Spillover Index Research Articles

The impact of risk spillover network-based multidimensional systemic risks on the real economy

ABSTRACT Multidimensional systemic risk measures are constructed based on dynamic risk spillover networks to quantify the level, transmission speed, and transmission breadth. To investigate the significance, direction, magnitude, and persistence of the impacts of systemic risks on future real economic distribution, we combine the quantile regression model and Bootstrap quantile t test method. Finally, the heterogeneity of the impacts of systemic risks and SIFIs (systemically important financial institutions) risks is analysed. Our empirical results find that 1) most bank companies take the role of risk drivers in the financial system, while most security, insurance, trust, and real estate companies take the role of risk distributors and risk receivers. 2) the total spillover index, average shortest path length, and average clustering coefficient have significant impacts on the left tail and central tendency of future real economy, and the latter two measures also have significant impacts on the right tail. 3) SIFIs risks have earlier and greater impacts than Non-SIFIs (non-systemically important financial institutions) risks. Multidimensional systemic risks contain additional information to identify financial risks earlier than single institutions risks. 4) the impacts of financial risks on the real economy are persistent. The superimposition of impacts may trigger extreme risks.

Spillovers between cryptocurrencies, gold and stock markets: implication for hedging strategies and portfolio diversification under the COVID-19 pandemic

PurposeThis study analyzes the static and dynamic risk spillover between US/Chinese stock markets, cryptocurrencies and gold using daily data from August 24, 2018, to January 29, 2021. This study provides practical policy implications for investors and portfolio managers.Design/methodology/approach The authors use the Diebold and Yilmaz (2012) spillover indices based on the forecast error variance decomposition from vector autoregression framework. This approach allows the authors to examine both return and volatility spillover before and after the COVID-19 pandemic crisis. First, the authors used a static analysis to calculate the return and volatility spillover indices. Second, the authors make a dynamic analysis based on the 30-day moving window spillover index estimation.FindingsGenerally, results show evidence of significant spillovers between markets, particularly during the COVID-19 pandemic. In addition, cryptocurrencies and gold markets are net receivers of risk. This study provides also practical policy implications for investors and portfolio managers. The reached findings suggest that the mix of Bitcoin (or Ethereum), gold and equities could offer diversification opportunities for US and Chinese investors. Gold, Bitcoin and Ethereum can be considered as safe havens or as hedging instruments during the COVID-19 crisis. In contrast, Stablecoins (Tether and TrueUSD) do not offer hedging opportunities for US and Chinese investors.Originality/value The paper's empirical contribution lies in examining both return and volatility spillover between the US and Chinese stock market indices, gold and cryptocurrencies before and after the COVID-19 pandemic crisis. This contribution goes a long way in helping investors to identify optimal diversification and hedging strategies during a crisis.

European equity markets volatility spillover: Destabilizing energy risk is the new normal

AbstractWhile energy risk is increasingly recognized as a systemic risk, there is limited comprehensive analysis of the risk propagation in regional contexts. In this study, we examine oil and natural gas price changes and shocks in relation to equity market returns and volatility for 24 European Economic Area (EEA) countries. In addition to traditional panel regressions, we also deploy the Diebold‐Yilmaz (2014) spillover index for a closed network analysis. We differentiate in the cross‐section across the core EU block, PIIGS countries, EU enlargement countries joining after 2004, and other non‐EU countries, to provide insights into the ongoing debates on the European energy market stability. While we find evidence of the manifestation of energy risk throughout the sample period, we find that until 2019 the primary sources of volatility spillover in the EEA economic network arose from economic or political uncertainty. Energy risks, measured by large crude oil and natural gas price shocks also significantly contributed to equity market volatility, with increasing volatility risk arising from natural gas, a green labelled energy source after 2019. Last, we show that CEEC equity markets are more sensitive to oil and natural gas price shocks when domestic currencies depreciate against the Euro.

Frequency connectedness between DeFi and cryptocurrency markets

This study investigates the time-frequency spillovers across main DeFi (AVA, POWR, DOGE, MED, BAT, Link, Maker, and SNX) and four leading cryptocurrency markets (Bitcoin, Ethereum, Litecoin, and Ripple). The results show using the time-domain spillover index of Diebold and Yilmaz (2012) high connectedness across markets, underlying forecasting opportunities. Ethereum and Litecoin are the largest contributors and receivers of spillovers while MED is the lowest contributors of shocks in the system. Using the time-frequency spillover index by Baruník and Křehlík (2018), we show that the spillover at long term is higher than those at the short- and medium terms. BAT, LINK, and LTC are net contributors of spillovers at different time investment horizons while AVA, POWR, DOGE, and MED are net receivers irrespective of the time horizons. For the remaining market, they switch from net receiver to net contributor and vice versa. The major events (COVID-19, COVID-19 vaccination, and Ukraine-Russia tensions) intensify the dynamic of return spillovers at short-, medium-, and long-terms, implying contagion effects and a decline in the diversification benefits.

The influence of the spillover between futures and spot markets on hedging policy: evidence from Chinese stock markets

This paper examines the impact of risk spillovers between Chinese stock and futures markets on stock hedging policies. This paper calculates the correlation between the overall risk spillover and the hedging ratio, effectiveness, and hedging cost based on the hedging portfolio of stock futures of the CSI 300 index, the SSE 50 index, and the CSI 500 index. The results show that, on the one hand, the CSI 300 and CSI 500 futures markets are the propagators of risk spillovers, which shows that the price fluctuations of these two types of futures markets have a stronger impact on the stock spot price. The CSI 50 Futures market is the recipient of risk, and the CSI 50 Futures price is more vulnerable to the impact of the stock market. The results of the three types of samples show that when the stock market experiences extreme decline, the Risk Spillover Effect in the futures and spot markets will be significantly reduced, which indicates that the price of stocks and their derivatives is more deviated from the equilibrium relationship of pricing, and at this time, the function of derivatives in risk control is unreliable. On the other hand, the total spillover index of stock futures and spot markets has a positive correlation with hedging rate, hedging effectiveness and hedging cost. When the total spillover real number increases, it will get a more effective hedging effect. In addition, the hedging index is more sensitive to the overall spillover of high asset liquidity hedging portfolios. These conclusions provide a basis for adjusting asset positions according to the changes of Risk Spillover between the futures market and the spot market to obtain higher hedging effectiveness. The contribution of this paper is to reveal the possibility of finding the risk factors of hedging from the perspective of Risk Spillover in a very simple way, so it has reliable practicability in practice.

Quantitative easing and the spillover effects from the crude oil market to other financial markets: Evidence from QE1 to QE3

The relationships between the oil market and other financial markets remain poorly understood. In this paper, we first construct a set of spillover indices that measure the return spillovers from the oil market to other financial markets in the short, medium, and long terms, and then we examine the drivers of spillover intensity by focusing on the effect of quantitative easing in the U.S. The main empirical results are as follows. First, the return spillovers from the oil market to other markets are driven by various frequencies (short-term to long-term), and intensified during the global financial crisis of 2007–2009. Second, quantitative easing has different effects on the spillover intensity at different frequencies, with the effect on short-term spillovers being less significant. Third, our research provides the first empirical evidence for a double-edged sword effect of quantitative easing on the systemic risk from the frequency perspective.

Time-varying connectedness and causality between oil prices and G7 economies exchange rates. Evidence from the COVID-19 and Russia-Ukraine crises

PurposeThis study aims to attempt to investigate the time-varying causality and price spillover effects between crude oil and exchange rate markets in G7 economies during the COVID-19 and Russia–Ukraine crises.Design/methodology/approachThis study uses time-varying Granger causality test and spillover index.FindingsThis study finds a time-varying causality between exchange rate returns and oil prices, implying that crude oil prices have the predictive power of the foreign exchange rate markets in G7 economies in their domain. Furthermore, the total spillover index is estimated to fall significantly around COVID-19 and war events. However, this index is relatively high – more than 57% during the first wave of COVID-19 and decreasing slightly during the Russia–Ukraine conflict.Practical implicationsThis outcome supports the hypothesis that the majority of the time-varying interaction between exchange rates and oil prices takes place in the short term. As a result, the time-varying characteristics provide straightforward insight for investors and policymakers to fully understand the intercorrelation between oil prices and the G7 exchange rate markets.Originality/valueFirst, this study has reexamined the oil–exchange rate nexus to highlight new evidence using novel time-varying Granger causality model recently proposed by Shi et al. (2018) and the spillover index proposed by Diebold and Yilmaz (2012). These approaches allow the author to improve understanding of time-varying causal associations and return transmission between exchange rates and oil prices. Second, compared to past papers, this paper has used data from December 31, 2019, to October 31, 2022, to offer a fresh and accurate structure between the markets, which indicates the unique experience of the COVID-19 outbreak and Russia–Ukraine war episodes. Third, this study analyzes a data set of seven advanced economies (G7) exhibiting significant variations in their economic situations and responding to global stress times.

Net-Zero Policy vs Energy Security: The Impact on GCC Countries

Gulf Cooperation Council countries have accumulated large oil portfolio revenues. However, the world economy is seeking to reduce carbon emissions, and in turn, its reliance on fossil fuel resources through investments in renewable energyresources. The aim of this research is to analyze oil portfolio risk from anexporters’perspective, highlighting how relevant determinants, such as the increasing penetration of renewables in the importer counterparties, and financial and policy uncertainty, increase the volatility of oil export portfolios.We construct oil portfolios for four Gulf Cooperation Council countries (Kuwait, Oman, Saudi Arabia, United Arab Emirates) from 2008 to 2018, and compute volatility spillovers à la Diebold and Yilmaz. Then, the effects of policy and economic variables on volatility spillover indices are estimated using different panel linear regression models. We find rising renewable market shares significantly affects oil export portfolio risks and reduces adverse impacts on importing countries of oil market fluctuations.

Extreme risk contagion between international crude oil and China's energy-intensive sectors: New evidence from quantile Granger causality and spillover methods

We use quantile Granger causality and quantile spillover indices to analyze the connectedness of returns and volatility between crude oil and China's energy-intensive sectors (steel, electricity, coal, and petrochemical). The results show that both the strength and the direction of the connectedness are related to financial conditions (measured by quantiles), with the tail quantiles having much stronger connectedness than the middle level. Bidirectional Granger causality between crude oil and energy-intensive sectors occurs in the left (right) tail of returns as well as in the right tail of volatility. According to the quantile spillover indices, the spillover indices of crude oil's return and volatility are 2.79 % and 0.09 %, respectively, in the median condition, while they are as high as 71.09 % and 287.42 %, respectively, in the extreme condition. We emphasize that the volatility contagion from crude oil to energy-intensive sectors is significant only in the right tail quantiles. Among different sectors, the strongest connectedness between crude oil and the coal sector is observed at the extremes, followed by the petrochemical sector. The impact of China's energy-intensive sectors on the crude oil market deserves attention, as crude oil is a net receiver of information from China's energy-intensive sectors during certain periods.

Frequency spillover effects between natural gas market, uncertainty, and stock market: new evidence from China.

This study investigates the correlation between the natural gas market, uncertainty, and the stock market in China. The research uses the BK model and considers both high- and low-frequency bands. According to the dynamic spillover indices and connectedness network, natural gas market, uncertainty, and stock market spillover influences are concentrated in the high-frequency band within 12 trading weeks and minor in the low-frequency band. The natural gas market receives information in the return model. In contrast, the uncertainty of the financial and energy market is the primary transmitter and substantially impacts the natural gas market return. In the volatility model, the natural gas market remains the information receiver but is affected by financial market uncertainty and stock market volatility. During the financial crisis and the COVID-19 pandemic, results indicate that financial market uncertainty, energy market uncertainty, and stock market volatility exerted a substantial influence on the natural gas market.

Industry volatility spillover and aggregate stock returns

ABSTRACT We propose an industry volatility spillover index and find that this new predictor has an impressive in-sample and out-of-sample predictability of aggregate stock returns. A one-standard-deviation increase in this index leads to a 4.14% increase in the expected excess return over the next month. Furthermore, a mean-variance investor can realize sizable economic gains by using this spillover index in his/her asset allocation. The forecasting power of the spillover index remains significant after controlling for popular economic variables and newly proposed predictors. Due to the absence of learning, the spillover index shows increasing predictive ability over the recent period. The economic origins of the spillover index’s success stem from both the cash flow and discount rate channels, as well as significant association with investor sentiment and tail risk. The return predictability of this index is pervasive across characteristic-sorted portfolios and is particularly strong for large-cap or difficult-to-value stocks.

Dynamic Connectedness among Forex Markets of Pakistan and Its Major Trading Partners

Globalization and financial liberalization have made market participants and policymakers ponder over the importance of financial interconnectedness and shock spillovers. This discussion is in line with the International Portfolio Theory & Diversification. In this context, this study investigates the intensity and direction of return and volatility spillovers of foreign exchange (FX) markets of Pakistan and its major trading partners. Using an innovative research technique by Diebold and Yilmaz (2009, 2012), we separately compute measures of return and volatility spillovers. Furthermore, we also calculate total spillover, directional spillover, and net spillover indices using a daily data set over the period 1995 to 2019. To capture secular and cyclical movements in the trading partners’ FX markets, we rely upon a rolling window analysis. Our results, based on the spillovers indices, support the presence of dynamic connectedness among currency pairs of the major trading partners. We also note that among the sample economies, the USA, the EU, Singapore, and Malaysia are the main sources and originators of shocks spillover while Pakistan, India, Japan, Kuwait, Singapore, and UAE are net shock receivers. The rolling window analysis indicates that returns and volatility spillovers intensify during the phases of financial or economic anxiety. These results have some important implications for individuals working on risk management, portfolio diversification, and trading strategies.

Estimating the effect of international crude oil prices on RMB exchange rate with two-way fluctuation spillover.

International crude oil prices has become one of the important factors that affect exchange rate fluctuation. By combining TVP-VAR model and DY time-varying spillover index, this paper measures the two-way fluctuation spillover effect between international crude oil prices and RMB exchange rate from 2011 to 2022. The research results show that: (1) There are obvious time-varying characteristics among international crude oil prices, onshore RMB exchange rate and offshore RMB exchange rate, and they are strengthened when major events occur at home and abroad; (2) The spillover and absorption level among international crude oil prices, onshore RMB exchange rate and offshore RMB exchange rate is relatively limited; (3) After the "exchange rate reform on August 11", the market participation of RMB exchange rate has increased year by year, and the offshore RMB is mainly net spillover, while the onshore RMB is mainly reflected as a recipient; (4) The offshore RMB exchange rate has a driving effect on the onshore RMB exchange rate. Based on this, it is necessary to orderly promote the development and utilization of new energy, actively promote the market-oriented reform of RMB exchange rate, play the leading role of offshore market, and accelerate the construction of crude oil-RMB settlement system.

Market inefficiency spillover network across different regimes

This study examines risk transmission among 34 stock markets from the perspective of market inefficiency spillover effects. We suggest the use of the hidden Markov model along with the multifractal detrended fluctuation analysis to measure the degree of market efficiency for both bull and bear regimes. Diebold–Yilmaz spillover indices are used to document the asymmetric characteristics of market inefficiency spillovers in bull and bear regimes. When the market crashes or bubbles and the external economic and financial environment worsens, inefficiency spillovers increase significantly.

Leading and lagging role between financial stress and crude oil

PurposeThe crude oil market plays a key role in addressing the issue of energy economics. This paper aims to detect the causality relationship between the crude oil market and economy based on the financial system.Design/methodology/approachThis paper used the static and dynamic Hatemi-J Bootstrap Toda–Yamamoto and Diebold–Yilmaz connectedness index. The Hatemi-J Bootstrap Toda-Yamamoto approach allows researchers to use nonstationary data and that method is robust to nonnormal distribution and heteroscedasticity. The Diebold–Yilmaz connectedness index model provides researchers to detect the power of connectedness besides linkage direction. The analyzed period is the span from January 3, 2005 to October 3, 2022.FindingsThe results show bidirectional causality in the full sample but unidirectional causality before and after the 2008 financial crisis. During the 2008 financial crisis period and the COVID-19 period, there was a bidirectional and unidirectional causality, respectively. The connectedness approach indicates that the crude oil market affects financial stress through investors’ risk preferences.Research limitations/implicationsThe Diebold–Yilmaz spillover index model is based on vector autoregression methods with a stationarity precondition. However, some of the five dimensions that constitute the financial stress index (FSI) are nonstationary in level. Therefore, the authors takes the first difference of the nonstationary data.Practical implicationsThe linkage between the crude oil market and the FSI provides useful information for investors and policymakers. For instance, this paper indicates that an investor wanted to forecast future value of the crude oil (financial stress) should consider the current and past values of financial stress (crude oil). Moreover, policymaker should consider the crude oil market (FSI) to make a policy proposal for financial system (crude oil market).Originality/valueRecently, indicators of economic activity levels (economic policy uncertainty, implied volatility index) have begun to be considered to analyze the relationship between energy and the economy but very little is known in the literature about the leading and lagging roles of data in subsample periods and the linkage channel. The other originality of this research is using the new econometric approaches.

The asymmetric volatility spillover across Shanghai, Hong Kong and the U.S. stock markets: A regime weighted measure and its forecast inference

Based upon generalized spillover index method under Markov switching vector auto-regression (MS-VAR) framework, this paper aims at a regime dependent measure of the volatility spillover asymmetry across Shanghai, Hong Kong and the U.S. stock markets. For that, we apply a regime weighted measure of spillover and its asymmetry. The empirical results show that there existed evident negative asymmetric effect of spillover across the three markets, and it was regime dependent and time-varying: it got intensified under high volatility regime and turned out stronger after the Program of Shanghai-Hong Kong stock connect. The regime weighted measure of spillover asymmetry is found to be reliable; it could be explained appropriately with the economic policy uncertainty and investors' fear in the U.S., extreme shocks like crash of Shanghai stock market and COVID-19 as well as the capital flow through the Program. From in-sample evidence and out-of-sample forecast of Shanghai stock market, we infer that traditional forecast models could be improved if regime dependent spillover and its asymmetry are considered as additional factors.

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.

Green Sukuk or Green Bonds: A Comparative Study of Diversification and Hedging Prospects

This study employs the wavelet coherence and the spillover index methodologies to compare time-varying relationship between green sukuk (GSI) and green bonds (SPGB) with S&P 500, FTSE 100, KBW NASDAQ Financial Technology Index and Bitcoin between October 2019, and December 2022. While the predominant tonality of the coherence of GSI and SPGB was weak, GSI and SPGB failed to offset spillover during the covid-19 pandemic. Overall, GSI exhibited superior diversification and hedging performance. The spillover index results unveiled the influence of bearish market conditions on the intensity of market connectedness, prompting temporal transmission of risks between markets which can last up to eight days following the emergence of a shock. GSI slightly outperformed SPGB in portfolio diversification amid low total market connectedness, less net spillover, and less net-pairwise spillover as revealed by the findings of the spillover index. A combination of GSI or SPGB with S&P 500, FTSE 100, KBW NASDAQ Financial Technology Index, and Bitcoin under the same investment portfolio is likely to offer long-term investors diversification and hedging opportunities. Keywords: green sukuk, green bond, co-movement, spillover, wavelet, risk management.

Revisiting the connectedness between oil prices and uncertainty indicators in BRICS countries

The paper revisited the connectedness between oil prices (ROILP), economic policy uncertainty (EPU), and geopolitical risks (GPR) in the BRICS countries using data from January 1997 to October 2022. The study applied the spillover index, a rolling window based on the generalized VAR framework, and a wavelet coherence approach to investigate the passthrough, multiple regime changes, co-movements, and lead-lag status between the series. The study found that oil prices were a significant transmitter of shocks to other variables in the system among countries, with Brazil having the highest spillover index (11.7%) followed by South Africa (10.5%), India (10.2%), China (9.5%), and Russia (9.2%). The rolling spillover from GPR to ROILP suggested that GPR had a varying spillover effect on real oil prices in the BRICS economies, with Russia and India experiencing relatively stronger effects compared to the other countries. The spillover from EPU to ROILP showed that Brazil and China recorded significant increases in spillover effects, both respectively reached peaks of 54% and 61% suggesting their vulnerability to EPU and its impact on real oil prices during economic stress. The wavelet coherence largely revealed mixed directions of co-movement and heterogeneity between ROILP and EPU (GPR) across countries, indicating that the relationship between the variables was not consistent, varied over time and scale. Different from previous studies, the analysis focused on the direction of the spillover between oil prices and uncertainty measures, then estimated the numerical amount of the spillover to discover the variable that gave more shock than it received. While the wavelet coherence identified variations in coherence, across the time and frequency domain, the adoption of the rolling-window technique enabled the capture of the influence of arbitrary multiple regime changes in the spillover. The findings had some policy implications.

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.