Volatility Connectedness Research Articles

Clustering asset markets based on volatility connectedness to political news

To assess similarities in international asset markets’ responses to political news, we construct a political news index using advanced natural language processing. We then examine how the volatility across international asset markets is connected to the development of our political news index by measuring the daily directional connectedness using a VAR-based framework. Finally, we apply an unsupervised algorithm to cluster markets based on their volatility connectedness to political news. Our analysis reveals eight distinct clusters that reflect the markets’ sensitivities to political dynamics. This data-driven analysis offers insights into the influence of political developments on market volatility.

So different and yet so alike: A comparative analysis of firms' connectedness in the stock and corporate bond markets

AbstractWe study firms' return and volatility connectedness in the stock and corporate bond markets. Our approach to capturing firm‐specific return and volatility time series in the corporate bond market is based on a repeat‐sales index at the firm level. Measuring the pairwise connectedness of firms, we show that the two markets share similar dynamics in the connectedness of their firms. Firms tend to cluster within their own sectors and ties between firms in the corporate bond market are proportionally weaker. Financial firms play a critical role in the propagation of shocks, but this role differs markedly in the two markets.

Dynamic connectedness among market volatilities: a perspective of COVID-19 and Russia-Ukraine conflict

PurposeThis paper aims to investigate the dynamic volatility connectedness among 13 G20 countries by using the volatility indices.Design/methodology/approachThe connectedness approach based on the time-varying parameter vector autoregression model has been used to investigate the linkage. The period of study is from 1 January 2014 to 20 April 2023.FindingsThis analysis revealed that volatility connectedness among the countries during COVID-19 and Russia–Ukraine conflict had increased significantly. Furthermore, analysis has indicated that investors had not anticipated the World Health Organization announcement of COVID-19 as a global pandemic. Contrarily, investors had anticipated the Russian invasion of Ukraine, evident in a significant rise in volatility before and after the invasion. In addition, the transmission of volatility is from developed to developing countries. Developed countries are NET volatility transmitters, whereas developing countries are NET volatility receivers. Finally, the ordinary least square regression result suggests that the volatility connectedness index is informative of stock market dynamics.Originality/valueThe connectedness approach has been widely used to estimate the dynamic connectedness among market indices, cryptocurrencies, sectoral indices, enegy commodities and metals. To the best of the authors’ knowledge, none of the previous studies have directly used the volatility indices to measure the volatility connectedness. Hence, this study is the first of its kind that has used volatility indices to measure the volatility connectedness among the countries.

Dynamic volatility among fossil energy, clean energy and major assets: evidence from the novel DCC-GARCH

The objective of this paper is to assess the dynamic volatility connectedness between fossil energy, clean energy, and major assets i.e., Bonds, Bitcoin, Dollar index, Gold, and Standard and Poor's 500 from September 17, 2014 to October 11, 2022. The main motivation of the study relates to examining the dynamic volatility connectedness mentioned during periods of important events such as the recent coronavirus pandemic and the Russia–Ukraine conflict which has shown the vulnerability of economic and financial assets, energy commodities, and clean energy. The novel Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) approach is employed for the investigation of the sample period mentioned. Empirical analysis reveals that both the total and net volatility connectedness between assets is time-varying. The highest connectedness among the assets is observed with the onset of the coronavirus (COVID-19) pandemic, and it increases with some important international events, such as the Russia–Ukraine conflict, the referendum of Brexit, China–US trade war, and Brexit day. On average, the result shows that 32.8% of the volatility in one asset spills over to all other assets. The DCC-GARCH results also indicate that crude oil, bonds, and Bitcoin act as almost pure volatility transmitters, whereas the Dollar index, gold, and S&P500 act as volatility receivers. On the other hand, clean energy is found neutral to external shocks until the first quarter of 2020 and after that time, it starts to behave as a volatility transmitter. Based on the obtained results, we offer some specific policy implications that are beneficial to the US economy and other countries.Graphical Dynamic volatility connectedness between fossil energy, clean energy, and major assets (Bonds, Bitcoin, Dollar index, Gold, and Standard and Poor's 500)

Volatility Connectedness of MOVE Index and Bond Returns

Research Originality: To the best of our knowledge, this is the first study which measures the impact of the MOVE index on bond yield volatility by comparing its effect on both short-term and long-term. The paper contributes to the existing literature by providing a better understanding of the relationship between maturity and volatility spillover. Research Objectives: This study examines the dynamic volatility connectedness between 2 and 10-year bond yields and the MOVE Index. Furthermore, it is aimed at detecting the transmitter of volatility among variables. Research Methods: Using a data set from 2010 to 2022, the study utilizes a Time-Varying Parameter Vector Autoregression (TVP-VAR) model to analyse the dynamics and relationship between bond yields and the MOVE Index. Empirical Results: The study finds a significant volatility spillover between the MOVE Index and Türkiye's bond yields. Notably, the linkage between the MOVE Index and the 10-year bond rates is stronger than with the 2-year rates. Additionally, the MOVE Index emerges as the primary transmitter of volatility, impacting both bond yields. Implications: This study sheds light on the complexity and dynamics of volatility spread in Türkiye's bond market, providing essential insights for forecasting bond yields and shaping financial policies.JEL Classification: G17, C22, G12How to Cite:Baykut, E., Gökgöz, H. (2024). Volatility Connectedness of MOVE Index and Bond Returns. Etikonomi, 23(1), 27 – 46. https://doi.org/10.15408/etk.v23i1.36131.

What drives the return and volatility spillover between DeFis and cryptocurrencies?

AbstractIn this paper, we study the return and volatility connectedness between cryptocurrencies and DeFi Tokens, considering the impact of different uncertainty indices on their connectivity. Initially, we estimate a TVP‐VAR model to obtain the total connectedness between the two markets. We find that returns on the cryptocurrencies transmit significantly larger shocks and, thus, are responsible for most variations in the majority of DeFis' returns. Then, to analyse the impact of uncertainty on total return and volatility connectedness, we use four factors, namely, Economic Policy Uncertainty (EPU), The Chicago Board Options Exchange Volatility Index (VIX), Infectious Disease Equity Market Volatility Tracker (ID‐EMV) and Geopolitical Risks (GPR). We find that except for geopolitical risks, all three measures have a positive impact on return and volatility connectedness, while GPR exerts a negative impact. Finally, we provide implications for researchers, market participants and policymakers.

Borsa Istanbul Sectoral Connectedness Analysis

This study examines the volatility connectedness among banks, industrials, and services subindices of Borsa Istanbul using the Diebold-Yılmaz connectedness index methodology. The findings indicate that the banks index typically acts as a net receiver of connectedness from industrials and services indices. If the banks index is considered a proxy for the financial side of the Turkish economy while the other two represent the real side, this result aligns with earlier observations on the connectedness between the real and financial sides of economies. Specifically, it suggests that when a proxy for the real side incorporates financial variables, the real side tends to be a net source of connectedness most of the time. As shock propagators, industrials play a dominant role, and the banks index often moves in the opposite direction to the other two sectors.
 Key Words: Real and Financial Sectors, Financial Connectedness, Volatility, Borsa Istanbul.
 JEL Classification: C32, E44, G10.

Asymmetric Volatility Connectedness Among G7 Stock Markets

Asymmetric Volatility Connectedness Among G7 Stock Markets

Exploring volatility interconnections between AI tokens, AI stocks, and fossil fuel markets: evidence from time and frequency-based connectedness analysis

Energy and artificial intelligence (AI) are two of the top fields of the present time. However, investors of conventional energy assets have yet to consider the rapidly emerging AI-based assets for diversification. Owing to the rise of new categories of assets in the last two decades, which has sparked the interest of global investors to attain the most favorable outcomes, we examine volatility connectedness between AI stocks (MSFT-Microsoft, GOOG-Alphabet, AMZN-Amazon), AI tokens (AGIX-SingularityNET, OCEAN-Ocean Protocol, FET-Fetch.ai), and fossil fuel markets (WTI, BRENT, and GAS-natural gas) over the period from May 6, 2019, to July 8, 2023. We apply a novel three-dimensional framework in which we model time-domain and frequency-domain volatility spillovers at the median-, lower- and upper-quantiles in static as well as dynamic settings to achieve the objectives of this paper. We find the variation in static and dynamic connectedness between markets over time-frequencies and quantiles. Results reveal that AGIX, BRENT, FET, MSFT, and WTI are recipients, whereas AMZN, GAS, GOOG, and OCEAN are transmitters of the spillovers at the median quantile. Furthermore, we demonstrated that the short-term and long-term metrics for dynamic total connectedness might not consistently exhibit the same direction. Moreover, our analysis indicates that the short-term fluctuations predominantly influence the network's overall shock transmission, while the longer-term aspect has the potential to alter the role of a net transmitter or receiver of shocks. These findings provide portfolio managers, regulators, and policymakers with valuable information regarding portfolio adjustments, hedging, and financial stability.

Volatility connectedness on the central European forex markets

We perform a comprehensive assessment of volatility connectedness between the currencies of Central European (CE) countries using high-frequency data from 2009 to 2022. We provide evidence of asymmetries in connectedness that are dominated by negative volatility, especially during periods of economic distress. We also detect statistically significant economic or political events that lead to increased volatility connectedness. Plus, we document the impact of global shocks, not local ones. Further, the existing lag in the response of the spillover index to stressful events offers an opportunity to effectively hedge foreign exchange risk and to use the CE currencies as hedging tools. Finally, in terms of market-specific factors, liquidity dominates uncertainty as a connectedness driver. Our results are robust with respect to volatility measures and provide direct policy implications for portfolio composition and hedging.

Is investing in green assets costlier? Green vs. non-green financial assets

The urgent shift towards a zero- or low-carbon economy to combat climate change has heightened interest in green financial instruments among investors. Although green financial assets have grown significantly in a decade, investing in green assets is speculated to be costlier than traditional assets. Against this backdrop, we analyse whether investing in green financial assets is costlier than investing in traditional assets. We employ a comprehensive methodology, encompassing risk-adjusted returns, tail risks, time-varying correlations, novel quantile volatility connectedness, and portfolio implication techniques to show that most green assets outperform non-green assets in terms of risk-adjusted returns. Likewise, the potential loss likelihood of investing in green assets is lower. Our time-varying correlation outcomes reveal a strong positive association between green assets and their traditional counterparts, with limited hedging opportunities. Further, quantile vector autoregression results suggest that total connectedness between the green assets and their non-green pairs is lower in the normal market than in the extreme markets. Green and non-green asset pairs are also reciprocal in spreading and absorbing volatility shocks across most market states. Finally, the portfolio implications reveal that most asset pairs have a higher hedging cost; however, allocating more than 90% of funds to green assets can provide significant hedging effectiveness. Overall, investing in green assets is not costlier than investing in non-green assets, but they may be marginally rewarded. Our empirical findings have crucial implications for investors, policymakers, and regulatory bodies.

Exploring interconnectedness between climate change, renewable energy, technological innovation, and G-17 banking stock markets

This study schemes to explore novel insights from crisis risk spillovers and volatility connectedness among climate change, renewable energy, technological innovation, and G17-banking stocks. We adopt a diversified approach combining the time-varying parameter vector autoregressive (TVP-VAR) model for dynamic analysis with the Diebold-Yilmaz framework for static analysis and found that all markets have significant risk spillover and connectedness during the crisis. The ECOI index, OCEAN, and NEX are risk spillover transmitters and designed significant connectedness networks with these markets in crises. Further, France, Canada, and the US banking sectors are the main transmitters in the network during COVID-19. The US banking sector is also the primary risk transmitter during the Russia-Ukraine war. However, Japan and Korea banking stock markets consistently receive risk spillovers in the COVID-19 and Russia-Ukraine wars. This study provides significant implications for all stakeholders across the globe in these financial markets and for sustainable climate and environment.

Time-varying spillovers in high-order moments among cryptocurrencies

This study uses high-frequency (1-min) price data to examine the connectedness among the leading cryptocurrencies (i.e. Bitcoin, Ethereum, Binance, Cardano, Litecoin, and Ripple) at volatility and high-order (third and fourth orders in this paper) moments based on skewness and kurtosis. The sample period is from February 10, 2020, to August 20, 2022, which captures a pandemic, wartime, cryptocurrency market crashes, and the full collapse of a stablecoin. Using a time-varying parameter vector autoregressive (TVP-VAR) connectedness approach, we find that the total dynamic connectedness throughout all realized estimators grows with the time frequency of the data. Moreover, all estimators are time dependent and affected by significant events. As an exception, the Russia–Ukraine War did not increase the total connectedness among cryptocurrencies. Analysis of third- and fourth-order moments reveals additional dynamics not captured by the second moments, highlighting the importance of analyzing higher moments when studying systematic crash and fat-tail risks in the cryptocurrency market. Additional tests show that rolling-window-based VAR models do not reveal these patterns. Regarding the directional risk transmissions, Binance was a consistent net transmitter in all three connectedness systems and it dominated the volatility connectedness network. In contrast, skewness and kurtosis connectedness networks were dominated by Litecoin and Bitcoin and Ripple were net shock receivers in all three networks. These findings are expected to serve as a guide for portfolio optimization, risk management, and policy-making practices.

What do we know about COVID-19 media coverage and African stock markets? A time-varying connectedness analysis

ABSTRACT In this study, we explore the role of media coverage in the connectedness between African stock markets during the COVID-19 pandemic. Using the time-varying parameter autoregression connectedness metric and the Media Coverage Index (MCI), we analyse return and volatility connectedness between African stocks from January 2020 to October 2022. Our findings reveal that the MCI significantly drives risk transmission and financial market contagion from the global market to African markets. We document a peak in return (volatility) connectedness at 73% (55%) during the height of the pandemic in March 2020. The MCI transmits return shocks to all African stock markets except South Africa and Botswana, and volatility spillovers to all African markets. Our analysis suggests potential diversification attributes for stocks from Ghana, Mauritius, and Nigeria, which are strictly net receivers throughout the pandemic period. These findings have important implications for risk management, asset allocation, and market regulation decisions.

Spillovers and hedging effectiveness between islamic cryptocurrency and metal markets: Evidence from the COVID-19 outbreak

This study investigates the static and dynamic interdependence of the Islamic cryptocurrency and metal markets using the TVP-VAR methodology. The empirical findings suggest that Islamic cryptocurrencies are the recipients of both return and volatility spillovers, while most metals act as transmitters of these spillovers. The dynamic spillovers are intensified in the COVID-19 period compared to the pre-COVID-19 period. We find that the return connectedness is short-lived lived whereas the volatility connectedness is a long-term phenomenon. Finally, we also compute the optimal weights, hedge ratios, and hedging effectiveness during COVID-19. The results suggest that investors should add Islamic cryptocurrencies to metals portfolios in order to get maximum risk-adjusted returns during the pandemic crisis. Our findings are helpful for portfolio managers and investors in making decisions regarding diversification, portfolio allocation, forecasting, and hedging.

Multilayer networks in the frequency domain: Measuring volatility connectedness among Chinese financial institutions

We construct multilayer networks in the frequency domain, including short-term, medium-term, and long-term connectedness networks, to examine the volatility connectedness among Chinese financial institutions. We investigate the network topology of intra-layer and inter-layer by the proposed method on the static and dynamic samples. We observe that the volatility connectedness among financial institutions is heterogeneous in the frequency domain. Specifically, we find that long-term connectedness among financial institutions rises significantly during crisis events. In addition, we note that China Life Insurance, Ping An Bank, and most securities tend to play risk emitters at a specific layer during the period of stress rise. Finally, we find that the stock market crisis in 2015 changed the edge structure among financial institutions in the short-term and long-term. Our work provides a new perspective for financial connectedness and risk contagion.

Measuring the International Influence of the RMB and its Regional Heterogeneity: Theory and Evidence from the Global FX Market Network

ABSTRACT This study assesses the global and cross-regional influence of China’s currency, the renminbi (RMB). We first develop a theoretical framework to determine the channel through which progress in RMB internationalization can be mapped onto the market influence of the RMB exchange rate on that of other currencies. Using a volatility connectedness approach, we then construct a series of spillover indices reflecting the international influence of the RMB. We find that the RMB’s influence is worldwide but is smaller than that of major international currencies, such as the US dollar. Overall, the RMB has basically become a peripheral currency in Greater China, Central Asia, Eastern Europe, and some South Asian economies. Because of the Belt and Road initiative, regionalization of the RMB has also progressed in developing economies; however, China still has far to go in fully achieving internationalization of its currency.

The dynamic volatility nexus of geo-political risks, stocks, bond, bitcoin, gold and oil during COVID-19 and Russian-Ukraine war.

We investigate the dynamic volatility connectedness of geopolitical risk, stocks, bonds, bitcoin, gold, and oil from January 2018 to April 2022 in this study. We look at connectivity during the Pre-COVID, COVID, and Russian-Ukraine war subsamples. During the COVID-19 and Russian-Ukraine war periods, we find that conventional, Islamic, and sustainable stock indices are net volatility transmitters, whereas gold, US bonds, GPR, oil, and bitcoin are net volatility receivers. During the Russian-Ukraine war, the commodity index (DJCI) shifted from being a net recipient of volatility to a net transmitter of volatility. Furthermore, we discover that bilateral intercorrelations are strong within stock indices (DJWI, DJIM, and DJSI) but weak across all other financial assets. Our study has important implications for policymakers, regulators, investors, and financial market participants who want to improve their existing strategies for avoiding financial losses.

Connectedness between oil price shocks and US sector returns: Evidence from TVP-VAR and wavelet decomposition

This paper examines the dynamic return and volatility connectedness between oil price shocks (demand, supply, and risk shocks) and US sector returns from October 2001 to January 2022. For this purpose, we combine the decomposition of the time series in time scales through the wavelet approach with the application of the TVP-VAR model proposed by Antonakakis et al. (2020). Our results show the high dynamic connectedness between markets and allow the identification of the role of all sector indices (except Communication Services, Utilities and Real Estate) and risk shocks as net contributors of shocks to the system, whereas demand and supply shocks are net receivers of spillovers. We further explore and document from a portfolio performance perspective the benefits of diversified portfolios comprised of all consider sector indices that include assets linked to the calculation of oil price shocks according to Ready (2018).

The volatility connectedness of US industries: The role of investor sentiment

We investigate the influence of investor sentiment on the high-frequency volatility connectedness of US industry stock portfolios. Using a time series network approach, we find that two connectedness lags and triangular peer effects explain a significant amount of the network’s variability. We further find that squared investor sentiment is associated with a significant positive increase in the contribution of Energy stocks to volatility connectedness, at the expense of Consumer Services and Utilities stocks. The results imply the existence of sentiment-induced volatility transmission shocks driven by the Energy sector.