Connectedness Risk Research Articles

Time–frequency co-movement and risk connectedness among cryptocurrencies: new evidence from the higher-order moments before and during the COVID-19 pandemic

Analyzing comovements and connectedness is critical for providing significant implications for crypto-portfolio risk management. However, most existing research focuses on the lower-order moment nexus (i.e. the return and volatility interactions). For the first time, this study investigates the higher-order moment comovements and risk connectedness among cryptocurrencies before and during the COVID-19 pandemic in both the time and frequency domains. We combine the realized moment measures and wavelet coherence, and the newly proposed time-varying parameter vector autoregression-based frequency connectedness approach (Chatziantoniou et al. in Integration and risk transmission in the market for crude oil a time-varying parameter frequency connectedness approach. Technical report, University of Pretoria, Department of Economics, 2021) using intraday high-frequency data. The empirical results demonstrate that the comovement of realized volatility between BTC and other cryptocurrencies is stronger than that of the realized skewness, realized kurtosis, and signed jump variation. The comovements among cryptocurrencies are both time-dependent and frequency-dependent. Besides the volatility spillovers, the risk spillovers of high-order moments and jumps are also significant, although their magnitudes vary with moments, making them moment-dependent as well and are lower than volatility connectedness. Frequency connectedness demonstrates that the risk connectedness is mainly transmitted in the short term (1–7 days). Furthermore, the total dynamic connectedness of all realized moments is time-varying and has been significantly affected by the outbreak of the COVID-19 pandemic. Several practical implications are drawn for crypto investors, portfolio managers, regulators, and policymakers in optimizing their investment and risk management tactics.

Risk Connectedness between Crude Oil, Gold and Exchange Rates in China: Implications of the COVID-19 Pandemic

Risk Connectedness between Crude Oil, Gold and Exchange Rates in China: Implications of the COVID-19 Pandemic

Price Risk Connectedness in the Principal Olive Oil Markets of the EU

Price Risk Connectedness in the Principal Olive Oil Markets of the EU

Social Connectedness Risk Among Middle-Aged and Older Black Men With Chronic Conditions

Social connectedness is a multi-factorial concept encompassing structural, functional, and quality aspects. For those with chronic conditions, threats to social connectedness can exacerbate illness symptomology and impose barriers to disease self-management. This study identified factors associated social connectedness risk among Black men ages 40+ years with one or more chronic conditions. Data from 2019 were analyzed from a national sample of 1,200 Black males collected with an internet-delivered questionnaire. Three logistic regression models were fitted to assess factors associated with not having enough people to call for help (structural), feeling isolated from others (functional), and not being content with friendships/relationships (quality). All regression models were adjusted for age, education, marital status, number of chronic conditions, self-reported barriers to disease self-management, and having cut-down or skipped social activities because of health problems. On average, participants were age 56.7 (±9.7) years and self-reported 4.0 (±2.9) chronic conditions. Approximately 23% were ages 65+ years, 45.4% cut-down or skipped social activities because of health problems, 25.2% did not have enough people to call for help, 26.0% felt isolated from others, and 23.8% were not content with friendships/relationships. Across the three regression models, men who were middle-aged (P<0.05), never married (P<0.01), cut-down or skipped social activities because of health problems (P<0.001), and reported more barriers to disease self-management (P<0.001) were significantly more likely to report social connectedness risk. Findings suggest that efforts to improve the self-management of illness symptomology may mitigate threats to structural, functional, and quality aspects of social connectedness among this male population.

Tail price risk spillovers along the US beef and pork supply chains

The objective of this paper was to investigate the intensity and the pattern of tail price risk spillovers in the US beef and pork industries. To this end, it estimates CoVaR functions for directly linked market pairs (farm–wholesale and wholesale–retail) along the relevant supply chains using quantile regressions. Over the total sample (1980–2020) and two sub‐samples (1980–1999 and 2000–2020), the beef industry appears to exhibit a higher degree of price risk connectedness relative to the pork industry. Positive and negative tail price events are transmitted between markets with the same intensity. However, tail price events (irrespective of sign) are likely to spillover with greater intensity backwards in the supply chain than forwards. This pattern of transmission may be a cause of concern about the efficiency of alternative meat marketing arrangements as risk‐sharing instruments.

Tail risk connectedness in the oil-stock nexus: Evidence from a novel quantile spillover approach

This study investigates the tail risk spillovers between the crude oil market and the stock markets of twelve major oil-importing and seven oil-exporting countries. We employ a novel quantile spillover index approach that allows measuring the dynamics and network of risk connectedness in the oil-stock nexus under different market conditions. Our results identify the risk transmitters, the risk receivers, and the evolution of the tail risk spillovers between oil market and stock markets over time. We find that extreme events will cause structural reversal of risk spillovers. Importantly, we provide evidence of heterogeneity in the cross-market risk spillovers at different quantiles, with higher risk spillovers occurring in both tails than those at the median. Among them, risk spillovers at the upside of the volatility distribution (0.95 quantile) are higher than those at the downside of the volatility distribution (0.05 quantile). Furthermore, we also document asymmetries in the risk spillovers at the upside and downside of the volatility distributions. The insights gained from this study suggest that extreme risks and close connections should be monitored, and their shocks should be mitigated by policy makers and market regulators.

Optimal time-varying tail risk network with a rolling window approach

We contribute to the literature on financial network contagion and systemic risk by developing a time-varying framework based on the rolling window technic and high dimensional quantile regression. The new selection criterion enables us to determine the optimal rolling width, which trades off the estimation accuracy and time variation of the tail risk network. Monte Carlo simulations show that our procedure significantly improves upon the estimation performance of the time-varying model. Using Chinese banking’s market data over 2009–2019, we measure the time-varying tail risk connectedness among banks using idiosyncratic returns. We find strong evidence of prominent risk dependence across banks during the stock crash. The network model using the optimal rolling window outperforms the traditional approaches in capturing structural changes.

Multiscale and partial correlation networks analysis of risk connectedness in global equity markets

This paper investigates the multiscale properties and the evolution patterns of risk connectedness in global equity markets using the sample from January 03, 2000 to December 24, 2018. GARCH–EVT–VaR model is used to measure systemic risk of global equity markets. We deal with the application of Univariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and Extreme Value Theory (EVT) to measure the Value at Risk (VaR) of global equity markets. We then introduce the maximal overlap discrete wavelet transform (MODWT) method and partial correlation coefficients into the complex network theory to construct multiscale and partial correlation networks in global equity markets. We find the evidence of the strong risk connectedness among global equity markets in both the overall and multiscale networks, and their topological properties vary across time–frequency horizons. The US and Eurozone equity markets play a predominant role in the process of risk transmission. Most developing markets seem to remain inactive in the multiscale networks of risk connectedness. The rolling-window analysis based on time–frequency domain demonstrates that risk networks tend to be more concentrated not only at the mid-term scales but also during the financial crisis. Empirical results reveal multiscale risk connectedness characteristics, providing useful information for regulators to formulate macro prudential supervision policy and investors to update the portfolio strategies and risk prevention measures.

International tail risk connectedness: Network and determinants

We construct a complete network of directional tail risk connectedness for 32 countries within a Least Absolute Shrinkage and Selection Operator (LASSO) Quantile Regression framework. In addition to highlighting the network’s essential features, including the key drivers and receivers of tail risk, we reveal some striking new network determinants. These include the predominant role of economy size, as well as the negative net impact of economic linkages such as trade and capital flows in addition to capital stocks on cross-country tail risk connectedness.

Systemic risk and financial contagion across top global energy companies

This study seeks to explore the firm-level interconnections in the fast changing and integrating global energy market. We investigate the risk connectedness using a Value-at-Risk (VaR) measure within a network composed of the top 20 global energy companies recognized by the Platts global energy company rankings, using the Diebold and Yilmaz's (2014) approach. We manage to identify the top risk contributors to the system across companies, regions and industries, and construct a total systemic risk index (TSRI) of the energy system. We further investigate which risk factors play a role in driving the evolution of the TSRI, again from a network perspective. The results show that its dynamics are mainly driven by the US stock market volatility and investors' sentiment in the financial market over the full sample, while energy market risks and exchange rate movements exert significant but short-term influences.

Coherence, extreme risk spillovers, and dynamic linkages between oil and China’s commodity futures markets

This paper investigates the time-frequency dependence, extreme risk spillovers, and dynamic linkages between oil and China’s commodity futures markets, using wavelet coherence, quantile connectedness approach, and the DECO-FIAPARCH (1,d,1) model. The results suggest that the oil market exhibits higher coherence with the copper, natural rubber, and fuel oil futures but low coherence with corn, soybean, soybean meal, and white sugar futures on a long-term scale. The crude oil market leads most of China’s commodity futures. The results of the DECO model point to the time-varying and low average equicorrelations between oil and China’s commodity futures. The total risk connectedness at the extreme lower quantile level (0.01) is higher than the conditional mean and conditional median level. WTI oil, Brent oil, soybean oil, and copper futures are the main spillover net-transmitters whereas the white sugar, soybean, soybean meal, cotton, corn, aluminum, natural rubber, and fuel oil futures are risk spillover net-recipients. The dynamic extreme negative risk spillovers are highly volatile and vulnerable to major international events such as the GFC, oil price plunge, and the COVID-19 pandemic. Finally, Brent oil offers better portfolio diversification benefits than WTI oil and the optimal-weighted portfolio illustrates the highest risk and downside risk reduction effectiveness.

Tail Risk Connectedness in the Refined Petroleum Market: A First Look at the Impact of the COVID Pandemic

We use the conditional autoregressive value at risk (CAViaR) model in combination with the time-varying parameter vector autoregressive (TVP-VAR) based connectedness approach to study the systematic tail risk transmission considering two types of crude oil (Brent and WTI) and also four refined petroleum products prices (gasoline, heating oil, jet fuel and propane) from January, 17, 1997 to December 11, 2020. For robustness purposes, we utilise three commonly used VaR probability cut-off points; that is, 10%, 5% and 2.5%. The selected sample period covers the first stage of the recent COVID-19 pandemic crisis. Results show that, both heating oil and kerosene are persistent net transmitters of uncertainty episodes in the network, signifying the importance of liquidity in the relevant markets. In addition, the role of the two measures of crude oil appears to shift around 2009 following developments in the energy market. Overall, findings suggest that, connectedness rises with major crisis episodes and that the recent COVID-19 pandemic appears to have the potential to propel tail risk and exposure to losses to levels akin to those of the Global Financial Crisis of 2007.

Dynamic dependence and risk connectedness among oil and stock markets: New evidence from time-frequency domain perspectives

This paper investigates the time-frequency dependence and risk connectedness among oil and stock markets in oil-importing and oil-exporting countries using the wavelet coherence and BK frequency connectedness method. Those two methods allow us to capture the dynamics of the dependence and risk connectedness over time as well as across different frequency bands (i.e. 1–5 days, 5–22 days, and more than 22 days). The empirical results demonstrate that the dependence structures among oil and stock markets are stronger on long-term scales and the lead-lag associations among oil and stock markets are mixed and time-varying. The total risk spillovers among oil and stock markets are mainly transmitted in the long-run (i.e. more than 22 days). The oil market receives much more risk spillovers from the stock markets in the US, EU, Canada, and Russia. The dynamic risk spillovers at diverse frequency bands are time-varying and heterogeneous. Besides, the major international crisis events, such as the GFC, oil price collapse, and the COVID-19 pandemic have greatly intensified the risk spillover magnitude. The time-frequency dependence and risk spillover analysis can offer great benefits for those energy and financial market participants with multivariate time-horizons in asset allocation and risk management practices.

Digital economy era: the role of the telecommunications sector in frequency-dependent default risk connectedness

We use frequency-dependent connectedness measures to study the role played by the telecommunications (telecoms) sector in sectoral default risk connectedness at three frequency bands, i.e. the short-, medium-, and long-term financial cycles. We extend credit risk spillovers analysis from the time domain to the frequency domain. Our findings indicate that investors in the global CDS sector index market have different investment horizons, but they prefer to process default risk information mainly within one week. In the within-region analysis, except for North America, the telecoms sector plays a significant role in transmitting net credit risk to the other sectors, especially in the short-term financial cycle. In the cross-region analysis, the European telecoms sector is the major net default risk transmitter on all three frequency bands. Our study has noteworthy empirical implications for consumption-based asset pricing models, cross-sector credit risk connectivity, and regional financial stability.

Risk Connectedness Heterogeneity in the Cryptocurrency Markets

This paper examines the risk connectedness across the seven cryptocurrencies, Bitcoin, Ethereum, Ripple, Litecoin, Stellar, Monero and Dash, who admit large capitalizations in the cryptocurrency market. The data sample is from August 7, 2015 to February 15, 2020. We apply the CAViaR model to measure the return risks of the cryptocurrencies, showing their similar risk tendencies with volatility clusterings during the beginning of 2017 and the end of 2018. The net pairwise spillover index developed by Diebold and Yilmaz (2012) is use as the measure for the risk connectedness among the cryptocurrencies. We find that the risk spillover directions are highly correlative with the capitalizations of the cryptocurrencies. The cryptocurrencies with small capitalizations transmit risks to those with large cryptocurrencies. In the risk downward tendency, the risk spillover levels among the cryptocurrencies are stronger than that in the risk upward tendency, while the spillover directions keep the same in both risk tendencies, except the cryptocurrency Monero, which may be due to the trading volume difference from the others. We use the generalized forecast error variance decomposition for the spillover index and explore the risk connectedness across the cryptocurrencies in differen time frequencies, including the short term (0-4 days), medium term (4-30 days) and long term (30-300 days) frequency. The risk spillovers in the short term frequency can be neglected, which implies the delay effects of risk spillovers. The risk spillovers in medium term frequency are mostly stronger than that in long term frequency. The dynamic connectedness result shows the risk spillover mean in the long term frequency is larger than that in the medium term frequency. An inverse result holds for the risk spillover range. The risk spillover fluctuations in the long and medium term frequency admit the coincident comparison for spillover levels in these two frequencies. The findings in this paper provide suggestions for regulators controlling the market stability and investors generating investment strategies.

Tail risk connectedness between US industries

AbstractWe use the Least Absolute Shrinkage and Selection Operator (LASSO) quantile regression technique to construct and analyse the complete tail risk connectedness network of the whole US industry system. We also investigate the empirical relationship between input–output linkages and the tail risk spillovers among US industries. Our findings identify the tail‐risk drivers, tail‐risk receivers, and tail‐risk distributors among industries and confirm that the actual trade flow between industries is a major driver of their tail risk connectedness.

Connectedness and systemic risk spillovers analysis of Chinese sectors based on tail risk network

This paper investigates the systemic risk spillovers and connectedness in the sectoral tail risk network of Chinese stock market, and explores the transmission mechanism of systemic risk spillovers by block models. Based on conditional value at risk (CoVaR) and single index model (SIM) quantile regression technique, we analyse the tail risk connectedness and find that during market crashes, stock market exposes to more systemic risk and more connectedness. Further, the orthogonal pulse function shows that Herfindahl-Hirschman Index (HHI) of edges has a significant positive effect on systemic risk, but the impact shows a certain lagging feature. Besides, the directional connectedness of sectors shows that systemic risk receivers and transmitters vary across time, and we adopt PageRank index to identify systemically important sector released by utilities and financial sectors. Finally, by block model we find that the tail risk network of Chinese sectors can be divided into four different spillover function blocks. The role of blocks and the spatial spillover transmission path between risk blocks are time-varying. Our results provide useful and positive implications for market participants and policy makers dealing with investment diversification and tracing the paths of risk shock transmission.

How connected is the global sovereign credit risk network?

Abstract This paper estimates the global network structure of sovereign credit risk by applying the Diebold-Yilmaz connectedness methodology on sovereign credit default swaps (SCDSs). The level of credit risk connectedness among sovereigns, which is quite high, is comparable to the connectedness among stock markets and foreign exchange markets. In the aftermath of the recent financial crises that originated in developed countries, emerging market countries have played a crucial role in the transmission of sovereign credit risk, while developed countries and debt-ridden developing countries have played marginal roles. Secondary regressions show that both trade and capital flows are important determinants of pairwise connectedness across countries. The capital flows became increasingly important after 2013, while the effect of trade flows decreased during the crisis and did not recover afterwards.

Risk connectedness of selected CESEE stock markets: a spillover index approach

PurposeThe purpose of this paper is to empirically evaluate risk spillovers between selected CESEE (Central, Eastern and South-Eastern Europe) stock markets in order to evaluate the possibilities of an international diversification of a portfolio.Design/methodology/approachThe VAR model and the Diebold and Yilmaz (2009, 2012) spillover index are used, with rolling indices estimation over time in order to observe dynamics, which is important for investment strategies. Data are monthly and include selected CESEE stock market indices which were available to the researcher.FindingsThe empirical analysis for the period of January 2012–June 2019 indicates that some country risks were the net emitter of shocks in the system (Slovenia and Czech Republic), whereas some were net receivers (Croatia and Ukraine). The results are robust with respect to changing the length of the rolling window analysis, which means that investors could utilize such an approach in a dynamic portfolio selection.Research limitations/implicationsObserving only selected markets due to data (un)availability.Practical implicationsThe paper shows how international investors can utilize the aforementioned methodology in order to make a more detailed analysis of the dynamics of stock markets connectedness so that international portfolios can be rebalanced according to the results and investors’ preferences.Originality/valueThis is the first such research which focuses on CESEE countries, since existing research is focused on more developed stock markets. Moreover, the empirical analysis extends to commenting the pairwise net indices over time, which is important for the dynamic portfolio rebalancing over time.

Measuring connectedness of euro area sovereign risk

We introduce a method for measuring the default risk connectedness of euro zone sovereign states using credit default swap (CDS) and bond data. The connectedness measure is based on an out-of-sample variance decomposition of model forecast errors. Due to its predictive nature, it can respond to crisis occurrences more quickly than common in-sample techniques. We determine the sovereign default risk connectedness using both CDS and bond data in order to obtain a more comprehensive picture of the system. We find evidence that there are several observable factors that drive the difference between CDS and bonds, but both data sources still contain specific information for connectedness spill-overs. In general, we can identify countries that impose risk on the system and the respective spill-over channels. Our empirical analysis covers the years 2009–2014, such that the recovery paths of countries exiting EU and IMF financial assistance schemes and the responses to the ECB’s unconventional policy measures can be analyzed.