Conditional correlations and volatility spillovers between crude oil and stock index returns

This paper investigates the conditional correlations and volatility spillovers between the crude oil and financial markets, based on crude oil returns and stock index returns. Daily returns from 2 January 1998 to 4 November 2009 of the crude oil spot, forward and futures prices from the WTI and Brent markets, and the FTSE100, NYSE, Dow Jones and S&P500 stock index returns, are analysed using the CCC model of Bollerslev (1990), VARMA-GARCH model of Ling and McAleer (2003), VARMA-AGARCH model of McAleer, Hoti, and Chan (2008), and DCC model of Engle (2002). Based on the CCC model, the estimates of conditional correlations for returns across markets are very low, and some are not statistically significant, which means the conditional shocks are correlated only in the same market and not across markets. However, the DCC estimates of the conditional correlations are always significant. This result makes it clear that the assumption of constant conditional correlations is not supported empirically. Surprisingly, the empirical results from the VARMA-GARCH and VARMA-AGARCH models provide little evidence of volatility spillovers between the crude oil and financial markets. The evidence of asymmetric effects of negative and positive shocks of equal magnitude on the conditional variances suggests that VARMA-AGARCH is superior to VARMA-GARCH and CCC.     

New insights into the role of global factors in BRICS stock markets: A quantile cointegration approach

Because of the acceleration in marketization and globalization, stock markets in the BRICS (Brazil, Russia, India, China, and South Africa) countries are affected by various global factors, for example, oil prices, gold prices, global stock market volatility, global economic policy uncertainty, financial stress, and investor sentiment. This paper offers new insights into the short- and long-run linkages between global factors and BRICS stock markets by applying the quantile autoregressive distributed lags (QARDL) approach. This novel methodology enables us to test short- and long-run linkages accounting for distributional asymmetry. That is, the nonlinear dynamic relationship between the global factors and BRICS stock prices depends on market conditions. Our empirical results show that the effects of gold prices and global stock market volatility on BRICS stock prices are more significant in the long run than in the short run. A decrease in global stock market volatility is associated with higher stock prices, while gold prices demonstrate upward co-movement in dynamic correlations with stock markets. Irrational factors, such as economic policy uncertainty, financial stress, and investor sentiment, play a critical role in the short term, and negative interdependence is dominant. Finally, the rolling-window estimation technique is used to examine time-varying patterns between major global factors and BRICS stock markets.     

Global and regional integration of the Middle East and North African (MENA) stock markets

We investigate financial integration of MENA region to facilitate a more in-depth exploration of the structure of interdependence and transmission mechanism of stock returns and volatility between MENA and world stock markets. The EGARCH-M models with a generalized error distribution are employed to consider both leverage effect of negative shocks and leptokurtosis prevalent in the MENA stock markets. The estimation results of multivariate AR-GARCH models indicate that there are large and predominantly positive volatility spillovers and volatility persistence in conditional volatility between MENA and world stock markets. Own-volatility spillovers are generally higher than cross-volatility spillovers for all the markets.     

Economic policy uncertainty,oil price volatility and stock market returns: Evidence from a nonlinear model

This paper investigates the nonlinear relationship between economic policy uncertainty, oil price volatility and stock market returns for 25 countries by applying the panel smooth transition regression model. We find that oil price volatility has a negative effect on stock returns, and this effect increases with economic policy uncertainty. Furthermore, there is pronounced heterogeneity in responses. First, oil-exporting countries whose economies depend more on oil prices respond more strongly to oil price volatility than oil-importing countries. Second, stock returns of developing countries are more susceptible to oil price volatility than that of developed countries. Third, crisis plays a crucial role in the relation between oil price volatility and stock returns.     

Overnight stock returns and realized volatility

The information flow in modern financial markets is continuous, but major stock exchanges are open for trading for only a limited number of hours. No consensus has yet emerged on how to deal with overnight returns when calculating and forecasting realized volatility in markets where trading does not take place 24 hours a day. Based on a recently introduced formal testing procedure, we find that for the S&P 500 index, a realized volatility estimator that optimally incorporates overnight information is more accurate in-sample. In contrast, estimators that do not incorporate overnight information are more accurate for individual stocks. We also show that accounting for overnight returns may affect the conclusions drawn in an out-of-sample horserace of forecasting models. Finally, there is considerably less variation in the selection of the best out-of-sample forecasting model when only the most accurate in-sample RV estimators are considered.     

Sovereign rating changes—Do they provide new information for stock markets?

The purpose of this paper is to examine the impact of sovereign rating changes on international financial markets using a comprehensive database of 42 countries, covering the major regions in the world over the period 1995–2003. In general, we find that rating agencies provide stock and foreign exchange markets with new tradable information. Specifically, rating upgrades (downgrades) significantly increased (decreased) USD denominated stock market returns and decreased (increased) volatility. Whereas the mean response is contributed evenly by the local currency stock returns and exchange rate changes that make up the USD returns, only the foreign exchange volatility was behind the USD denominated return volatility. In addition, we find significant asymmetric effects of rating announcements. The market responses – both return and volatility – are more pronounced in the cases of downgrades, foreign currency debt, emerging market debt, and during crisis periods. This study has important policy implications for international investors’ asset allocation plans and for regulatory bodies such as the Basel Committee who increasingly rely upon Moody's, Standard and Poor's and Fitch's ratings for their regulatory regimes.     

中国股票市场国际联动性研究

本文选取2000~2015年全球40支股票指数日收盘价,通过建立收益率网络和DCC MVGARCH模型波动率网络对中国股票市场国际联动性进行实证分析。研究表明,随着经济全球化的加深,全球股市收益率和波动率联动逐渐增强;全球金融危机和欧债危机期间,收益率联动网络具有小世界性;中国与全球股市长期处于割裂状态,但在全球金融危机期间与其他市场联系加强。在全球经济形势复杂多变的情况下,中国应针对性采取措施促进股市发展,以分享全球金融一体化利益。     

Long memory and structural breaks in modeling the return and volatility dynamics of precious metals

We investigate the potential of structural changes and long memory (LM) properties in returns and volatility of the four major precious metal commodities traded on the COMEX markets (gold, silver, platinum and palladium). Broadly speaking, a random variable is said to exhibit long memory behavior if its autocorrelation function is not integrable, while structural changes can induce sudden and significant shifts in the time-series behavior of that variable. The results from implementing several parametric and semiparametric methods indicate strong evidence of long range dependence in the daily conditional return and volatility processes for the precious metals. Moreover, for most of the precious metals considered, this dual long memory is found to be adequately captured by an ARFIMA–FIGARCH model, which also provides better out-of-sample forecast accuracy than several popular volatility models. Finally, evidence shows that conditional volatility of precious metals is better explained by long memory than by structural breaks.     

Macroeconomic news and the returns of financial companies

This research is concerned with the response of the NASDAQ Financial 100 index to macroeconomic news. The paper employs the newly developed technique of generalized impulse response analysis to examine how macroeconomic shocks affect the performance of the financial sector. The results identify the magnitude and persistence of the response of financial companies stock returns arising from shocks to the stance of monetary policy, real output, inflation, and risk. The findings add to the literature on the determinants of financial sector stocks and on the relationship between the stock market and the macroeconomy. Copyright © 2002 John Wiley & Sons, Ltd.     

The impact of VIX on China’s financial market: A new perspective based on high-dimensional and time-varying methods

Based on the new perspective of high-dimensional and time-varying methods, this paper analyzes the contagion effects of US financial market volatility on China’s nine financial sub-markets. The results show evidence of non-linear Granger causality from the US financial volatility (VIX) to the China’s financial markets. Increased US financial volatility has a negative next-day impact on the stock, bond, fund, interest rate, foreign exchange, industrial product and agricultural product markets, and a positive next-day impact on the gold and real estate markets. US financial volatility has the greatest impact on industrial product market, following by stock, agricultural product, fund, real estate, bond, gold, foreign exchange, and interest rates. Major risk events such as the global financial crisis can cause an enhanced contagion effect of US financial volatility to China's financial markets. This paper supports the achievements of China's actions to prevent and resolve major financial risks in the period of the COVID-19 epidemic.     

Does oil price variability affect the long memory and weak form efficiency of stock markets in top oil producers and oil Consumers? Evidence from an asymmetric MF-DFA approach

This study examines the asymmetric multifractality and the market efficiency of the stock markets in the countries that are the top crude oil producers (USA, KSA, Canada and Russia) and consumers (Brazil, China, India, and Japan) using an asymmetric multifractal detrended fluctuation analysis (A-MF-DFA) method. The results show evidence of an asymmetric multifractal nature for all markets. Moreover, the multifractality is stronger in the upward movement of the market returns, except in China. The degree of efficiency of the stock markets is shown to be time-varying and experienced a decrease during the 2008 global financial crisis (GFC), but an upside trend occurred during the recent oil price crash followed a significant decline during COVID-19. The stock markets have an anti-persistent feature during GFC and COVID-19, whereas they exhibit a long-term persistent feature during oil price crash. More interestingly, the efficiency of the stock markets of crude oil producers is lower in general than that of oil consumers. Furthermore, the efficiency of the stock market is lower in the downward movement of the market returns than in the upward movement. Asymmetry and oil price uncertainty index are the key driver of the stock markets and can serve as predictor of the stock market dynamics of top oil producers and top oil consumers particularly during COVID-19 and oil price crash.     

Identifying states of global financial market based on information flow network motifs

Stock markets exhibit different states due to internal and external shocks. It is of great significance to identify the status of global financial markets. Based on 48 global stock indices from 1996 to 2018, we constructed a global stock index transfer entropy network, which represents the information flow between stock markets in different economies. By analyzing the triadic motifs in the transfer entropy network, we divided the global stock market into different market states based on the distribution of motifs. The characteristic structure of information flow was observed in different market states. We found that the information flow in the global stock market increases significantly during major financial events, indicating that the global stock index has a mutual influence and a close relationship with each other. In addition, stock indices in the Asia-Pacific, Middle East, and Africa are the main sinks of information, while stock indices in the Americas and Europe are the primary sources of information.     

Price effects of steel commodities on worldwide stock market returns

We analyze the price effects of steel commodities on stock market returns in emerging and developed economies. These commodities have recently attained increased media exposure due to the rise in the U.S. steel import tariffs, which pose the threat of reducing global demand for steel products and, consequently, lowering prices abroad. However, little has been investigated on the impact of steel commodity prices on worldwide stock market returns. By performing structural VAR and GARCH techniques on a weekly-frequency time series from 2002 to 2015, we find positive and statistically significant effects of linear and non-linear steel commodity price shocks on real stock returns in the commodity markets. In the highly diversified financial markets such as U.S. and Germany, real stock returns do not significantly respond to steel commodity price shocks, although we find highly significant positive responses from developed economies such as Australia, Japan and South Korea. Results are robust to different model specifications. Our evidence suggests that higher tariffs on steel imports represent a larger disadvantage to commodity markets which are more largely impacted by steel commodity prices. We provide economic policy implications based on recent literature.     

Spillover effects on the sectoral returns for australian and New Zealand equity markets

In this paper, we examine the return and volatility spillovers, together with the trend spillovers on the sectoral equity returns for Australian and New Zealand markets. We find that the return spillovers of industrial, local and global shocks have a limited effect on Australian and New Zealand sector returns, whereas the volatility spillovers play a significant role on explaining the volatility of sector equity indices. Furthermore, we discover that the volatility spillover effects of the global and industrial shocks are greater in magnitude for explaining the volatility of the Australian sectors than those of New Zealand, particularly basic materials, oil and gas, technology and telecom sectors. By employing the trend spillover model, we find that the volatility spillover effects of global sector indices have been increasing over the volatility of the Australian sectoral returns until now. This finding proposes that Australian sector equity market is more integrated with the world than the New Zealand counterpart.     

What drives herding in oil-rich,developing stock markets? Relative roles of own volatility and global factors

The main goal of this paper is to formally establish the volatility-herding link in the developing stock markets of the oil-rich GCC countries by examining how market volatility affects herd behavior after controlling for global factors. Using a regime-switching, smooth transition regression model (STR), we find significant evidence of herding in all Gulf Arab stock markets, with the market volatility being the more paramount factor governing the switches between the extreme states of non-herding and herding. The global variables comprised of the U.S. stock market performance, the price of oil and the US interest rate as well as the risk indexes including the CBOE Volatility Index (VIX) and the St. Louis Fed's Financial Stress Index (FSI) are found to be significant factors governing the transition to herding states. The findings stress the effect of contagion in financial markets, despite the restrictions established by the GCC policymakers in order to protect their markets.     

Time-frequency effect of crude oil and exchange rates on stock markets in BRICS countries: Evidence from wavelet quantile regression analysis

This study examines the effects of oil prices and exchange rates on stock market returns in BRICS countries (Brazil, Russia, China, India and South Africa) from a time–frequency perspective over the period 2009–2020. We use wavelet decomposition series to develop a threshold rolling window quantile regression to detect time–frequency effects at various scales. The empirical results are as follows. First, our findings confirm that the effects of both crude oil prices and exchange rates on BRICS stock returns are asymmetric. Positive shocks of crude oil have a greater impact on a bull market, whereas negative shocks have a greater impact on a bear market. Second, there is a short-term enhancement effect of crude oil and exchange rate on BRICS stock markets. In addition, volatility in the macro financial environment also exacerbates the impacts of oil prices and exchange rates on the stock market, and these fluctuations are heterogeneous. Overall, these findings provide useful insights for international investors and policy makers.     

Wavelet coherence analysis of returns,volatility and interdependence of the US and the EU money markets: Pre & post crisis

This research analyse the US and the EU money markets interdependence from 2004 to 2018. The study explains to what extent the volatility of the chosen money markets instruments in two regions is inter-correlated before, during and after the financial crisis of 2008. We apply the econometric analysis and estimate time-series models of class GARCH to study the historical dynamics of interbank rates and bond returns. The study demonstrates that correlation between returns of analogous money market instruments in the EU and US is not stable over time. We find that correlation rises in periods when countries are exposed to the same external shocks as global financial crisis. Wavelet coherence analysis suggests that investors do not get any advantages of portfolio diversification investing only in US treasuries with different maturities for more than 256 days and do not get any advantages at all investing only in European bonds.     

On the predictability of the distribution of excess returns in currency markets

This paper investigates the predictability of foreign exchange (FX) volatility and liquidity risk factors on returns to the carry trade, an investment strategy that borrows in currencies with low interest rates and invests in currencies with high interest rates. Previous studies have suggested that this predictability could have been spuriously accounted for due to the persistence of the predictors. The analysis uses a predictive quantile regression model developed by Lee (2016) that allows for persistent predictors. We find that predictability changes remarkably across the entire distribution of currency excess returns. Predictability weakens substantially in the left tail once persistence is accounted for, implying a moderate negative predictive relation between FX volatility risk and carry trade returns. By contrast, it becomes stronger in the right tail. Furthermore, we provide evidence that FX volatility risk still dominates liquidity risk after controlling for persistence. These findings suggest that the persistence of the predictors needs to be taken into account when one measures predictability in currency markets. Finally, out-of-sample forecast performance is also presented.     

Risk spillover from crude oil prices to GCC stock market returns: New evidence during the COVID-19 outbreak

In this study, we examine oil price extreme tail risk spillover to individual Gulf Cooperation Council (GCC) stock markets and quantify this spillover’s shift before and during the COVID-19 pandemic. A dynamic conditional correlation generalized autoregressive heteroscedastic (DCC- GARCH) model is employed to estimate three important measures of tail dependence risk: conditional value at risk (CoVaR), delta CoVaR (ΔCoVaR), and marginal expected shortfall (MES). Using daily data from January 2017 until May 2020, results point to significant systemic oil risk spillover in all GCC stock markets. In particular, the effect of oil price systemic risk on GCC stock market returns was significantly larger during COVID-19 than before the pandemic. Upon splitting COVID-19 into two phases based on severity, we identify Saudi Arabia as the only GCC market to have experienced significantly higher exposure to oil risk in Phase 1. Although all GCC stock markets received greater oil systemic risk spillover in Phase 2 of COVID-19, Saudi Arabia and the United Arab Emirates appeared more vulnerable to oil extreme risk than other countries. Our empirical findings reveal that investors should carefully consider the extreme oil risk effects on GCC stock markets when designing optimal portfolio strategies, minimizing portfolio risk, and adopting dynamic diversification process. Policymakers and regulators should also enact awareness, oversight, and action plans to minimize adverse oil risk effects.