Financial stress spillover network across Asian countries in the context of COVID-19

Using the dynamic connectedness framework of Antonakakis et al. (2020), this paper explores the financial stress spillover characteristic across nine Asian countries during major economic, political and public health emergency events, especially during COVID-19. We first find a substantial increase in the intensity of total financial stress spillover across nine Asian countries during COVID-19. Second, there are clear differences in the financial stress spillover networks across Asian countries during different economic and political events. In particular, in the first three months after the outbreak of COVID-19, there was considerable month-to-month variation in the financial stress spillover network. Singapore and Japan are the major net transmitter and receiver of financial stress shocks, respectively, during all considered events. During COVID-19, China, as the first country to detect and contain COVID-19, is the strongest net financial stress shock receiver in March 2020, but transmitted net financial stress shocks in February 2020, when the epidemic in China is serious.

Are categorical EPU indices predictable for carbon futures volatility? Evidence from the machine learning method

The purpose of this paper is to explore whether the categorical Economic Policy Uncertainty (EPU) indices are predictable for the volatility of carbon futures, in the mixed data sampling (MIDAS) regression framework. The prediction methods include the MIDAS-RV model, the MIDAS models extended by individual categorical EPU index, combination prediction approaches, the MIDAS models extended by dimensionality reduction techniques as well as the machine learning methods on the basis of MIDAS model and Markov regime switching method. We find firstly that categorical EPU indices are predictable for carbon futures volatility, but the predictive power of individual categorical EPU indices is not robust. Secondly, machine learning methods, especially the machine learning method considering the Markov regime switching structure, help to obtain valid information from multiple categorical EPU indices and produce robust and superior prediction accuracy for carbon futures volatility. The results of the extension analysis also found that machine learning methods, especially the machine learning method considering the Markov regime switching structure help to produce higher investment performance and more accurate long-term carbon futures volatility forecasts. Meanwhile, we also find the advantages of the MIDAS based machine learning methods over the traditional AR based machine learning methods. Finally, the forecasting performance of the machine learning method which considering Markov regime switching structure are superior during both the low and high volatility regimes and even during the COVID-19 pandemic.

An oil futures volatility forecast perspective on the selection of high-frequency jump tests

This paper examines the forecasting performances of high-frequency jump tests for oil futures volatility from a comprehensive perspective. It contributes to the literature by investigating which jump test is the best for oil futures volatility forecasting under different circumstances and whether the jump component extracted from multiple alternative tests is useful for further improving forecasting performance. Our results show that the jumps of the TOD test (Bollerslev et al., 2013) have satisfactory performance over the medium-term and especially the short-term forecasting horizons. Most importantly, the jump components from the intersection of multiple intraday tests further improve the forecasting performance. A variety of further discussions, including models controlling for stock market effects and considering periods of high (low) volatility and the COVID-19 pandemic period, confirm the conclusions. This paper attempts to shed light on oil futures volatility prediction from the perspective of jump test selection.

Forecasting stock market volatility with a large number of predictors: New evidence from the MS-MIDAS-LASSO model.

This paper explores the effectiveness of predictors, including nine economic policy uncertainty indicators, four market sentiment indicators and two financial stress indices, in predicting the realized volatility of the S&P 500 index. We employ the MIDAS-RV framework and construct the MIDAS-LASSO model and its regime switching extension (namely, MS-MIDAS-LASSO). First, among all considered predictors, the economic policy uncertainty indices (especially the equity market volatility index) and the CBOE volatility index are the most noteworthy predictors. Although the CBOE volatility index has the best predictive ability for stock market volatility, its predictive ability has weakened during the COVID-19 epidemic, and the equity market volatility index is best during this period. Second, the MS-MIDAS-LASSO model has the best predictive performance compared to other competing models. The superior forecasting performance of this model is robust, even when distinguishing between high- and low-volatility periods. Finally, the prediction accuracy of the MS-MIDAS-LASSO model even outperforms the traditional LASSO strategy and its regime switching extension. Furthermore, the superior predictive performance of this model has not changed with the outbreak of the COVID-19 epidemic.

Normal and extreme interactions among nonferrous metal futures: A new quantile-frequency connectedness approach

Nonferrous metal markets are wildly discussed for their ultimate importance in industry production. However, the interactions among major international nonferrous metal futures, especially their extreme connectedness at different time frequencies (horizons), are rarely recognized. This paper investigates the normal and extreme interactions at various time frequencies among twelve major international nonferrous metal futures traded in LME and SHFE by proposing a new quantile-frequency connectedness measurement, which combines the quantile connectedness approach of Ando et al. (2018) and frequency connectedness method of Barunik and Krehlik (2018). The main empirical results show that, firstly, these major nonferrous metal futures maintain very tight total connectedness no matter in normal or extreme conditions, and the extreme left- and right-tail connectedness measures are larger than the one at normal case. Secondly, there is no clear difference between the extreme downside (left-tail) and upside (right-tail) total connectedness in both time and frequency domains. Thirdly, the total and net connectedness effects of these nonferrous metal futures are mainly centered in short-term frequency at both normal and extreme quantiles. Fourthly, the dynamic analysis indicates that the total connectedness among these futures are very stable throughout the data sample, even during the recent COVID-19 pandemic. Finally, these nonferrous metal futures play quite different roles in net connectedness effects across various quantiles and time frequencies.

Can dimensional reduction technology make better use of the information of uncertainty indices when predicting volatility of Chinese crude oil futures?

In this paper, we try to forecast the volatility of Chinese crude oil futures (COF) using multiple economic policy uncertainty indicators. MIDAS-RV model is combined with the principal component analysis (PCA), scaled PCA (SPCA) and partial least squares (PLS) techniques in this work, construct the newly MIDAS-RV-PCA, MIDAS-RV-PLS and MIDAS-RV-SPCA models, their prediction performance is compared with the common combination forecasting methods. The in-sample estimation analysis on MIDAS-RV-X models show the that it is necessary to consider multiple economic policy uncertainty indices when predicting the Chinese COF volatility and the in-sample analysis on dimensionality reduction model further demonstrate the rationality of using dimensionality reduction techniques. The out-of-sample evaluation results show that the MIDAS-RV-SPCA is a superior model when forecasting the short-term volatility of Chinese COF using multiple economic policy uncertainty indicators, especially during the periods of high volatility and COVID-19 pandemic. The results also indicates that the DMSPE(0.9) method in the combination forecasting method shows its superior forecasting ability in long-term volatility of Chinese COF, especially during the low volatility and pre-pandemic period.

Forecasting China's crude oil futures volatility: New evidence from the MIDAS-RV model and COVID-19 pandemic.

In this study, we focus on the role of jumps and leverage in predicting the realized volatility (RV) of China's crude oil futures. We employ a standard mixed data sampling (MIDAS) modeling framework. First, the in-sample results indicate that the jump and leverage effects are useful in predicting the RV of Chinese crude oil futures. Second, the out-of-sample results suggest that jump has very significant predictive power at the one-day-ahead horizon while the leverage effect contains more useful information for long-term predictions. Moreover, our results are supported by a number of robustness checks. Finally, we find new evidence that the prediction model that considers the leverage effect has the best predictive power during the COVID-19 pandemic.

Return connectedness among commodity and financial assets during the COVID-19 pandemic: Evidence from China and the US.

In this paper, we explore the dynamics of the return connectedness among major commodity assets (crude oil, gold and corn) and financial assets (stock, bond and currency) in China and the US during recent COVID-19 pandemic by using the time-varying connectedness measurement introduced by Antonakakis et al. (2020). Firstly, we find that the total return connectedness of the US commodity and financial assets is stronger than that of the Chinese commodity and financial assets in most cases, and both of them increase rapidly after the outbreak of COVID-19. Secondly, gold is a net transmitter of return shocks in both the Chinese and the US markets before the burst of COVID-19 pandemic, while stock and currency become net transmitters of shocks in both markets after that. Thirdly, corn usually receives the shocks from other commodity and financial assets in both China and the US markets during the COVID-19 epidemic, and the shocks it receives peak during this period, making it the strongest net receiver of shocks. Fourthly, crude oil shifts from a net transmitter to a net receiver of shocks in China after the outbreak of COVID-19, but it remains to be a net transmitter of shocks in the US. Finally, bond changes from a net receiver to a net transmitter of shocks in China after the outbreak of the epidemic, but converts from a net transmitter to a net receiver of shock in the US. The interchangeable roles of the commodity and financial assets suggest flexible regulatory and portfolio allocation strategies should be applied by policy makers and investors.

Infectious disease pandemic and permanent volatility of international stock markets: A long-term perspective.

Understanding the impact of infectious disease pandemic on stock market volatility is of great concerns for investors and policy makers, especially during recent new coronavirus spreading period. Using an extended GARCH-MIDAS model and a newly developed Infectious Disease Equity Market Volatility Tracker (EMV-ID), we investigate the effects of infectious disease pandemic on volatility of US, China, UK and Japan stock markets through January 2005 to April 2020. The empirical results show that, up to 24-month lag, infectious disease pandemic has significant positive impacts on the permanent volatility of international stock markets, even after controlling the influences of past realized volatility, global economic policy uncertainty and the volatility leverage effect. At different lags of eruptions in infectious disease pandemic, EMV-ID has distinct effects on various stock markets while it has the smallest impact on permanent volatility of China's stock market.