Normal Market Conditions Research Articles

Normal and extreme impact and connectedness between fossil energy futures markets and uncertainties: Does El Niño-Southern Oscillation matter?

Extant research may still lack a nuanced comprehension of the impact and connectedness among fossil energy markets and multiple uncertainties, particularly climate uncertainty represented by the El Niño Southern Oscillation (ENSO), through normal to extreme conditions, despite the recognition that uncertainties play a pivotal role in shaping the price dynamics of fossil energy markets. In view of this, employing the quantile-on-quantile regression and quantile connectedness approaches, our study, to the best of our knowledge, pioneers a comprehensive evaluation of the impact and connectedness between three important fossil energy markets and four pivotal uncertainties under different market conditions. Our empirical findings underscore several key points. Firstly, the conventional mean-based methods risk underestimating the linkages between fossil energy markets and uncertainties, as the impact and connectedness between these variables are distinctly more pronounced under extreme market conditions. Secondly, we observe the ENSO predominantly operates as a net performer, while the crude oil market acts as a net participant in the takeover of information spillovers. Thirdly, our analysis reveals that strong La Niña events propagate more potent information spillovers when compared to strong El Niño events. Fourthly, the dynamic analysis lends further credence to a tighter time-varying connectedness between fossil energy markets and uncertainties under extreme downside and upside than under normal market conditions. Finally, the dynamic connectedness results propose that the ENSO consistently serves as a stable net contributor of information to all other variables under extreme market conditions. In contrast, geopolitical uncertainty and infectious disease uncertainty operate as both recipients and contributors over time and across quantiles with the dominance of a net contributor pattern in the extreme upside and downside, respectively. These findings provide fresh and valuable implications for enhancing resilience against shocks from uncertainties impacting fossil energy commodities.

No place like home: Home bias and flight-to-quality in Group of Seven countries

Home bias in investments (a major puzzle in international economics) is important because it can lead to inefficient portfolios, reduction of international trade, and exacerbation of financial contagion. In contrast, flight-to-quality (FTQ) acts to intensify international investments. We examine relationships between these two contrary phenomena in normal market conditions and exceptional periods, such as the 2008–2009 financial crisis, the 2013 taper tantrum episode, and the 2020 Covid-19 crisis in Group of Seven (G7) countries from 2004 to 2021. We apply a multivariate panel regression framework, using dynamic conditional correlations as measures of FTQ and multiple, robust specifications for the bias measure. Our findings indicate a positive relationship between home allocation and FTQ, this flight being oriented toward safe domestic assets. This result is surprising as it shows that home bias can have a beneficial side, as it can, by orienting safety-seeking investments domestically, counteract somewhat the aggravation of FTQ.

Extreme risk dependence and time-varying spillover between crude oil, commodity market and inflation in China

The fluctuation of general commodity prices, particularly crude oil prices, exerts a substantial influence on the economic development of all countries. Based on the mean and quantile spillover index of time-varying parameter vector autoregressive (TVP-VAR), we measure the spillover effects among crude oil, commodity market, and inflation in China under normal and extreme conditions from April 2018 to January 2023. The results show the interdependence among crude oil, commodity markets, and inflation; however, the degree of correlation varies at different quantiles. Notably, the spillover index is significantly higher at the extreme quantile, indicating that extreme risks can easily affect the spillover effect among crude oil, commodity markets, and inflation. Meanwhile, the tail risk spillover effect among crude oil, commodity markets, and inflation exhibits asymmetry, indicating that the spillover effect is greater during downward market conditions than upward ones. Furthermore, oil prices serve as a significant provider of systemic risk, both in normal market conditions and during times of extreme risk. In most cases, oil prices transfer more risks to inflation and commodity markets, commodity markets transfer more risks to inflation, and the paired spillover effects between commodity markets fluctuate over time. Finally, our results hold significant implications for investors, market regulators, and policymakers.

Asymmetric effects of market uncertainties on agricultural commodities

The interplay between energy and agricultural commodities and the responses of agricultural commodities to global uncertainty factors have been receiving increasing attention due to the pivotal contributions of commodity markets to the world economy. This study investigates the asymmetric effects of oil, market volatility and sentiment, economic policy uncertainty, and geopolitical risk on agricultural commodities in a nonparametric quantile-on-quantile regression framework. The sample used for empirical analysis spans January 03, 2013, to October 21, 2022, and includes ten agricultural commodities (canola, cocoa, coffee, corn, feeder cattle, lean hogs, live cattle, orange juice, sugar, and wheat), crude oil, crude oil implied volatility (OVX), market sentiment (VIX), economic policy uncertainty of the US (USEPU), and geopolitical risk (GPR). The empirical findings suggest that crude oil fails to serve as either a hedge or safe-haven for agricultural commodities. Meanwhile, OVX and VIX serve as potential hedges against agricultural commodity shocks. At elevated levels of USEPU, agricultural commodities reveal only weak hedging capacities against the USEPU-driven downside risks and mostly act just as diversifiers of economic policy uncertainty effects. However, they could hedge against the crystallisations of the downside geopolitical risks. These findings are important for effective regulation policies, risk management, and portfolio decisions across bullish, bearish, and normal market conditions.

Dynamic connectedness across energy and metal futures markets during the COVID-19 pandemic: New evidence from a time-varying spillover index

The COVID-19 outbreak has greatly increased economic uncertainty worldwide and significantly impacted the markets for energy and metal futures. In order to capture the dynamic connectedness between the energy and metal futures markets for the period of January 3, 2013, to June 30, 2022, and to validate the mechanism by which COVID-19 affects spillover effects, this study combines a time-varying parametric vector autoregressive model with spillover effects. It is clear that there is a clear spillover effect in the energy and metal futures markets, showing time-varying characteristics, with fluctuations in the range of 35%–65%. The findings demonstrate that there are considerable spillover effects in the energy and metal futures markets, which fluctuate between 35% and 65% over time and are subject to unexpected events. Especially, the spillover network and dynamic spillover results indicate that during the COVID-19 outbreak, the spillover effect between the energy futures market and the metal futures market significantly increased. The mechanism analysis results indicate that COVID-19 influences the spillover effects between the energy and metal futures markets mostly through uncertainty and financial channel. Further research found that compared to normal market conditions, bullish and bearish market conditions had a greater impact on spillovers in the energy and metal futures markets.

Forecasting the South African Real Spot-rate Curve During Covid-19 Stress

Purpose: The aim of this study is to investigate the performance of forecasting the South African real spot rate curve using different mathematical term-structure models during the Covid-19 period. This study follows previous studies by Reid (2009) and Mashoene et al., 2021) where dynamic term-structure models performed well compared to static term-structure models over normal market conditions. For high volatility, normality often breaks, which might result in a need for recalibration in the currently used methodology or a total change in the methodology.
 Methodology: This study explores the dynamism of both dynamic term-structure models, which follow the Nelson-Siegel framework, and static term-structure models with the option of recalibration of model parameters to account for a change in macro-economic dynamics brought by the effect of the Covid-19 pandemic. The forecasting exercise was applied to term-structure models following the Nelson-Siegel framework using AR(1) process.
 Findings: The incorporation of macroeconomic factors performed poorly in estimating future model parameters. This could be attributable to the effect of global stress, which resulted in poor linear relationships between macroeconomic and financial factors. Recalibration process on static Nelson-Siegel term-structure models improved the models' performance, especially during the high volatility/period of total economic shutdown due to the Covid-19 pandemic.
 Implications: The application of static Nelson-Siegel term-structure models with recalibrated parameters might improve bond pricing processes and risk management strategies in emerging markets, especially during market stress.

Dynamic volatility transfer in the European oil and gas industry

The study examines dynamic volatility transmissions among European energy industry participants along the production lines of Upstream, Midstream, Downstream, and Integrated Oil Gas (IOG) segments. Using Diebold-Yilmaz (2012, 2014) spillover index, during the sample period of October 2006 to June 2022, we find significant internal volatility spillover among the European energy sector participants, primarily emanating from Upstream companies. In subsamples, we show that Downstream and Midstream segments can also become volatility transmitters under certain conditions. More importantly, the large Russian IOG companies became significant volatility transmitters after 2022 with the onset of Russia's war on Ukraine, potentially causing major system instability because these IOG firms were traditionally volatility absorbers in the network. Overall, we provide insights about the interconnectedness among European energy companies during normal and extreme market conditions and highlight important system dynamics that could be useful for policy makers and investors.

Economic policy uncertainty, geopolitical risk, market sentiment, and regional stocks: asymmetric analyses of the EU sectors

The purpose of this study is to investigate the asymmetric effects of economic policy uncertainty (EPU), geopolitical risk (GPR), and market sentiment (VIX) on European Union (EU) stocks by sectors of economic activity. The design and methodological approach of our research are rooted in parametric and nonparametric quantile-based techniques. We employ monthly data covering eleven sectors of economic activity in addition to GPR, Global EPU, European Union EPU, United States EPU, and VIX. Our dataset covers the period between February 2013 and September 2022. Our findings show a generally low predictive power of the considered EPU measures on the stock returns of the EU sectors. Notwithstanding, the analysis reveals that EPU from the EU has the highest predictive ability on the EU sectoral stock returns while EPU from the US has no significant predictive ability on the stock returns from the EU. Our findings also highlight the asymmetric effects of various EPUs on EU stocks. Moreover, certain sectoral exposure to EU stocks, found to serve just as diversifiers in normal market conditions, could become a hedge and safe-haven against GPR in extreme economic conditions. Our findings also highlight the role of the VIX as a good gauge to hedge against the downside risks of the EU stocks. The originality of our work is two-fold. First, we extend the study of how global factors influence the EU stock market to the most recent period including the Russia–Ukraine conflict. Second, we perform this study on a sectoral basis. Therefore, the value of our findings is that they provide notable implications for market regulation and portfolio management.

Quantile connectedness between cryptocurrency and commodity futures

This study investigates the quantile dependence and spillovers for return and volatility of Bitcoin and futures of crude oil, copper, natural gas, and gold. We apply quantile vector autoregression and quantile connectedness approaches using a rolling-window method to examine spillover dynamics. The empirical results reveal that return spillovers increase when asset returns deviate from normal market conditions, and volatility spillovers are particularly increased during bullish market conditions. Moreover, the study finds that under bearish and normal market conditions, Bitcoin is a major recipient of return spillovers from all futures, and crude oil and copper are major transmitters of return spillovers to natural gas and gold, respectively. However, during bullish market states, Bitcoin becomes a major transmitter of return spillovers to other futures. Under unstable market conditions, gold is a major transmitter of volatility spillover to crude oil and natural gas. Furthermore, the directional link from Bitcoin to other futures is stronger when market issues exist.

Energy Financial Risk Management in China Using Complex Network Analysis

Effective energy financial risk management is crucial to ensure that China's economic system can remain stable. This article utilizes the quantile vector autoregressive spillover index model, complex networks, and deep learning methods to simultaneously assess both the internal and external energy financial market risks in China. Spillover effects under different market conditions are also examined. The research findings indicate that: (1) Under extreme market conditions, static total spillover values between internal and external markets exceed 70%, while under normal market conditions, they are only around 53% and 13%, respectively; (2) Crude oil and fuel oil as well as energy and stocks are important nodes in both internal and external markets; and (3) The attention-convolutional neural network-long short-term memory model outperforms the second-best performing model, and achieves an improvement of 12.9% and 21.4% in terms of mean absolute error and root mean square error, respectively; inclusion of early warning indicators leads to further improvements of 19.8% and 31.9%, respectively.

Connectedness between carbon and sectoral commodity markets: Evidence from China

The carbon market is an efficient mechanism to reduce CO2 emissions and thus respond to climate change. This work reveals the connectedness and its determinants between carbon and sectoral commodity markets in China. For this purpose, we apply the newly developed quantile-based connectedness measures that allow us to distinguish the relationship between markets under normal and extreme market conditions. The results indicate that: (i) the total spillovers in the carbon-commodity system in both tails are apparently bigger than that at the middle quantile, and are obviously affected by the Russia-Ukraine conflict; (ii) carbon markets receive more information from carbon-intensive commodity markets than from relatively low-carbon commodity markets in both tails; (iii) carbon markets mainly exhibit commodity attributes under normal market conditions, but exhibit commodity and financial attributes under extreme market conditions; and (iv) the spillovers between carbon and commodity markets are more influenced by monetary policy factors in both tails.

Impact of the COVID-19 pandemic on the stock market and investor online word of mouth

Investor sentiment based on social media Word-of-Mouth (WoM) has been shown to be predictive of stock returns under normal market conditions. However, the COVID-19 pandemic has brought unprecedented impact and uncertainty to the stock market. In this paper, we examine the predictability of social media-based WoM on day-ahead US stock market returns across three phases of the pandemic: pre-COVID, peak-COVID, and recovery. We collect over 24 million stock-related tweets from Twitter across these three periods and employ text-mining methods for feature engineering. We focus on two channels of WoM information, aggregate sentiments, and disaggregated topics, representing investors' overall opinions and discussion patterns about the stock market. We find that, in contrast to normal market conditions, the investor sentiment indices are not significant in predicting market returns during the peak-COVID and recovery periods, while for the pre-COVID period, our findings are consistent with prior studies. To explore the effect of disaggregated latent topics, we propose an ensemble feature selection approach that identifies relevant topics for each of the three periods. We find that adding topic information significantly improves model prediction during the peak-COVID period. Our findings imply that during a global socio-economic disruption and the subsequent recovery, the landscape of financial markets, examined through the lens of social media-based WoM, is different than during normal market conditions. Our findings also suggest that investors who primarily trade on sentiment may wish to leverage machine learning techniques to identify latent topic information, subsequently incorporating such information into their trading decision-making.

Alarming contagion effects: The dangerous ripple effect of extreme price spillovers across crude oil, carbon emission allowance, and agriculture futures markets

The inherent financial interconnections between crude oil prices, carbon emission allowances, and agriculture commodity futures warrant a thorough investigation as fossil energy consumption, carbon emissions, and agriculture plants are three critical components of global environmental protection. This paper aims to quantify not only the normal (mean quantile) static and dynamic spillover effects among them in both time and frequency domains but also the more critical extreme spillovers that occur across various time horizons. Additionally, we explore the vital role of carbon futures in hedging risk and enhancing the performance of oil and agricultural portfolios. Empirical results indicate that, under extreme market situations, the total spillovers among oil, carbon, and agriculture commodity futures are much larger than those under normal conditions. Furthermore, soybean and corn are generally the most potent information transmitters over other futures in the time domain, while carbon emission allowance futures act as an obvious spillover receiver at both normal and extreme market conditions across various time frequencies. Both the total spillover and the net spillover are centered at a short-term frequency (i.e., one to four weeks). Finally, we find that carbon futures can contribute to improving the hedge effectiveness and performance of oil and agricultural portfolios. These findings have valuable implications for policymakers, relevant producers/consumers, as well as futures investors.

Do geopolitical risks and global market factors influence the dynamic dependence among regional sustainable investments and major commodities?

This paper uses the Quantile Vector-Autoregressive (Q-VAR) technique to examine the connectedness between three regional (North America, Europe and Asia-Pacific) sustainability indices and major natural resource commodities including energy commodities (crude oil and natural gas), precious metals (gold, silver, and platinum), and industrial metals (steel, aluminium, and copper). It also uses a linear regression model to investigate the macroeconomic and geopolitical factors that drive the connectedness among these investments. The QVAR results reveal asymmetric connectedness among these investment indices, with the levels of total connectedness during extreme downside and upside market conditions being significantly stronger than the level of connectedness during normal market condition. The results also show that, on average, the amount of shocks the regional sustainable investment indices each received from the studied energy and metal commodity markets are higher (lower) than what they transmit to the commodity market during the extreme upside (downside) market condition. During the normal market condition, however, only the Asian Pacific sustainable investment receives more shock than it transmits to the studied commodity market indices, making a net shock receiver. Finally, geopolitical risks, business environment conditions, gold and fixed income markets and economic policy uncertainty are important predictors of return connectedness, although the predictive strength and direction vary across market conditions. We discuss the implications of our findings.

Green bond vs. Islamic bond: Which one is more environmentally friendly?

This research investigates the dynamic dependence and causality relationship of the S&P Kensho Clean Energy (CE) and Cleantech (CT) indices with two green bond indices, including the S&P Green Bond Index (GB) and Green Bond Select (GBS) indices, and four Islamic bond indices, including A-, AA-, AAA-, and BBB-graded Sukuk Indices. In the long- and medium-term, the dependence of CE and CT on Sukuk and green bond indices strengthens under normal or bearish market conditions based on quantile cross-spectral (coherency). Overall, among all alternative financing instruments, AA- and A-rated Sukuk indices and GBS indices have higher coherency with CE and CT. Furthermore, the direction of causality in the frequency domain is most commonly observed from Sukuk and bond indices to CT and CE. Once again, AA-rated, A-rated, and GBS have more significant causality effects on CT and CE for nearly all frequencies compared to other indices. These results present vital implications for both policymakers who want to advocate the development of environmentally friendly projects and investors who want to invest their capital in green economic activities in terms of diversifying and hedging their investments.

Further evidence on the returns to technical trading rules: Insights from fourteen currencies

This work studies the returns to technical trading rules utilising a sample of 14 currency pairs by applying five trading rules: Filter rules, Trading Range Breakout, Moving Average, and Bollinger bands over varying market conditions. The mixed results indicate that technical analysis based on the applied trading rules is appropriate for the trading strategies examined. The one per cent filter, 150-day MA rule and the Bollinger bands trading rule surpassed the other applied techniques in general performance. What is also clear is that these trading rules are rewarding over varying market conditions. Results from various subperiods, representing normal and stressful market conditions, generally confirm our main findings and show the added value of the trading range breakout rule, which delivers performance in strongly trending markets. Evidence from bootstrap analysis demonstrates the predictive ability of the Moving Average and Bollinger band trading rules.

Flight from Liquidity and Corporate Bond Yields

AbstractThis paper documents that in distress periods, liquidity constrained investors sell liquid corporate bonds and hold onto illiquid ones, a phenomenon which we refer to as flight from liquidity. Performing within issuer‐time analysis to properly control for credit risk, we find that flight from liquidity results in a decline in the liquidity premium. In other words, liquid bonds that are significantly more expensive in normal market conditions, lose more value in distress periods and trade at a closer, and sometimes at an indistinguishable, yield spread to their illiquid peers from the same issuer. We also find that these shocks to the liquidity premium are short‐lived and do not have a long‐lasting pricing impact. We provide suggestive evidence that the liquidity clientele effect derives these results. Our findings suggest that investment exposure to liquid bonds entails a unique risk arising during periods of distress.

Mechanisms of overpricing: An investigation on momentum crashes

This paper documents asymmetric overpricing for loser stocks across business cycle. In time of normal market conditions, we find strong interactions among key factors in determining momentum strength, and they are subject to significant investor attention. By contrast, during periods of poor market conditions, many of these interactions are not observed, suggesting that overpricing errors for loser stocks are generally weaker due to the lack of investor confidence and funding liquidity issue. A cross-sectional sample selection based on overpricing mechanism of loser stocks suggests that the damaged from momentum crashes can be eased and momentum profits can be significantly enhanced.

A cross-quantile correlation and causality-in-quantile analysis on the relationship between green investments and energy commodities during the COVID-19 pandemic period

PurposeThis paper aims to examine the cross-quantile correlation and causality-in-quantiles between green investments and energy commodities during the outbreak of COVID-19. To be specific, the authors aim to address the following questions: Is there any distributional predictability among green bonds and energy commodities during COVID-19? Is there exist any directional predictability between green investments and energy commodities during the global pandemic? Can green bonds hedge the risk of energy commodities during a period of the financial crisis.Design/methodology/approachThe authors use the nonparametric causality in quantile and cross-quantilogram (CQ) correlation approaches as the estimation techniques to investigate the distributional and directional predictability between green investments and energy commodities respectively using daily spot prices from January 1, 2020, to March 26, 2021. The study uses daily closing price indices S&P Green Bond Index as a representative of the green bond market. In the case of energy commodities, the authors use S&P GSCI Natural Gas Spot, S&P GSCI Biofuel Spot, S&P GSCI Unleaded Gasoline Spot, S&P GSCI Gas Oil Spot, S&P GSCI Brent Crude Spot, S&P GSCI WTI, OPEC Oil Basket Price, Crude Oil Oman, Crude Oil Dubai Cash, S&P GSCI Heating Oil Spot, S&P Global Clean Energy, US Gulf Coast Kerosene and Los Angeles Low Sulfur CARB Diesel Spot.FindingsFrom the CQ correlation results, there exists an overall negative directional predictability between green bonds and natural gas. The authors find that the directional predictability between green bonds and S&P GSCI Biofuel Spot, S&P GSCI Gas Oil Spot, S&P GSCI Brent Crude Spot, S&P GSCI WTI Spot, OPEC Oil Basket Spot, Crude Oil Oman Spot, Crude Oil Dubai Cash Spot, S&P GSCI Heating Oil Spot, US Gulf Coast Kerosene-Type Jet Fuel Spot Price and Los Angeles Low Sulfur CARB Diesel Spot Price is negative during normal market conditions and positive during extreme market conditions. Results from the non-parametric causality in the quantile approach show strong evidence of asymmetry in causality across quantiles and strong variations across markets.Practical implicationsThe quantile time-varying dependence and predictability results documented in this paper can help market participants with different investment targets and horizons adopt better hedging strategies and portfolio diversification to aid optimal policy measures during volatile market conditions.Social implicationsThe outcome of this study will promote awareness regarding the environment and also increase investor’s participation in the green bond market. Further, it allows corporate institutions to fulfill their social commitment through the issuance of green bonds.Originality/valueThis paper differs from these previous studies in several aspects. First, the authors have included a wide range of energy commodities, comprising three green bond indices and 14 energy commodity indices. Second, the authors have explored the dependency between the two markets, particularly during COVID-19 pandemic. Third, the authors have applied CQ and causality-in-quantile methods on the given data set. Since the market of green and sustainable finance is growing drastically and the world is transmitting toward environment-friendly practices, it is essential and vital to understand the impact of green bonds on other financial markets. In this regard, the study contributes to the literature by documenting an in-depth connectedness between green bonds and crude oil, natural gas, petrol, kerosene, diesel, crude, heating oil, biofuels and other energy commodities.

Extreme dependence and spillovers between uncertainty indices and stock markets: Does the US market play a major role?

This study investigates the spillovers and connectedness between uncertainty indices of oil gold, and stock (VIX), the economic policy uncertainty (EPU) and international stock markets (US, EU, UK, Japan, China, and Vietnam) under bearish, normal, and bullish market conditions. We employ a cross-quantilogram and quantile connectedness approach to investigate the contemporaneous linkages and asymmetries among stock markets under various financial market uncertainty. Using the cross-quantilogram approach, we find strong cross-quantilogram dependencies from the US stock market to other markets, even after controlling for the uncertainties. We find greater spillovers under volatile market conditions—bearish and bullish—than under stable market conditions using the quantile connectedness approach. The financial volatility or uncertainty indices are net transmitter (receiver) of spillover in the stock markets, especially in the bearish and tranquil (bullish) market status. Furthermore, all markets and the uncertainty indices except the US, Europe, and the UK are net receivers of spillovers in the lower quantile, while VIX uncertainty index shifts to being a net transmitter of spillovers in the lower and median quantiles. In the upper quantile, Japanese stock market and the uncertainty indices are net receivers of spillovers. VIX is strongly linked to the US (Chinese) market in tranquil (bullish) market conditions. In addition, the maximum amount of spillovers was attained during the beginning of 2020, coinciding with the onset of the COVID-19 pandemic's first wave.