus on the human element in shared values, holistic well-being, cooperation and experience. The global community band together to deal with these complexities. This is a typical example of global connectedness with positive and negative connotations.

Volatility spillovers among major tourism stock indices during Covid-19 pandemic

This paper examines the dynamics of volatility spillovers among five major tourism stock indices during the Covid-19 period. Our paper enriches the current literature as it is the first paper to investigate the volatility spillovers among major global tourism stock indices by adopting Diebold and Yilmaz (2012. Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57–66. ), and Barunik and Krehlik (2018. Measuring the Frequency Dynamics of Financial Connectedness and Systemic Risk. Journal of Financial Econometrics, 16(2), 271–296.) time and frequency domain methods. Results suggest that total spillovers of the tourism stock indices rose significantly during the pandemic. Turkey and Italy are net volatility spillover transmitters, and others are net volatility spillover receivers. Findings of this study also indicates that the effect of volatility transmission among tourism stock markets is temporary (short-lasting). The results suggest that short-term investors and portfolio managers should avoid investing in the tourism indices in the short term.

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.

Machine Learning and Deep Learning based Stock Market Prediction considering Covid-19 as a Feature

India's capital markets are witnessing intense uncertainty due to global market failures. Since the outbreak of COVID-19, risk asset prices have plummeted sharply. Risk assets declined half or more compared to the losses in 2008 and 2009. The high volatility is likely to continue in the short term;as a result, the Indian markets have declined sharply. In this paper, we have used different algorithms such as Gated Recurrent Unit, Long Short-Term Memory, Support Vector Regressor, Decision Tree, Random Forest, Lasso Regression, Ridge Regression, Bayesian Ridge Regression, Gradient Boost, and Stochastic Gradient Descent Algorithm to predict financial markets based on historical data available along with economic and financial features during this pandemic. According to our findings, deep learning models can accurately estimate financial indexes by utilizing non-linear transaction data. We found that the Gated Recurrent Unit performs better than the existing model. © 2023 IEEE.

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.

Precious Metals Comovements in Turbulent Times: COVID-19 and the Ukrainian Conflict

We examined the evolution of cross-market linkages between four major precious metals and US stock returns, before (Phase I) and after (Phase II) the COVID-19 outbreak. Phase II was also extended to encompass the Ukrainian conflict, which prolonged the period of uncertainty in financial markets. Due to the increase in volatility observed in Phase II, we used a heteroskedasticity-adjusted correlation coefficient to examine the evolution of correlation changes since the COVID-19 outbreak. We also propose a relevant dissimilarity measure in multidimensional scaling analysis that can be used for depicting associations between financial returns in turbulent times. Our results suggest that (i) the correlation levels of gold, silver, platinum, and palladium returns with US stock returns have not changed substantially since the COVID-19 outbreak, and (ii) all precious metal returns exhibit movements that are less synchronized with US stock returns, with palladium and gold being the least synchronized. © 2023 by the author.

The impact of price insulation on world wheat markets during COVID-19 and the Ukraine crisis

This paper begins with a survey of recent commodity price developments that highlights the magnitude of this price surge and identifies the rapid rise in wheat prices as a key element. The analysis in this paper focuses on the extent to which domestic markets are insulated from these changes and on the resulting impacts on world prices. An econometric analysis using Error Correction Models finds stable long-term relationships between world wheat prices and most domestic prices of wheat and wheat products, but with considerable variation across countries in the rate of price transmission. A case study of the price shocks during the Covid pandemic and the Ukraine food price crisis finds that price insulation roughly doubled the overall increase in world wheat prices and raised their volatility both during periods of price increase and price decline.

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.

Reaction of the cereal market to the disruption of trade flows in Romania

The paper presents the reaction of the Romanian cereal market to the disruption of trade flows caused by certain shocks, such as the COVID-19 pandemic, which lead to changes with high impact on the functioning of this market, representing an important test for the resilience of the sector. Due to trade liberalization in global markets, including agri-food markets, the competitiveness of exports has become increasingly important, contributing to the creation of the country's competitive advantage. Any restrictions to trade in agri-food products can distort trade flows, and this disruption will have an impact on supply and prices. Maintaining a balance between imports and exports is essential to ensure domestic market stability. International trade in agri-food products plays an important role in global food security. The results show that Romania mainly exports unprocessed agricultural products, with cereals having the largest share in the export structure, cereal supply is dependent on climate change, yet it is one of the products with the lowest volatility. The cereal market shows a more elastic reaction to price responses, even though demand for staple foods is generally inelastic.

Modelling volatility effects between stock, oil, gold and forex markets: Evidence from India

Although several studies on the integration of diverse stock markets have been conducted in the financial literature, most of them have focused on the integration and volatility spillovers across established stock markets. The present study explores the dynamics of integration and volatility spillover across gold, oil, forex, and stock markets during four significant events in India: the pre-changed government regime, the post-changed government regime, the post-Brexit referendum date, and the COVID era. Daily data from 2010 to 2022 is divided into four categories using the Chow test. This is done to examine if these events' financial turmoil affects market interconnectivity. The unit root test determines data stationarity. The ARCH LM test examines series volatility clustering, and the BEKK GARCH test examines market volatility spillover. Results indicate that gold cannot be considered a hedge or safe haven. Secondly, market interconnectedness increased during the crisis period. Third, domestic political and geopolitical conditions globally do not increase the scale of spillover amongst financial assets, though they impact the spillover's magnitude. The results of this study have several important implications for portfolio diversification and risk management. © Varsha Ingalhalli, Prachi Kolamker, 2023.

Forecasting in the absence of precedent

We survey approaches to macroeconomic forecasting during the COVID‐19 pandemic. Due to the unprecedented nature of the episode, there was greater dependence on information outside the econometric model, captured through either adjustments to the model or additional data. The transparency and flexibility of assumptions were especially important for interpreting real‐time forecasts and updating forecasts as new data were observed. We revisit these themes with a time‐varying parameter (TVP) vector autoregression (VAR), which attributes the large jumps primarily to increased volatility rather than changes in the type or propagation of shocks.

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.

Economic Policy Uncertainty and Emerging Stock Market Volatility

This research examines the effect of economic policy uncertainty (EPU) indices on Pakistan's stock market volatility. Particularly, we examine the impact of the economic policy uncertainty index for Pakistan and bilateral global trading partner countries, the US, China, and the UK. We employ the GARCH-MIDAS model and combination forecast approach to evaluate the performance of economic uncertainty indices. The empirical findings show that the US economic policy uncertainty index is a more powerful predictor of Pakistan stock market volatility. In addition, the EPU index for the UK also provides valuable information for equity market volatility prediction. Surprisingly, Pakistan and China EPU indices have no significant predictive information for volatility forecasting during the sample period. Lastly, we find evidence of all uncertainty indices during economic upheaval from the COVID-19 pandemic. We obtained identical results even during the Covid-19. Our findings are robust in various evaluation methods, like MCS tests and other forecasting windows.

A review on drawdown risk measures and their implications for risk management

As highlighted by the recent market turmoil following COVID‐19, markets can experience significant retracements or drawdowns. While these recent market moves have definitely been large, significant drawdowns have been around since the start of financial markets. Various risk metrics such as Value at Risk and volatility are used to describe risk. The intuitive drawdown risk measure, which is often used in practice alongside the above metrics, is receiving more and more academic attention. In this article we provide a systematic review of the literature on the drawdown risk measures. We describe two different methodologies for calculating drawdowns and analyze drawdown based risk measures used in risk management, portfolio construction and optimization. Finally we discuss the statistical properties related to drawdowns. Based on the research done so far, we identify several areas for further research.

The impact on supply chain networks of shifting demand and supply dynamics

The supply chain networks that support a business have usually evolved over time, shaped by various market and supply forces and by the expectations and strategic intent of a series of leaders. Recent shocks and higher awareness of risk and shifts on both the demand and supply side are expected to require global networks to be reassessed. This chapter reviews some of the key changes impacting global chains and considers the implications for future supply chain networks and the people who will manage them. © Springer Nature Switzerland AG 2023. All rights reserved.

Evolution of oilseed sector in Romania - an analysis from the perspective of effects produced by the COVID-19 pandemic

Romania ranks first in the European Union for the production of sunflower seeds, third for the production of soybeans and seventh for the production of rapeseed. The paper aims to analyse the effects produced by the COVID-19 pandemic on the evolution of the oilseed sector in Romania. Thus, the following indicators were analysed: evolution of areas under oilseeds, total oilseed production and average yields, as well as the volatility of selling prices for oilseeds. The results of the study reveal that Romania has been the largest producer of sunflower seeds in the European Union. The average yields in sunflower, soybeans and rapeseed have shown great variations in the analysed period. According to Eurostat data, it can be noticed that although Romania is the third large producer of oilseeds in the EU, the average yields continue to be low compared to those from other large EU producers. Yields are also among the most volatile in the EU. The selling prices for soybeans showed a higher increase in the year 2020 than in 2019 in Romania, compared to the increase in the average selling prices of the EU-27 (+9.89%). The selling prices for rapeseed also had a higher increase in 2020 than in 2019 in Romania, compared to the increase in the average selling prices of the EU-27 (+2.31%).

Using Survey Information for Improving the Density Nowcasting of U.S. GDP

We provide a methodology that efficiently combines the statistical models of nowcasting with the survey information for improving the (density) nowcasting of U.S. real GDP. Specifically, we use the conventional dynamic factor model together with stochastic volatility components as the baseline statistical model. We augment the model with information from the survey expectations by aligning the first and second moments of the predictive distribution implied by this baseline model with those extracted from the survey information at various horizons. Results indicate that survey information bears valuable information over the baseline model for nowcasting GDP. While the mean survey predictions deliver valuable information during extreme events such as the Covid-19 pandemic, the variation in the survey participants' predictions, often used as a measure of "ambiguity,” conveys crucial information beyond the mean of those predictions for capturing the tail behavior of the GDP distribution.

Natural resources and financial development: Role of corporate social responsibility on green economic growth in Vietnam

In this study, the relationship among natural resources, financial development, and the role of corporate social responsibility (CSR) on green economic growth in Vietnam has been analyzed. We have applied the Pooled Mean Group-Autoregressive Distributed Lag (PMG-ARDL) model to assess this relationship for the period of 1990-2018. The Johansen-Fisher panel cointegration and Kao tests show that the variables are cointegrated. Accordingly, CSR, the PMG-ARDL findings, financial development, and natural resources development all have a long-term positive association but a short-term negative relationship with green economic growth. If efficient fiscal and financial management measures are not implemented, the panel nations' public debt sustainability will be jeopardized due to CSR's overreliance on natural resources rents (NRR). Natural resources may have a detrimental impact on financial growth if laws are not implemented. CSR regulations in Vietnam may reduce greenhouse gases (GHG) emissions and promote green economic growth. If this criterion is met, NRR-related improvements in financial development may be sustained, and relevant policy recommendations may be made.