Oil futures volatility prediction: Bagging or combination?

This paper compares the predictive performance of the bagging method and traditional combination models for forecasting oil futures volatility, using economic policy uncertainty (EPU) indices and macroeconomic variables as predictors. Our empirical findings indicate that the bagging method outperforms the conventional combination models, demonstrating the effectiveness of machine learning combination models. These results are confirmed by different evaluation methods, alternative forecasting methods, and alternative oil futures, and hold up during the COVID-19 pandemic and various business cycles. Furthermore, we show that EPU indices are more useful than macroeconomic variables for forecasting oil volatility during the COVID-19 pandemic. Thus, our analysis provides new insights into combination forecasts.

The role of categorical EPU indices in predicting stock-market returns

This study investigates the predictive ability of categorical economic-policy uncertainty (EPU) indices for stock-market returns. The results indicate that some categorical EPU indices have superior predictive ability for stock returns and even achieve higher realized utility than the original EPU index and popular predictors. Furthermore, the diffusion indices based on EPU categories, especially those that use partial least squares (PLS) to extract the principal components, more effectively use the forecast information contained in categorical EPU indices, resulting in improved forecast performance, including reduced forecast errors and increased economic value for investors. In addition, the categorical EPU indices show superior forecasting performance during economic-expansion, the China-US trade-war, and COVID-19 pandemic periods.

Asymmetric dynamic risk transmission between financial stress and monetary policy uncertainty: thinking in the post-covid-19 world

Considering the dramatically increasing impact of the COVID-19 outbreak on monetary policy and the uncertainty in the financial system, we aim to examine the dynamic asymmetric risk transmission between financial stress and monetary policy uncertainty. Our sample covers 30 years of data. We first employ the conventional Granger causality test to examine the average relationship between financial stress and monetary policy uncertainty, and the results cannot provide evidence of causality between them. However, from an asymmetric perspective, we further detect the strongly apparent existence of the asymmetric structure of causality between them. Finally, we conduct further research on the asymmetric impacts from a time-varying perspective. The time-varying test finds that this relationship can be influenced by major events, especially the dot-com bubble, the 2009 financial crisis, and the current COVID-19 pandemic. Thus, one can learn more information about the influencing mechanism between financial stress and monetary policy with our work, which may be beneficial for making better decisions in the future.

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.

Vitamin C promotes ACE2 degradation and protects against SARS-CoV-2 infection.

ACE2 is a major receptor for cellular entry of SARS-CoV-2. Despite advances in targeting ACE2 to inhibit SARS-CoV-2 binding, strategies to flexibly and sufficiently reduce ACE2 levels for the prevention of SARS-CoV-2 infection have not been explored. Here, we reveal vitamin C (VitC) administration as a potent strategy to prevent SARS-CoV-2 infection. VitC reduces ACE2 protein levels in a dose-dependent manner, while even a partial reduction in ACE2 levels can greatly inhibit SARS-CoV-2 infection. Further studies reveal that USP50 is a crucial regulator of ACE2 levels. VitC blocks the USP50-ACE2 interaction, thus promoting K48-linked polyubiquitination of ACE2 at Lys788 and subsequent degradation of ACE2 without affecting its transcriptional expression. Importantly, VitC administration reduces host ACE2 levels and greatly blocks SARS-CoV-2 infection in mice. This study reveals that ACE2 protein levels are down-regulated by an essential nutrient, VitC, thereby enhancing protection against infection of SARS-CoV-2 and its variants.

Forecasting international REITs volatility: the role of oil-price uncertainty

We forecast realized variance (RV) of Real Estate Investment Trusts for 10 leading markets and regions, derived from 5-minutes-interval intraday data, based on the information content of two alternative metrics of daily oil-price uncertainty. Based on the period of the analysis covering January 2008 to July 2020, and using variants of the popular MIDAS-RV model, augmented to include oil market uncertainties, captured by its RV (also derived from 5-minute intraday data) and implied volatility (i.e. the oil VIX), we report evidence of significant statistical and economic gains in the forecasting performance. The result is robust to the size of the forecasting samples, including that of the COVID-19 period, lag-length, nonlinearities, asymmetric effects, and forecast horizon. Our results have important implications for investors and policymakers.

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.

XAF1 Protects Host against Emerging RNA Viruses by Stabilizing IRF1-Dependent Antiviral Immunity.

XIAP-associated factor 1 (XAF1) is an interferon (IFN)-stimulated gene (ISG) that enhances IFN-induced apoptosis. However, it is unexplored whether XAF1 is essential for the host fighting against invaded viruses. Here, we find that XAF1 is significantly upregulated in the host cells infected with emerging RNA viruses, including influenza, Zika virus (ZIKV), and SARS-CoV-2. IFN regulatory factor 1 (IRF1), a key transcription factor in immune cells, determines the induction of XAF1 during antiviral immunity. Ectopic expression of XAF1 protects host cells against various RNA viruses independent of apoptosis. Knockout of XAF1 attenuates host antiviral innate immunity in vitro and in vivo, which leads to more severe lung injuries and higher mortality in the influenza infection mouse model. XAF1 stabilizes IRF1 protein by antagonizing the CHIP-mediated degradation of IRF1, thus inducing more antiviral IRF1 target genes, including DDX58, DDX60, MX1, and OAS2. Our study has described a protective role of XAF1 in the host antiviral innate immunity against RNA viruses. We have also elucidated the molecular mechanism that IRF1 and XAF1 form a positive feedback loop to induce rapid and robust antiviral immunity. IMPORTANCE Rapid and robust induction of antiviral genes is essential for the host to clear the invaded viruses. In addition to the IRF3/7-IFN-I-STAT1 signaling axis, the XAF1-IRF1 positive feedback loop synergistically or independently drives the transcription of antiviral genes. Moreover, XAF1 is a sensitive and reliable gene that positively correlates with the viral infection, suggesting that XAF1 is a potential diagnostic marker for viral infectious diseases. In addition to the antitumor role, our study has shown that XAF1 is essential for antiviral immunity. XAF1 is not only a proapoptotic ISG, but it also stabilizes the master transcription factor IRF1 to induce antiviral genes. IRF1 directly binds to the IRF-Es of its target gene promoters and drives their transcriptions, which suggests a unique role of the XAF1-IRF1 loop in antiviral innate immunity, particularly in the host defect of IFN-I signaling such as invertebrates.

How does the COVID-19 outbreak affect the causality between gold and the stock market? New evidence from the extreme Granger causality test.

The causal relationship between gold and stocks has been widely studied, while their causality and the long- and short-run characteristic of this relationship have not been examined under different shocks. The purpose of this paper is to fill this gap. Meanwhile, considering the impact of the COVID-19 outbreak on gold and stock markets, we also aim to investigate whether the relationship changes after this epidemic. With invoking the time- and frequency-domain extreme Granger causality tests, we find that a significant causality between gold and stock usually comes from extreme shocks, displaying as the long-term causality running from gold shocks to stock shocks while the fickle impact of stock shocks on gold shocks. Besides, empirical results suggest that the causality between gold and stock shocks is greatly promoted after this epidemic. The present study is useful for investors and policymakers, as it has reference significance when dealing with subsequent extreme shocks or events.

Macroeconomic attention and stock market return predictability

Our investigation evaluates the novel macroeconomic attention indices (MAI) of Fisher et al. (2021) in terms of their ability to predict stock market returns based on dimension reduction methods and shrinkage methods. Our results demonstrate that macroeconomic attention indices can predict stock market returns with a significant degree of accuracy. In addition, the components of MAI indices based on partial least squares (PLS) and the least absolute shrinkage and selection operator (LASSO) methods have a greater capacity to improve the accuracy of the prediction of stock market returns than the components of the traditional macroeconomic variables. Moreover, we find that shrinkage methods can generate performances superior to those of the other models for forecasting stock market returns. We further demonstrate that macroeconomic attention indices embody superior predictive ability during the COVID-19 pandemic and over longer periods of time. Our study sheds new light on stock market returns’ prediction from the perspective of macroeconomic fundamentals.

The role of uncertainty measures in volatility forecasting of the crude oil futures market before and during the COVID-19 pandemic

The purpose of this article is to investigate whether various uncertainty measures provide incremental information for the prediction the volatility of crude oil futures under high-frequency heterogeneous autoregressive (HAR) model specifications. Moreover, by considering the information overlap among various uncertainty measures and fully using of the information in various uncertainty measures, this paper uses two prevailing shrinkage methods, the least absolute shrinkage and selection operator (lasso) and elastic nets, to select uncertainty variables during the entire sampling period, before the COVID-19 pandemic and during the COVID-19 pandemic and then uses the HAR model to predict crude oil volatility. The results show that (i) uncertainty measures can be utilized to predict crude oil volatility under the high-frequency framework in both in-sample and out-of-sample analyses. (ii) Because of the information overlap between various uncertainty measures, adding a large number of uncertain variables to the HAR model may not significantly improve the volatility prediction. (iii) Before and during the COVID-19 pandemic, Chicago Board Options Exchange (CBOE) crude oil volatility (OVX) has the greatest impact on crude oil volatility, infectious disease equity market volatility (EMV) exerts a significant influence on crude oil futures volatility forecasts during the COVID-19 period, and CBOE implied volatility (VIX) and the financial stress index (FSI) have substantial impacts on crude oil futures volatility forecasts before COVID-19.

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.

Singlehanded or joint race? Stock market volatility prediction

This paper examines whether the realized skewness and kurtosis contain predictability for Shanghai Stock Exchange Sector Index. We find kurtosis contains more information to predict the Shanghai Stock Exchange Sector Index volatility. Importantly, the model considering the combination of both skewness and kurtosis has the best predictability for the stock market volatility. Moreover, we investigate the economic values of the models and asymmetric effects of skewness and kurtosis on stock market volatility, finding skewness (skewness <0) and kurtosis (kurtosis >3) own better forecasting performance. Finally, we discuss the predictability of skewness and kurtosis during two turbulent periods of China's stock bubble and the COVID-19 pandemic.

Policy uncertainty and carbon neutrality: Evidence from China

This paper examines the effects of China's economic policy uncertainty (CEPU) index and climate policy uncertainty (CPU) index on the Wind carbon neutral concept (CNCI) index volatility. We find that both CEPU and CPU indices have significant effects on the CNCI volatility. In addition, when the market suffers more volatile risks, the CPU index has relatively better performance than the CEPU index for forecasting CNCI index volatility. This paper can provide new insights to realize China's goal of achieving peak emissions by 2030 and carbon neutrality by 2060.

Oil price volatility predictability: New evidence from a scaled PCA approach

We introduce the scaled principal component analysis (sPCA) method to forecast oil volatility, and compare it with two commonly used dimensionality reduction methods: principal component analysis (PCA) and partial least squares (PLS). By combining the simple autoregressive model with the three dimensionality reduction methods, we obtain several interesting and notable findings. First, the model with the sPCA diffusion index performs substantially better than the competing models based on the out-of-sample Roos2 test. Moreover, the model with the sPCA diffusion index consistently demonstrates superior forecasting power compared with the other models under different macroeconomic conditions (e.g., business cycle recessions and expansions, high- and low-volatility levels, and the COVID-19 pandemic). Furthermore, the findings of our study are strongly robust to various robustness tests, such as alternative forecasting window sizes and different lags of model selection.

Oil futures volatility predictability: Evidence based on Twitter-based uncertainty

This paper explores the influence of the Twitter-based uncertainty index on oil futures market volatility. The Twitter-based Market Uncertainty (TMU) index, based on the novel Markov-regime GARCH-MIDAS model, may significantly improve prediction accuracy for oil futures volatility. Moreover, the TMU was still useful in predicting oil volatility during the COVID-19 pandemic. Furthermore, when the alternative Twitter-based uncertainty index, Twitter-based Economic Uncertainty (TEU), is adopted, these results are also robust. This paper highlights the importance of the Twitter-based uncertainty index for oil futures market.

Interleukin-8 as a Biomarker for Disease Prognosis of Coronavirus Disease-2019 Patients.

The widespread prevalence of coronavirus disease-2019 (COVID-19) which is caused by severe respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, has resulted in a severe global public health emergency. However, there are no sensitive biomarkers to predict the disease prognosis of COVID-19 patients. Here, we have identified interleukin-8 (IL-8) as a biomarker candidate to predict different disease severity and prognosis of COVID-19 patients. While serum IL-6 become obviously elevated in severe COVID-19 patients, serum IL-8 was easily detectible in COVID-19 patients with mild syndromes. Furthermore, lL-8 levels correlated better than IL-6 levels with the overall clinical disease scores at different stages of the same COVID-19 patients. Thus, our studies suggest that IL-6 and IL-8 can be respectively used as biomarkers for severe COVID-19 patients and for COVID-19 disease prognosis.

Adjuvant therapeutic effects of moxibustion on COVID-19: A protocol for systematic review and meta-analysis.

BACKGROUND: COVID-9 has become a global pandemic with severe health issues around the world. However, there is still no effective drug to treat the disease, and many studies have shown that moxibustion plays a positive role in adjuvant treatment of COVID-19. Therefore, this meta-analysis is designed to evaluate the efficacy of moxibustion for COVID-19. METHODS: The relevant randomized controlled trials will be systematically retrieved from the electronic database, including PubMed, Embase, Cochrane Clinical Trials Database, Web of Science, and China National Knowledge Infrastructure, without restrictions on publication status and language. Two reviewers will independently review all included studies and assess the risk of bias. Two reviewers will independently extract data from the included studies based on a pre-designed standardized form. Any disagreements will be resolved by consensus. The meta-analysis will be performed with RevMan (V5.3.5) software. RESULT: The results of this study will be published in a peer-reviewed journal. CONCLUSION: This ongoing meta-analysis will provide up-to-date evidence of the efficacy of moxibustion for patients with COVID-19. REGISTRATION: The meta-analysis has been prospectively registered in PROSPERO (CRD42020211910).

Which popular predictor is more useful to forecast international stock markets during the coronavirus pandemic: VIX vs EPU?

This study mainly investigates which predictors (VIX or EPU index) are useful to forecast future volatility for 19 equity indices based on HAR framework during coronavirus pandemic. Out-of-sample analysis shows that the HAR-RV-VIX model exhibits superior forecasting performance for 12 stock markets, while EPU index just can improve forecast accuracy for 5 equity indices, implying that VIX index is more useful for most stock markets' future volatility during coronavirus crisis. The results are robust in recursive window method, alternative realized measures and sub-sample analysis;moreover, VIX index still contains the strongest predictive ability by considering kitchen sink model and mean combination forecast. Furthermore, we further discuss the predictive effect of VIX and EPU index before the coronavirus crisis. Our article provides policy makers, researchers and investors with new insights into exploiting the predictive ability of VIX and EPU index for international stock markets during coronavirus pandemic.

The role of the IDEMV in predicting European stock market volatility during the COVID-19 pandemic.

The main purpose of this paper is to investigate whether the Infectious Disease EMV tracker (IDEMV) proposed by Baker et al. (2020) has additional predictive ability for European stock market volatility during the COVID-19 pandemic. The three European stock markets we consider are France, UK and Germany. Our investigation is based on the HAR and its augmented models. We find that the IDEMV has stronger predictive power for the France and UK stock markets volatilities during the global pandemic, and the VIX has also superior predictive ability for the three European stock markets during this period.