Strong Co-movement Research Articles

Dynamic Comovement between Indian Commodity Futures, Economic Policy Uncertainty and Geopolitical Risk: Evidence from Wavelet Analysis

The present study examines the dynamic comovements between two global risk factors and commodity futures returns. The study considers the daily futures price of nine commodities spanning from January 4th, 2012 to September 29th, 2023. The study employs wavelet analysis and wavelet- based Granger causality tests to analyze dynamic comovement and causal relationship between global risk factors and commodity futures return at different time horizons. The study results show a strong comovement between the US economic policy uncertainty (USEPU) and commodity futures return except for silver and mentha oil. On the other hand, the geopolitical risk (GPR) exhibits a weak relationship with gold, lead, zinc, and energy commodities across all-time frequencies. Further, the Wavelet Granger causality test results provide strong evidence that commodities futures return cause the USEPU in all the time horizons. Followed by the geopolitical risk reports significant evidence that commodities futures return causes GPR in all time horizons.

Risk Transmission and Hedging Strategies Between Chinese Stock Market and Major Trading Partners Along the Belt and Road in COVID-19 Scenario

This paper focuses on the multi-scale spillover and time-varying dependence of Chinese stock market and its important trading partners along the Belt and Road around the COVID-19 crisis. We use multiple methods - the DY (12) and BK (18) connectedness approaches that investigate dynamic and frequency connectedness; the wavelet coherence and the time-varying CoVaR to examine the connection between price lead lags and systemic risk spillovers. Our empirical results show spillovers to be asymmetric, and short-term spillovers dominating. Meanwhile, the level of spillover in the system increased sharply after the COVID-19. In the medium and long-term frequency domains, wavelet coherence reveals strong co-movement between the Chinese market and its major trading partners. We see that the highest level of systematic risk spillovers occurs at the beginning of the COVID-19 outbreak. Finally, the weighting of the Chinese stock market in the effective portfolio rises after the COVID-19 outbreak.

Cyclinaclity Effects of Exchange Rates and Oil Prices

Understanding the concept of time scales is crucial when modeling economic and financial decisions. Within the time-frequency domain, this study delves into the relationship between fluctuations in oil prices and exchange rates across major oil-importing and exporting countries. The investigation employs various cross-wavelet techniques within the continuous wavelet transform framework, with a particular focus on wavelet coherence and phase-difference over the period 2000 to 2020. The results underscore a notable diversity in the connection between the oscillations of oil prices and exchange rates across diverse countries. This relationship is subject to temporal variations and is contingent upon the specific time horizon under consideration. In particular, our analysis reveals strong co-movements between oil prices and exchange rates across various time intervals and frequencies. Importing oil countries like New Zealand, Singapore, Brazil, and Taiwan exhibit particularly pronounced co-movements. Similarly, exporting oil countries such as Kuwait, Mexico, Russia, and Canada also display strong associations between oil prices and exchange rates. These correlations are intricately tied to key macroeconomic events, further highlighting the complex interplay between oil prices and exchange rate movements in different global regions. While a robust connection is evident in numerous countries, the strength of the relationship appears significantly weaker in several others. This variance underscores the nuanced nature of the association between the fluctuations in oil prices and exchange rates across the global landscape.

The spillover effects among financial stress, investor sentiment, and GCC stock markets: evidence under the bearish and bullish market states

ABSTRACT This study delves into an exploration of volatility spillover between financial stress, investor sentiment, and stock market index returns in the Gulf Cooperation Council (GCC) countries using the DCC-GARCH, wavelet coherence, and BEKK-GARCH models. A robustness check, applying Diebold and Yilmaz’s (2012, 2014) methodology, verifies network measures across markets. Significant patterns in financial stress index were observed during crises, with strong co-movement between financial stress, investor sentiment, and stock returns, especially during COVID-19 pandemic. Volatility transmission from stock returns to investor sentiment and the financial stress index was notable in some markets. Additionally, we found a significant volatility transmission from the financial stress index to market returns during periods of bearish stress in Bahrain and Kuwait and bullish stress across all countries. These findings offer insights for investors and fund managers optimizing portfolios and managing risk exposure.

Comparing the hedge and safe haven properties of individual commodities for China and United States equity sectors

PurposeThe purpose of this paper is to scrutinize the safe haven benefits of 13 individual commodities for the USA and Chinese equity sectors during the financial turmoil period. Therefore, sectoral investors in the USA and China could invest in those specific commodities that provide stable returns during the health crisis and financial turmoil periods.Design/methodology/approachThe daily data spans from February 1, 2015, to July 28, 2022. The present study applies several different approaches to analyzing the data set. The author apply the cross-quantilogram (C.Q) methodology to capture the lead-lag bivariate quantile interdependence between two stationary time series variables during the bearish, bullish and normal periods. Then the study used the hedging effectiveness (HE) and conditional diversification benefits (CDB) approaches to capture the hedging and diversification benefits of commodity classes and individual commodities.FindingsThe noteworthy findings of the quantilogram methodology reveal that livestock and agriculture commodities serve as better refuges as compared to the precious metals and energy index in both countries. On average, precious metals failed to serve as safe haven investments for the USA and Chinese equity market sectors. All energy commodities except soybean oil had strong comovements with China and the US equity sectors during bearish, bullish and normal periods. Lean hogs, fiddler cattle and live cattle are perfect hedging assets for both countries due to the presence of blue color at normal and bullish periods in all C.Q heat-maps. The HE table depicts that commodity indices and individual commodities failed to serve as hedging assets for the Chinese equity sectors. But commodities are semistrong hedging assets for the US equity sectors and the S&P 500 due to the average HE values being 0.7 and above. The CDB values depict that precious metals provide diversification benefits in both equity markets.Practical implicationsThe present study results have important implications for equity sector investors of the USA and China in suggesting particular commodity during the financial turmoil period. During the bearish market condition, risk averse equity sector investors can invest in livestock commodities and agriculture commodities, due to their relatively stable returns. In addition, policymakers can use the analysis insights to formulate policy tools and monitoring mechanisms, effectively mitigating the unfavorable effects arising from asymmetric dependence between commodities and equity sectors during the upper tail, middle and lower tail. Policymakers can suggest equity investors to invest in which commodity during extreme conditions.Originality/valueThe current study has the following points of originality. First, to the best of the author’s knowledge, this is the first study to investigate the individual commodities’ roles as safe havens taken from all four major commodity classes. More importantly, it is also noticeable that the safe haven abilities of commodities are usually tested for the stock market, but the equity sectors are ignored. Therefore, the present study used both stock market and sectoral indices data.

Co-movement between COVID-19, oil price and American stock market during 2020: Fresh investigation from partial and multiple wavelet methods

The pandemic has brought economic and social challenges to society, policymakers, financial market investors and firms. The current study aims to shed light on the financial aspects of COVID-19 during the first wave in 2020. In doing so, the study aims to explore the association between COVID-19 cases, oil prices, and the stock market returns (Standard & Poor's). In this paper, the authors use the daily new cases of COVID-19, oil price and stock market return from 21 January 2020 to 10 April 2020. To check the nexus between studied factors, we apply Wavelet Coherence, Partial Wavelet Coherence and Multiple Wavelet Coherence methods. The graphical empirics of Wavelet Coherence Methods mentioned interesting outcomes; (i) COVID-19 and oil price tend to move in the same direction, with COVID-19 leading, (ii) COVID-19 affect stock market returns, and (iii) oil price and stock market returns have strong co-movement.

Energy prices and exchange rates in the Eurasian Economic Union: evidence from Fourier Toda-Yamamoto approach

ABSTRACT Even though there is a growing literature on the co-movement between energy and currency markets, this issue is still unclear for the Eurasian Economic Union (EEU) countries. Based on this gap in the literature, the purpose of this study is to analyse the dynamic relationships between energy prices (crude oil and natural gas) and exchange rates for the EEU countries (Armenia, Belarus, Kazakhstan, Kyrgyzstan, and Russia) for the 2015–2022 period by means of cointegration and causality approaches without and with structural shifts. While the cointegration test without structural change does not find uniform steady-state equilibrium, considering structural change in the cointegration model reveals the existence of the long-run relationship between exchange rates and energy prices. The causality analysis uncovers predictive information between energy and currency markets in the EEU countries driven by the oil market; and accounting for smooth structural breaks in causality analysis based on the Fourier Toda-Yamamoto approach reinforces these findings. The empirical findings hence imply a strong co-movement between energy and currency markets in the EEU countries shaped by the oil market.

Labor market peer firms: understanding firms’ labor market linkages through employees’ internet “also viewed” firms

AbstractThis paper studies the grouping of firms based on their labor-market connections, a significant departure from the traditional approach of grouping based on product-market connections. It also proposes a measure of labor market peers by analyzing the “also viewed” companies on two major online labor market platforms, LinkedIn and Glassdoor. Using the labor market peer measure, I examine whether firms that hire employees with similar skills and that are presumably exposed to the same labor-related risks and shocks exhibit a strong comovement of stock returns and accounting-based performance variables. I find that labor market peers overlap but differ from traditional product-market-based industry groupings, have significant incremental power to explain stock return and accounting-based performance measure comovements, and outperform traditional industry groupings in explaining return and wage comovements when a base firm shares more labor skills with its peers. Overall, the study highlights that labor market peers capture fundamental linkages between firms that are challenging to identify using traditional industry measures.

Production complementarity and information transmission across industries

Economic theory suggests that production complementarity is an important driver of sectoral co-movements and business cycle fluctuations. We operationalize this concept using a measure of production complementarity proximity (COMPL) between any two companies. We show firms from different industries but are closely aligned in COMPL exhibit strong co-movement in their operating, investing, and financing activities, as well as quarterly earnings revisions and monthly returns. We further document a lead-lag effect in their returns, such that a long-short strategy based on recent COMPL peer returns yields a monthly 6-factor alpha of 122 basis points. This inter-industry momentum spillover effect is not explained by other network-based mechanisms, such as shared analyst coverage. We conclude information transmission takes place along complementarity networks, but stock prices do not update instantaneously.

Can Cryptocurrencies Provide Better Diversification Benefits? Evidence from the Indian Stock Market

Cryptocurrency has recently emerged as a financial asset among policymakers, investors and academics as a new alternative asset in the financial landscape. This research intends to empirically evaluate the safe haven, diversification and hedging potentials of digital currencies against the Indian equity market during different time frames. Four cryptocurrencies (Bitcoin, Ethereum, Binance Coin and Ripple) and Nifty 50 index data have been collected on a daily basis, from 26 July 2017 to 31 August 2023, for this purpose. Using wavelet-based methods, the study has discovered higher volatility in Nifty 50 and cryptocurrency prices during the crisis and stronger co-movement between pairs of equities and cryptocurrencies. Furthermore, the study finds that, under normal economic conditions, cryptocurrency can hedge the Indian stock market over short-term, medium-term and long-term investment horizons. However, investing in cryptocurrencies in the Indian stock portfolio for the short term does not give any safe haven or diversification advantages during times of economic crisis. Finally, we anticipate that the findings of our study will provide valuable insights into the potential usage of cryptocurrencies in the Indian stock market, both in stable and turbulent economic conditions. JEL Codes: G01, G41, N2, P34

Assessing the linkage of energy cryptocurrency with clean and dirty energy markets

The primary objective of this study is to explore the dynamic connectedness that exists among energy cryptocurrencies, clean energy metals, and conventional dirty energies. Moreover, we aim to investigate the time-varying effects of uncertainties on the connectedness that exists among these asset classes. We uncover heterogeneous reactions among energy cryptocurrencies and other assets. Common positive co-movement clustering is revealed mostly during the periods of the cryptocurrency crash and boom in 2018 and 2020, and the oil market crash, respectively. However, a constant weak and strong co-movement over shorter and longer horizons is observed throughout the sample period for all pairs analysed, with varying outcomes for longer horizons. Combining VIX and US EPU as measures of financial and economic uncertainty, we contend that uncertainty significantly affects energy cryptocurrencies, clean and dirty energies, and clean energy metals, with relatively weak impacts or influences observed for longer investment horizons. Our findings are highly beneficial to cryptocurrency investors, alternative asset investors, and policy makers, especially during periods of market turmoil.

Dynamic connectedness amongst green bonds, pollution allowance policy, social responsibility and uncertainty

PurposeThis study explores the interconnectedness and complexity of risk-varied climate initiatives such as green bonds (GBs), emissions trading systems (ETS) and socially responsible investments (SRI). The analysis covers the period from September 2011 to August 2022, using six indices: three representing climate initiatives and three indicating uncertainty.Design/methodology/approachTo achieve this, the study first examines dynamic lead-lag relations and correlation patterns in the time-frequency domain to analyse the returns of the series. Additionally, it applies an innovative approach to investigate the predictability of uncertainty measurements of climate initiatives across various market conditions and frequency spillovers in the short, medium and long run.FindingsThe findings indicate changing relationships between the series, increased linkages during turbulent market periods and strong co-movements within the network. The ETS is recommended for diversification and hedging against uncertainty indices, whereas the GB may be suitable for long-term diversification.Practical implicationsThis study highlights the role of climate initiatives as potential hedges and contagion amplifiers during crises, with implications for policy recommendations and the asymmetric effects on market connectedness.Originality/valueThe paper answers questions that previous studies have not and contributes to the literature regarding financial risk management and social responsibility.

Multi-scale analysis of the co-movement between China's new energy vehicle industry and Tesla: Evidence from capital market

China is currently the world's largest new energy vehicle market, and the development of its new energy vehicles is crucial to the sustainable development of mankind. As a leader in the new energy vehicle industry, Tesla's entry into the Chinese market has an important impact on its new energy vehicle industry, and the study of the relationship between the two is of great significance in promoting the development of China's new energy vehicle industry. Therefore, we analyzed the complex relationship between Tesla and China's new energy vehicle industry from 2013 to 2022 based on the stock market perspective using modal decomposition, Maximum mutual information coefficient, and transfer entropy. Mutual information coefficient results show that Tesla has stronger co-movements with China's New Energy Vehicle Manufacturing sector, and its strength is highest in the medium- and long-term time scales, up to 0.196 and 0.529, respectively. whereas the transfer entropy results show that Tesla has a stronger information transfer effect on the Vehicle Manufacturing sector and Charging Pile sector than on the New Energy Vehicles Battery sector and New Energy Vehicles Parts sector. However, in general, Tesla's information overflow to the whole Chinese new energy vehicle industry is on the rise. The Chinese government can appropriately give Tesla certain favorable policies and encourage Chinese enterprises to cooperate with it, giving full play to Tesla's catfish effect and technology demonstration effect, and then promoting the further development of China's new energy vehicle industry.

Can Equity Market Risk be Diversified with the Help of ESG Investment and Commodities?

The present study examines the time–frequency relationship between conventional equity, environmental, social and governance (ESG) Index and commodities in the Indian context. The present study uses wavelet-based decomposition methodology, and wavelet coherence to examine the co-movement and coherence among these markets. In addition, wavelet analysis explored in-phase and out-phase time–frequency relationships among the variables. The findings of the study highlight the coherence between these markets in different time horizons. Our results show that the impact of the COVID pandemic persisted across the time scales in the case of ESG index, Nifty and Oil. There is strong co-movement between ESG index and Nifty, hence ESG index cannot be considered as a hedge during crisis periods. Whereas gold exhibits hedging attributes against the volatility of conventional equity. The empirical findings have several implications for understanding the hedging attributes of ESG indices and commodities against conventional equities.

Co-Movements between an Asian Technology Stock Index and Cryptocurrencies during the COVID-19 Pandemic: A Bi-Wavelet Approach

This study investigates the co-movement patterns of Asia technology stock indices and cryptocurrencies during the COVID-19 pandemic. The analysis examines Bitcoin and Ethereum, China’s Tech index (XA90), and India’s Tech index (NSEIT) from 2017 to 2021, representing both before and during COVID-19. To visually explore the co-movement between these variables, a bi-wavelet method is employed. This approach allows for an examination of how these variables move together over time coherently. There were noticeable changes in the co-movement patterns between technology stock indices and cryptocurrencies during COVID-19 compared to before the pandemic. The duration of co-movements decreased significantly after the emergence of COVID-19. The previous financial crisis had a longer time horizon for joint movement, lasting 256 days. However, during the pre-COVID-19 period, XA90 exhibited a strong co-movement with Bitcoin over this extended period but weakened afterward when COVID-19 emerged. Conversely, NSEIT showed a significant co-movement with both Ethereum and Bitcoin in the initial stages of the pandemic. Before that period, NSEIT had muted price movements along with BTC. These changes in price co-movements suggest shifts in herding behavior due to the pandemic. Notably, cryptocurrency markets have demonstrated faster recovery compared to technology stock markets.

Co-Movement and Performance Comparison of Conventional and Islamic Stock Indices during the Pre- and Post-COVID-19 Pandemic Era

This study conducts a comparative analysis of the performance of Islamic and conventional indices in both developed and developing countries and territories, considering the pre- and post-COVID-19 pandemic periods. The research employs performance index tools and time–frequency wavelet-based analysis to assess how the COVID-19 pandemic affected the performance, volatility, and co-movement of Islamic and conventional stock indices. The findings reveal that Islamic stock indices are more resilient and tend to outperform conventional stocks during crisis periods in both developed and developing countries and territories, and this trend holds true in the long and short term across most countries. The analysis of wavelet coherence indicates a strong co-movement and coherence between Islamic and conventional indices. Furthermore, the study reveals that in developing countries and territories, the co-movement is characterized by weak coherence and high volatility compared to developed countries and territories. The study highlights the significance of Islamic indices as safe havens for investors during times of crisis, suggesting that including Islamic equities in investment portfolios can potentially yield higher returns compared to conventional indices. This research holds practical value for individual traders involved in the online trading of global stock indices, aiding them in constructing and designing internationally diversified portfolios. Unlike previous studies that focused on specific countries and territories and indices, this study offers a comprehensive examination of the behavior of Islamic and conventional indices across major global markets during both crisis and noncrisis periods. The results contribute significantly to the existing literature and offer valuable insights for investors.

The three co’s to jointly model commodity markets: co-production, co-consumption and co-trading

In this study, we develop a framework, based on a global vector autoregression (GVAR) model, to unite two perspectives on commodity markets, the commodity-specific, single-market-centered approach, investigating the micro- and macroeconomic drivers of commodity prices, and the market perspective, which observes joint movements of commodity prices on exchanges. Thereby, the GVAR model disentangles single market from inter-market effects, while simultaneously accounting for the impact of macroeconomic factors. We apply the framework to the six industrial metals markets, reflecting their interdependencies via their co-production, co-consumption, or co-trading relation. In particular, the numerous significant spillover effects in the cross-commodity dimension underline the importance of jointly modeling commodity markets. While the strong co-movement between industrial metal prices is represented exceptionally well by our framework, the microeconomic supply and demand attributes of the commodities have significant impact, within and across markets, even on price variables, highlighting their relevance in modern commodity market models. Moreover, we detect global shocks, e.g., an increase in global demand, affect each commodity market to a similar extent.

Risk spillovers connectedness between the US Fintech industry VaR, behavioral biases and macroeconomic instability factors: COVID-19 implications

PurposeThe main objective of this paper is to analyze the dynamic volatility spillovers between the investor's behavioral biases, the macroeconomic instability factors and the value at risk of the US Fintech stock market before and during the COVID-19 pandemic.Design/methodology/approachThe authors used the methodologies proposed by Diebold and Yilmaz (2012) and the wavelet approach.FindingsThe wavelet coherence results show that during the COVID-19 period, there was a strong co-movement among value at risk and each selected variables in the medium-run and the long-run scales. Diebold and Yilmaz's (2012) method proved that the total connectedness index raised significantly during the COVID-19 period. Moreover, the overconfidence bias and the financial stress index are the net transmitters, while the value at risk and herding behavior variables are the net receivers.Research limitations/implicationsThis study offers some important implications for investors and policymakers to explain the impact of the COVID-19 pandemic on the risk of Fintech industry.Practical implicationsThe study findings might be useful for investors to better understand the time–frequency connectedness and the volatility spillover effects in the context of COVID-19 pandemic. Future research may deal with investors' ability of constructing portfolios with another alternative index like cryptocurrencies which seems to be a safer investment.Originality/valueTo the best of the authors' knowledge, this is the first study that relies on the continuous wavelet decomposition technique and spillover volatility to examine the connectedness between investor behavioral biases, uncertainty factors, and Value at Risk of US Fintech stock markets, while taking into account the recent COVID-19 pandemic.

Integration and risk transmission in the market for crude oil: New evidence from a time-varying parameter frequency connectedness approach

In this study, we introduce a novel time-varying parameter vector autoregressive frequency connectedness approach to obtain refined measures of the frequency transmission mechanism and dynamic integration among six well-established crude oil benchmarks. The period of investigation ranges from May 14th, 1996 to December 3rd, 2020 and focuses on the differences between short-term (1–5 days) and long-term (6–100 days) crude oil volatility connectedness. Findings are suggestive of relatively strong co-movements among crude oil volatility over time. For most part of the sample period, connectedness occurs in the short-run; nonetheless, starting approximately in 2010, long-run connectedness gains much prominence until at least the end of 2015. Long-run connectedness is also prevalent at the beginning of 2020 caused by the COVID-19 pandemic. We opine that periods of increased long-run connectedness relate to deeper changes in the market for crude oil that bring about new dynamics and associations within the specific network.

COVID-19, Russia-Ukraine war and interconnectedness between stock and crypto markets: a wavelet-based analysis

ABSTRACT This paper aims to investigate the impacts of the COVID-19 pandemic and Russia-Ukraine war on the interconnectedness between the US and China stock markets, major cryptocurrency and commodity markets using the wavelet coherence approach over the period from January 1 2016 to April 18 2022. The aim is to understand how the COVID-19 pandemic and the Russia-Ukraine war have affected the hedging efficiency of volatile crypto-currencies and gold. Wavelet coherency analysis unveils perceptual differences between the short-term and longer-term market reactions. In the short-run, we find strong co-movements during the first and second waves of the pandemic. During the first wave, longer-term investors were driven by the belief of future pandemic demise. They make use of time diversification that results in positive returns. During the Russia-Ukraine war, S&P 500 leads Bitcoin, BNB, and Ripple whereas Ethereum leads S&P 500 and SSE.