Asymmetric volatility connectedness between Islamic stock and commodity markets

This paper examines the asymmetric volatility connectedness amongst the Dow Jones Islamic Market Index (DJIM) and the Brent crude oil, gold, and silver markets. We use the Diebold and Yilmaz methodology to examine asymmetric volatility connectedness associated with bad (negative semi-variance) and good (positive semi-variance) volatility in these markets. We identify significant volatility connectedness between the DJIM and commodity markets, with the DJIM and Brent oil markets being the largest net contributors of spillovers. Furthermore, the evidence on semi-volatility connectedness displays asymmetric behavior. Bad volatilities are associated with net transmission of spillovers to other markets, except for silver. Our results have significant implications for investors dealing with the DJIM and commodity markets in terms of asset management and diversification.     

Interconnected multilayer networks: Quantifying connectedness among global stock and foreign exchange markets

This paper proposes a novel interconnected multilayer network framework based on variance decomposition and block aggregation technique, which can be further served as a tool of linking and measuring cross-market and within-market contagion. We apply it to quantifying connectedness among global stock and foreign exchange (forex) markets, and demonstrate that measuring volatility spillovers of both stock and forex markets simultaneously could support a more comprehensive view for financial risk contagion. We find that (i) stock markets transmit the larger spillovers to forex markets, (ii) the French stock market is the largest risk transmitter in multilayer networks, while some Asian stock markets and most forex markets are net risk receivers, and (iii) interconnected multilayer networks could signal the financial instability during the global financial crisis and the COVID-19 crisis. Our work provides a new perspective and method for studying the cross-market risk contagion.     

Intraday momentum in Chinese commodity futures markets

In this paper, we study whether the intraday momentum exists in Chinese commodity futures markets. We first construct an open-interest-weighted index with the high-frequency data of all commodity futures traded and then examine the predictability of the last half-hour return with both in-sample and out-of-sample tests. Consistent with findings in other markets, we show that the first half-hour return can readily predict the last half-hour return. We further demonstrate that the magnitude of this intraday momentum varies with volatility, trading volume, trade size, the sign of the first half-hour return, the type of commodity futures, and the launch of night trading. Additionally, this intraday momentum does not result from data snooping but has economic significance and remains robust under different index weighting and predictor calculation methods.     

Quantile connectedness between energy,metal, and carbon markets

This study employs a quantile connectedness approach to examine the dynamic linkages and tail risk connectedness between energy, metal, and carbon markets. Results show that the connectedness between energy, metal, and carbon markets is about 51% at the mean or median and 87% under extreme conditions. This means that the spillover effects of the two tails are much stronger than those under the conditional mean and normal markets, and the spillover effect between markets is heterogeneous under different market conditions. The connectedness between energy, metal, and carbon markets is time-varying, and the volatility is relatively small under extreme positive and negative conditions. Notably, the dynamic connectedness of energy, metal, and carbon markets is different in extreme upward and downward markets, which reflects the asymmetry and tail dependence of spillover effects between markets and indicates that spillover effects are different between the periods of upward and downward markets. In addition, the results of portfolio strategy show that holding short positions in the carbon market is an effective investment choice.     

Does the stock market drive herd behavior in commodity futures markets?

This paper contributes to the debate on commodity financialization by extending tests of herd behavior to commodity futures markets. Utilizing a regime-switching model, we test the presence of herd behavior in a number of commodity sectors including energy, metals, grains and livestock during the low and high market volatility states. We find significant evidence of herd behavior in grains only during the high volatility state. We also find that large price movements in the energy and metal sectors significantly contribute to herd behavior in the market for grains. Finally, we find no significant effect of the stock market on herd behavior in the commodity futures market. Our findings in general do not support the much debated commodity financialization hypothesis.     

Modifying a simple agent-based model to disentangle the microstructure of Chinese and US stock markets

We modify a simple agent-based model (ABM) proposed by Franke and Westerhoff [J. Econ. Dyn. Control, 2012, 36(8), 1193–1211] through considering the price limits and the motion of the fundamental value. The method of simulated moments is applied to calibrate both initial and modified ABMs with CSI 300 and S&P 500 respectively, and the goodness-of-fit of each ABMs is tested. The calibration results indicate that the modified model performs better than initial one. Then, we utilize the GSL-div, proposed by Lamperti [Econometrics Stat, 2018, 5, 83–106.], to verify the explanatory power of ABMs. In this procedure, 13 ARCH family models are introduced as benchmarks. The result shows that the explanatory power of modified ABM exceeds ARCH models in both markets, while initial ABM may be defeated by some of the ARCH family models in explaining the microstructure of CSI 300. Finally, a heuristic algorithm is designed to disentangle the insights of Chinese and US stock markets to the observed time horizon through calibrating the initial fundamental value, and Kupiec test is used to check the robustness of the calibration. The result indicates that the explanation of modified model is robust in both markets, while initial model lost its robustness when explaining S&P 500.     

Time and frequency dynamics of connectedness and hedging performance in global stock markets: Bitcoin versus conventional hedges

Some studies have revealed the hedging ability of Bitcoin against stock markets, but the knowledge of how it compares with other hedges is in its infancy. This paper presents the first study on time-frequency domain connectedness and hedging among five hedges (Bitcoin, crude oil, commodities, gold and the U.S. dollar (USD) index) and four stock indices (developed markets ex U.S., emerging markets ex China, U.S. and China). We find that the connectedness between hedges and stock markets varies by time across time horizons. Specifically, the connectedness between Bitcoin and stock indices is the smallest among all hedges, especially for the short horizon. Gold and USD are isolated from other markets at longer horizons. The hedging ratio, optimal portfolio weights and hedging effectiveness also vary across investment horizons. For short-term investment, gold has better hedging effectiveness, especially for emerging stock markets and the U.S. stock market. For median- and long-term investment, USD has better performance, especially for developed markets ex U.S. and emerging stock markets. Additionally, although Bitcoin has good hedging properties, it has high volatility compared with other hedging assets. In other words, if Bitcoin is included in a portfolio, investors should pay attention to its wide variation. These empirical findings highlight the important role that gold and USD play in hedging against global stock markets.     

Momentum strategies in commodity futures markets

The article tests for the presence of short-term continuation and long-term reversal in commodity futures prices. While contrarian strategies do not work, the article identifies 13 profitable momentum strategies that generate 9.38% average return a year. A closer analysis of the constituents of the long–short portfolios reveals that the momentum strategies buy backwardated contracts and sell contangoed contracts. The correlation between the momentum returns and the returns of traditional asset classes is also found to be low, making the commodity-based relative-strength portfolios excellent candidates for inclusion in well-diversified portfolios.     

Dynamic volatility spillovers and investment strategies between the Chinese stock market and commodity markets

Building on the increased interest in the volatility spillover effects between Chinese stock market and commodity markets, this paper investigates the dynamic volatility spillovers of Chinese stock market and Chinese commodity markets based on the volatility spillover index under the framework of TVP-VAR. The result shows that there is a highly dependent relationship between the stock market and commodity markets. On average, the Chinese stock market is the net recipient of spillover, non-ferrous metals and chemical industry have a very obvious spillover impact on the stock market. The degree of total volatility spillover is different in different periods. After major crisis events, the volatility correlation between markets increases. Since the outbreak of COVID-19, the spillover effect of the stock market on the commodity market has been significantly enhanced. Then optimal portfolio weights and hedge ratios are calculated for portfolio diversification and risk management. The result shows that the ability of most commodities to hedge against risks is significantly reduced when the crisis occurs; NMFI (precious metals) and CRFI (grain) still have good hedging ability after the crisis, but the effectiveness of hedging risk is relatively low. Besides, the combination of CRFI and SHCI (the Shanghai composite index) is the most effective for risk reduction.     

Agricultural commodity futures trading based on cross-country rolling quantile return signals

This paper formulates and examines a new type of bivariate time series trading strategy based on signals generated from cross-country quantiles of return distributions. We conduct rolling quantile trading strategies separately in the U.S. and Chinese futures markets for soybeans, wheat, corn and sugar over very short (daily, intraday and overnight) holding periods. Overall, we find that these practical strategies outperform various benchmarks and there is a large profit potential when trades follow quantile-based signals rather than focusing on the median only. The results highlight the value of cross-country trading strategies and the harnessing of information from different parts of the return distributions which have so far been neglected.     

Information transmission in informationally linked markets: Evidence from US and Chinese commodity futures markets

This paper investigates information transmission and price discovery in informationally linked markets within the multivariate generalized autoregressive conditional heteroskedasticity and information share frameworks. Based on both synchronous and non-synchronous trading information from Chinese futures/spot markets, the New York Mercantile Exchange (NYMEX), Chicago Board of Trade (CBOT), and CME Globex futures markets for copper and soybeans, we show that there is a bidirectional relationship in terms of price and volatility spillovers between US and Chinese markets, with a stronger effect from US to Chinese markets than the other way around. Additionally, the NYMEX and CBOT play a more important role than the CME Globex in the flow of information from US to Chinese markets. Moreover, we find that Chinese copper market adjusts more quickly than the NYMEX copper market to correct the disparity between both markets. However, the converse is true in the case of soybeans. Finally, our results highlight the remarkable role of Chinese futures markets in the price formation process, though NYMEX and CBOT futures markets are the main driving force in price discovery.     

The predictability of aggregate returns on commodity futures

This paper provides evidence that aggregate returns on commodity futures (without the returns on collateral) are predictable, both in-sample and out-of-sample, by various lagged variables from the stock market, bond market, macroeconomics, and the commodity market. Out of the 32 candidate predictors we consider, we find that investor sentiment is the best in-sample predictor of short-horizon returns, whereas the level and slope of the yield curve have much in-sample predictive power for long-horizon returns. We find that it is possible to forecast aggregate returns on commodity futures out-of-sample through several combination forecasts (the out-of-sample return forecasting R² is up to 1.65% at the monthly frequency).     

Financialization and speculators risk premia in commodity futures markets

J.M. Keynes coined the term normal backwardation, a situation where a futures price for a particular expiry month is less than the expected spot price for that month. He argued hedgers pay speculators a risk premium, giving rise to normal backwardation. We study the behavior of commodity futures before and since financialization of the markets, which started about 20 years ago. We find the poor returns to managed futures in recent years are likely due to the impact of financialization and the associated outside money suppressing the futures risk premium.     

Permutation-weighted portfolios and the efficiency of commodity futures markets

Annals of Finance - We study the behavior of permutation-weighted portfolios, portfolios with weights that are proportional to a permutation of the current market weights. For markets with more...     

Global financial crisis and rising connectedness in the international commodity markets

This paper documents a dramatic change in the nature of connectedness in global commodity prices following the 2008 global financial crisis. We show that co-dependence in price-changes among seven major commodity classes goes from a pre-crisis average of 14.82% to a strikingly larger average of 47.87% in the period following the crisis, and which has endured until now. Dynamic swings in price co-movements of such a scale present a clear concern for financial investors and are of immediate interest to a wider policy-maker audience. Of particular interest is the empirical behavior of the food commodity price index, whose contribution to the system dynamics rises from less than 20% in the period up to 2008, to more than 80% after. To dispel any concern that these finding may be method-specific, we demonstrate their invariance to modeling procedure by providing analogous-results using a pairwise Granger causality analysis, as well as different sub-sampling choices.     

Higher-order moment risk connectedness and optimal investment strategies between international oil and commodity futures markets: Insights from the COVID-19 pandemic and Russia-Ukraine conflict

This paper investigates the higher-order moment risk connectedness between West Texas Intermediate (WTI) oil futures, Brent oil futures, Chinese oil futures and commodity futures (agricultural, industrial metals, and precious metals) before and during the COVID-19 pandemic and following the outbreak of the Russia-Ukraine conflict, by combining ex-post moment measures and the novel time-varying parameter (TVP)-vector auto-regression (VAR)-based connectedness approach. Further, this paper depicts the dynamic overall and pairwise correlations between oil and commodity futures and constructs the hedging and optimal-weighted portfolio strategies using the DCC-GARCH t-Copula model. This paper also constructs the multivariate oil-commodity portfolio based on the newly proposed minimum connectedness portfolio approach and takes into account the higher-order moment risk connectedness. The empirical results demonstrate that the dynamic linkages between international oil and commodity futures are positive, time-varying, and have been greatly intensified by the outbreak of the 2018 China-US trade war, the 2020 COVID-19 pandemic, and the 2022 Russia-Ukraine conflict. The risk connectedness results are moment-dependent. The averaged total skewness and kurtosis spillovers are lower than the return and volatility connectedness. Brent (WTI) oil is the largest net transmitter of the return and volatility (skewness and kurtosis) risk spillovers. The dynamic total, net, and net-pairwise spillovers are all time-varying and highly reactive to major crises, especially the COVID-19 pandemic and the Russia-Ukraine conflict. Furthermore, the optimal-weighted portfolio shows a higher risk reduction than the hedging strategy. Finally, the minimum skewness connectedness portfolio shows relatively higher hedging effectiveness, while the minimum kurtosis connectedness portfolio offers the highest cumulative returns.     

Intraday volatility in the stock index and stock index futures markets

We examine the intraday relationship between returns and returnsvolatility in the stock index and stock index futures markets.Our results indicate a strong intermarket dependence in thevolatility of the case and futures returns. Price innovationsthat originate in either the stock or futures markets can predictthe future volatility in the other market. We show that thisrelationship persists even during periods in which the dependencein the returns themselves appears to weaken. The findings arerobust to controlling for potential market frictions such asasynchronous trading in the stock index. Our results have implicationsfor understanding the pattern of information flows between thetwo markets.     

Extreme connectedness of agri-commodities with stock markets and its determinants

In this paper, we quantify the extreme connectedness between agricultural commodity prices with food and beverage stock market returns. We find that the connectedness of returns relies on the degree of the inverse shock, as suggested by the larger impact of the anticipated shock on the upper and lower tails than the estimated shock on the conditional mean. Additionally, the dynamics of the connectedness of returns monitored in the tail differ from the conditional mean. These two outcomes recommend that using conditional averages is limited and imprecise to analyze returns connected with extreme positive/negative events in agricultural commodities and food & beverage indices. Next, we find the determinants of the extent of the connectedness by employing firm level statistics. We find that some of the determinants driving the return spillovers at upper and lower quantiles are quite different from those driving the return spillovers at the middle quantile.     

Quantile dependencies and connectedness between the gold and cryptocurrency markets: Effects of the COVID-19 crisis

This paper examines the quantile dependence, connectedness, and return spillovers between gold and the price returns of leading cryptocurrencies, using quantile cross-spectral, the return spillovers based the quantile VAR, and quantile connectedness approaches. The results show that the dependencies within cryptocurrencies are highly symmetric and sensitive to different quantile arrangements. Under normal market conditions, we find a high positive dependence within cryptocurrencies and a low positive dependence between cryptocurrencies and gold. The dependence is higher at long term than intermediate- and short- terms before the pandemic during bearish market conditions. In contrast, the degree of dependence decreases at the intermediate- and long-terms during COVID-19 period than before. Moreover, the magnitude of return spillovers is higher at lower quantile (bearish market) than upper quantile (bullish market). Gold serves as a safe haven and diversifier asset for cryptocurrencies during COVID-19 outbreak at both intermediate and long terms.     

Investigating the leverage effect in commodity markets with a recursive estimation approach

This paper investigates the presence of the leverage effect in commodities, in comparison with financial markets. The EGARCH model with a Mixture of Normals distribution (EGARCH-MN) is used to capture (i) heavy tails and skewness in the conditional returns, and (ii) leverage effects and time-varying long-term component in the volatility specification. Besides, the estimation strategy relies on an innovative recursive (REC) method, which allows disentangling the leverage effect from the unconditional skewness as an empirical result. When applied to a broadly diversified dataset of assets during 1995–2012, the EGARCH-MN models offers state-of-the-art specifications with leverage and fat-tailed skewed densities, that allow to contrast the specific characteristics of commodities with traditional assets (equities, bonds, FX).