Time-varying Volatility Spillover Research Articles

Risk transference between climate variability and financial derivatives: Implications for global food security

We investigate the impacts of the El Niño-Southern Oscillation (ENSO) on global food security by using information embedded in financial derivatives. Using a state-of-the-art general circulation model (GCM) climate forecasting system that generates observations on the phase and magnitude of ENSO, we create novel indices that track its uncertainty throughout time. Together with the option implied volatilities of core food inputs (wheat, maize, rice, and soybean), we model the time-varying volatility spillover from ENSO to each commodity, capturing the direction and intensity of risk transference. Simulating Gaussian and non-Gaussian impulse responses that mimic an increase in climate variability show a persistent impact on the price uncertainty of the commodities lasting up to three months. We also show that shocks are contingent on the phase of ENSO, with warmer conditions in the Eastern Pacific (i.e., the El Niño phase) increasing risk transference for soybean and rice. In contrast, the La Niña phase has a material influence on the uncertainty of wheat and maize. In all, we reveal a volatility transmission channel of climate variability that affects financial markets, suggesting valuable inferences about the impact of climate shocks can be derived from the rich information embedded in commodity-linked derivatives.

Markov-switching multifractal volatility spillovers among European stock markets during crisis periods

ABSTRACT This research investigates time-varying volatility spillovers and connectedness among European stock markets during the COVID-19 pandemic and the Russia – Ukraine war, two events that destabilized global markets. With data from 20 European stock markets spanning 17 December 2019, to 17 March 2022, we employ the TVP-VAR model and estimate volatility using the Markov-switching multifractal volatility technique. Findings from log-volatility estimates suggest that markets are highly connected, with price movement driven mainly by spillover effects from other markets in the same region. Most emerging markets are net receivers of volatility, with most of Europe’s major markets being net transmitters of shocks. The COVID-19 pandemic appears to have impacted European stock markets more than the Russia – Ukraine war. Shifting to the results obtained based on MSM volatility estimates, we find that markets strongly correlate for both high and low volatility. In the case of a high volatility regime, we document the dominance of Finland, Denmark, and Iceland over major European markets. In contrast, under a low volatility regime, we note the dominance of major markets, including the UK and France, over emerging markets in Europe. The findings reveal the diversification potential of emerging European stock markets.

The impact of the Russia–Ukraine conflict (2022) on volatility connectedness between the Egyptian stock market sectors: evidence from the DCC-GARCH-CONNECTEDNESS approach

PurposeThis study investigates the impact of the Russia–Ukraine war (2022) on the volatility connectedness between Egyptian stock market sectors.Design/methodology/approachThis study employs the newest dynamic conditional correlation (DCC)-generalized autoregressive conditional heteroskedasticity (GARCH)-CONNECTEDNESS approach to examine volatility connectedness in a sample of ten sectors in the Egyptian stock market, namely banks, education, food, healthcare, industry, information technology, real estate, resources, transportation and travel, ranging from February 1, 2019 to May 31, 2022.FindingsThe findings show that connectedness among the Egyptian stock market sectors varies depending on the time. The average dynamic connectedness measure among sectors in Egypt is 73.24%. This average was 85.63% during the Russia–Ukraine War (2022). The author also shows that the transportation sector is the most significant net transmitter of volatility in the remaining sectors during the Russia–Ukraine War (2022).Practical implicationsThis study intends for policymakers to examine the co-movements, market variations and volatility spillover of stock markets, particularly during crises. Furthermore, the results help investors gain insight into diversifying the investors' portfolio assets to optimize profits.Originality/valueTo the best of the authors' knowledge, no study has investigated the implications of the war between Russia and Ukraine (2022) on sectoral interconnectedness within the stock markets in any country and discussion and empirical evidence from African countries are lacking. This study fills this gap in the literature. Additionally, the author uses the newest approach, the DCC-GARCH-CONNECTEDNESS approach, to describe the time-varying volatility spillover between economic sectors in Egypt.

Dynamic volatility spillover effects between wind and solar power generations: Implications for hedging strategies and a sustainable power sector

Large-scale wind and solar development is impeded severely by their inherent volatilities. This study applies a novel approach to reduce the renewable investment risks from the production perspective, although there are many related studies from the financial market perspective. Using daily data from the Gansu province of China between 2013 and 2018 based on a VAR-GARCH model, we first find that wind and solar power generation are volatile, negatively correlated, and exhibit strong time varying spillover effects . We then apply three different approaches to calculate the hedge ratios and optimal capacity portfolios of wind and solar in Gansu. The result from the best MVP approach shows that the optimal capacity weights are 28.3% for wind and 71.7% for solar. This study sheds light on designing a joint development strategy to reduce security risks and integration costs during transition toward a low carbon power sector. • A novel approach is applied to reduce the risks of high penetration of wind and solar power. • Time-varying volatility spillovers between wind and solar production are examined. • A significantly negative dynamic correlation between wind and solar is empirically demonstrated. • The complementarity relationship between wind and solar is employed to construct their optimal capacity weights.

Quantile risk spillovers between energy and agricultural commodity markets: Evidence from pre and during COVID-19 outbreak

The spillover effect is a significant factor impacting the volatility of commodity prices. Unlike earlier studies, this research uses the rolling window-based Quantile VAR (QVAR) model to describe the conditional volatility spillover between energy, biofuel and agricultural commodity markets. Since the magnitude of connectedness and spillover effects may switch between bearish and bullish market states over time, a QVAR model is a relatively realistic and appropriate approach to capture the connectedness as compared to the mean-based approaches of Diebold and Yilmaz (DY; 2009, 2012, & 2014) which are mostly used in the literature. To this end, we employ volatility estimates by using the realized variance advanced by Parkinson (1980). Specifically, we investigate the time-varying volatility spillovers and connectedness among agricultural markets (wheat, corn, sugar, soyabean, coffee, and cotton), energy markets (gasoline, crude oil, natural gas) and biofuel (ethanol) markets from January 12, 2012 to May 10, 2021. By comparing our empirical analysis with results from the DY spillover model, we establish that connectedness is stronger in the left and right quantiles than those in the mean and median of the conditional distribution, emphasizing the importance of systematic risk spillovers during extreme market movements. Furthermore, results find that volatility spillovers and connectedness in the right tail is higher than in the left tail. In particular, we document significant volatility spillovers from agricultural markets to energy markets during extreme markets conditions and observe the dominance of agricultural markets over energy markets. To ascertain the impact of COVID-19 on the volatility of markets examined, we divide our sample into sub-samples and observe significant variation in the level of volatility spillovers and connectedness across the markets before and during the outbreak of COVID-19. Finally, some useful implications are summarized for investors' portfolios and risk avoidance.

COVID-19 Shock and the Time-Varying Volatility Spillovers Among the Energy and Precious Metals Markets: Evidence From A DCC-GARCH-CONNECTEDNESS Approach.

The outbreak of the COVID-19 epidemic intensified the volatility of commodity markets (the energy and precious metals markets), which created a significant negative impact on the volatility spillovers among these markets. It may also have triggered a new volatility risk contagion. In this paper, we introduce the DCC-GARCH-CONNECTEDNESS approach to explore the volatility spillover level and multi-level spillover structure characteristics among the commodity markets before and during the COVID-19 epidemic in order to clarify the new volatility risk contagion patterns across the markets. The results implied several conclusions. (i) The COVID-19 epidemic has significantly improved the total volatility spillover level of the energy and precious metals markets and has enhanced the risk connectivity among the markets. (ii) The COVID-19 epidemic has amplified the volatility of the crude oil market, making it the main volatility spillover market, namely the source of volatility risk contagion. (iii) The COVID-19 epidemic outbreak enhanced the external risk absorption capacity of the natural gas and silver markets, and the absorption level of the external volatility spillover improved significantly. Furthermore, the risk absorption capacity of the gold market weakened, while the gold market has remained the endpoint of external volatility risk during the epidemic and has acted as a risk stabilizer. (iv) The volatility spillover among markets has clear time-varying characteristics and a positive connectedness with the severity of the COVID-19 epidemic. As the severity of the COVID-19 epidemic increases, the volatility risk connectivity among the markets rapidly increases.

Time-varying volatility spillovers between real exchange rate and real commodity prices for emerging market economies

This study aims to investigate real effective exchange rates and real commodity prices volatility transmission among Mexico, Indonesia and Turkey. According to the results, there is a bidirectional causality relationship between precious metals and the real exchange rate. However, this relationship varies over time. Especially in times of crisis such as the Covid-19 pandemic, the transfer of volatility disappears. Precious metals have a safe haven feature against the exchange rate. On the other hand, the reverse is not true. During the Covid period, the bilateral risk transfer between crude oil and exchange rate disappears. The crude oil and exchange rate have safe haven feature during the Covid-19 period. Only for Indonesia, there is risk transfer from oil to exchange rates.

Volatility communicator or receiver? Investigating volatility spillover mechanisms among Bitcoin and other financial markets

This paper proposes an integrated TVP-VAR model to investigate the volatility spillover mechanisms among different financial markets as well as their respective roles in the global volatility transmission system, including Bitcoin, crude oil, gold, stocks, foreign exchange and natural gas market. Utilizing the time-varying volatility spillover indices, we find that the Bitcoin, gold, foreign exchange and natural gas serve as volatility communicators. On the contrary, the crude oil and stock market act as volatility receivers. In addition, roles of each major commodity in the volatility spillover system are found to be significantly correlated with their respective financial properties. Specifically, Bitcoin is found to be a hedge rather than a safe haven in the financial system. Moreover, we discover that the overall volatility spillovers across the financial market system are more likely to be caused by shifts in external market attention among the different markets.

Modeling the frequency dynamics of spillovers and connectedness between crude oil and MENA stock markets with portfolio implications

This paper examines the frequency of spillovers between crude oil futures and the Middle East and North Africa (MENA) stock markets. We use the methodologies proposed by Diebold and Yilmaz (2012) and Baruník and Křehlík (2018) and the wavelet coherency approach. The results show time-varying volatility spillovers in the considered markets. The short-term spillovers are higher than their intermediate-term counterparts. The highest jump in spillovers occurs during the COVID-19 outbreak, followed by the global financial crisis and the recent oil price crash. The spillovers are higher for oil-exporting countries than oil-importing countries. Saudi Arabia, Qatar, and the United Arab Emirates (UAE) are the main contributors to spillovers in the short and intermediate terms. Brent oil, Egypt, Morocco, and Turkey are the net receivers of spillovers in the short term, and they switch to become net contributors to spillovers in the intermediate term. Turkey and oil-exporting stock markets receive more spillovers than oil-importing stock markets irrespective of the time frequency. Wavelet analysis shows evidence of co-movements between oil futures and stock markets at intermediate and low frequencies. The lead–lag relationships between crude oil and stock markets are mixed and time-varying. Moreover, a mixed portfolio offers diversification benefits. Hedging is more expensive during the pandemic period and particularly in the intermediate term compared to the short term. Hedging effectiveness is highest during the COVID-19 pandemic in the short and intermediate terms for almost all markets.

Eatery, energy, environment and economic system, 1970–2017: Understanding volatility spillover patterns in a global sample

This paper explores the complex patterns of volatility transmissions among oil price, agricultural energy consumption, biofuel consumption, food production, greenhouse gas (GHG) emissions, and gross domestic product (GDP) per capita in a sample of 157 countries spanning the period 1970–2017. We employ a panel vector autoregressive model to construct time-varying volatility spillover indices. The main findings include the following: (i) On average, spillover effects account for about 20% of total volatility in the system in the sample period. (ii) Agricultural energy consumption is the dominant gross volatility emitter in this system, while GHG emissions are the dominant gross volatility receiver. (iii) Oil price fluctuation contributes approximately 43% to the total volatility in GDP per capita. We do not observe a strong link between agricultural emissions and GDP per capita in terms of volatility spillovers. Importantly, given the observed significant impact of oil price, energy consumption, and food production on changes in welfare, we conjecture that monitoring the interactions of these sectors is a policy priority.

COVID-19 and asymmetric volatility spillovers across global stock markets

In this study, I improve the assessment of asymmetry in volatility spillovers, and define six asymmetric spillover indexes. Employing Diebold-Yilmaz spillover index, network analysis, and my developed asymmetric spillover index, this study investigates the time-varying volatility spillovers and asymmetry in spillovers across stock markets of the U.S., Japan, Germany, the U.K., France, Italy, Canada, China, India, and Brazil based on high-frequency data from June 1, 2009, to August 28, 2020. I find that the global markets are well connected, and volatility spillovers across global stock markets are time-varying, crisis-sensitive, and asymmetric. Developed markets are the main risk transmitters, and emerging markets are the main risk receivers. Downside risk dominates financial contagion effects, and a great deal of downside risk spilled over from stock markets of risk transmitters into the global markets. Moreover, during the coronavirus recession, the total degree of volatility spillover is staying at an extremely high level, and emerging markets are the main risk receivers in the 2020 stock markets crash.

Do hedge and merger arbitrage funds actually hedge? A time-varying volatility spillover approach

We examine the interaction between funds implementing hedge and merger arbitrage strategies and a set of traditional assets comprising equities, bonds, gold, crude oil, currency, commodities and real estate, by applying a time-varying spillover approach for the period 1/1/2010-7/31/2020. Results indicate that the funds absorb the fewest shocks from equities, crude oil, gold and currency compared to commodities, bonds and real estate. Furthermore, we test the effective hedging ability of these funds by estimating hedge ratios and optimal portfolio weights. Taking a short position in the volatility of the funds provides impeccable hedging effectiveness for all asset classes, except currency.

Volatility spillovers between strategic commodity futures and stock markets and portfolio implications: Evidence from developed and emerging economies

This study examines the short-, intermediate-, and long-term volatility spillovers between developed (Australia, Canada, France, Germany, Japan, UK, and US) and emerging BRICS (Brazil, Russia, China, India, and South Africa) stock markets and strategic commodity futures markets (oil and gold). Using Baruník and Křehlík's (2018) methodology, we find evidence of time-varying volatility spillovers, which are intensified under major events. Moreover, the total volatility spillovers are higher in the short term than both the intermediate and long terms. A portfolio management analysis reveals that a mixed portfolio (commodity and stock markets) provides a higher level of hedging effectiveness for both emerging and developed markets. In addition, the hedging effectiveness in BRICS markets is more pronounced than in developed markets irrespective of frequencies. The hedging effectiveness is also higher using gold than oil and under the short term rather than both the intermediate and long terms. These results provide important implications for risk management and funds allocation.

Time-varying volatility spillovers between oil prices and precious metal prices

This paper tackles whether there is a return and volatility spillover effect between oil price and precious metal prices such as gold, silver, platinum, and palladium using the causality-in-variance test approach proposed by Hong (2001). This study utilizes the daily data covering the period from 1990 to 2019 and the empirical findings reveal that there is causality-in-mean relation running from the oil return series to precious metal return series. The causality-in-mean test results show that oil price is Granger cause of all precious metals. Similarly, we find a volatility spillover effect from the oil market to the precious metal market according to the causality-in-variance test results. On the other hand, we find evidence in favor of the bidirectional volatility spillover effect between oil and silver return series. Also, the volatility spillover effect from oil to platinum seems to be weak when it is compared to the other precious metals. Moreover, the time-varying causality-in-variance test results provide a different picture because the null hypothesis of no volatility spillover from precious metals to oil is rejected at specific periods. More interestingly, we find that there is a sequential feedback relationship between oil and silver. Finally, the volatility spillover effect from the oil market to the precious metal markets seems to be strong specifically after the 2000s.

Analyzing time-varying volatility spillovers between the crude oil markets using a new method

Abstract The spillover effect is an important factor affecting the volatility of crude oil price. Basing on the study of Diebold and Yilmaz (2009, 2012, 2014), we propose a new method that calculates the time-varying volatility spillover indexes by the generalized forecast error variance decomposition of TVP-VAR-SV model. Then, using the new method, we study the time-varying volatility spillovers between four major crude oil markets (WTI, Brent, Oman, Tapis) from November 29, 2002 to July 13, 2018. By comparing the results of our new method and traditional rolling window method, we verify the superiority of our new method. The results show that the volatility spillovers calculated by the new method are clearer, more stable and not outlier sensitive. From the estimated results of time-varying volatility spillovers, we find that the volatility spillover between crude oil markets is slowly increasing, but there are obvious cyclical changes. And from the correlation analysis and the Granger causality test, we find that the volatility and volatility spillovers are positively correlated and are two-way Granger causality, which supported for the market infection hypothesis of King and Wadhwani (1990).

Time-varying volatility spillover between Chinese fuel oil and stock index futures markets based on a DCC-GARCH model with a semi-nonparametric approach

This paper reveals some new evidence on the volatility spillover between fuel oil and stock index futures markets in China by considering its time-variant feature. Time-varying effect is specified by the Legendre polynomials in a DCC GARCH model. Moreover, the semi-nonparametric (SNP) approach accounting for marginal excess kurtosis and asymmetry is applied. We find different time-evolving patterns of volatility spillover across multiple periods due to the structural breaks. The direction of mean volatility spillover is found to be bilateral. Moreover, the strength of mean volatility spillover shows that the effect from stock index to fuel oil futures is much stronger than the other way around. This implies that the stock index futures market plays a leading role in information processing. These findings have important implications for the fuel oil stakeholders as well as risk management.

Investigating Spillover Effects between Foreign Exchange Rate Volatility and Commodity Price Volatility in Uganda

This study investigates the impact of commodity price volatility spillovers on financial sector stability. Specifically, the study investigates the spillover effects between oil and food price volatility and the volatility of a key macroeconomic indicator of importance to financial stability: the nominal Uganda shilling per United States dollar (UGX/USD) exchange rate. Volatility spillover is examined using the Generalized Vector Autoregressive (GVAR) approach and Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) techniques, namely the dynamic conditional correlation (DCC), constant conditional correlation (CCC), and varying conditional correlation (VCC) models. Overall, the results of both the GVAR and MGARCH techniques indicate low levels of volatility spillover and market interconnectedness except during crisis periods, at which point cross-market volatility spillovers and market interconnectedness sharply and markedly increased. Specifically, the results of the MGARCH analysis show that the DCC model produces the best results. The obtained results point to an amplification of dynamic conditional correlations during and after the global financial crisis (GFC), suggesting an increase in volatility spillovers and interdependence between these markets following the global financial crisis. This is also confirmed by the results of the total spillover index based on the GVAR analysis, which shows low but time-varying volatility spillover that intensified during periods of high uncertainty and market crises, particularly during the global financial crisis and sovereign debt crisis periods.

Modelling time varying volatility spillovers and conditional correlations across commodity metal futures

This paper examines how the most prevalent stochastic properties of key metal futures returns have been affected by the recent financial crisis using both mapped and unmapped data. Our results suggest that copper and gold futures returns exhibit time-varying persistence in their corresponding conditional volatilities over the crisis period; in particular, such persistence increases during periods of high volatility compared with low volatility. The estimation of a bivariate GARCH model further shows the existence of time-varying volatility spillovers between these returns during the different stages of such a crisis. Our results, which are broadly the same in relation to the use of mapped or unmapped data, suggest that the volatilities of copper and gold are inherently linked, although these metals have very different applications.

Time-varying volatility spillovers between stock and precious metal markets with portfolio implications

This paper investigates the time-varying risk spillovers between precious metals (gold, silver, palladium, and platinum) and major stock markets (USA, Japan, Europe and Asia) using the spillover index of Diebold and Yilmaz (2012). We also analyze asset allocations, hedge ratios, and hedging strategies. The results show evidence of volatility spillovers between precious metal and stock markets. Further, all the stock markets (except for Japanese market) are a source of volatility spillovers and the four precious metal markets are net receipt of volatility spillovers during the Global Financial Crisis and European Sovereign Debt Crisis. Finally, we find evidence of cross-market hedging, asset allocation, and hedging effectiveness.

Who are the net senders and recipients of volatility spillovers in China’s financial markets?

Using a spillover index approach, we investigate volatility spillovers across China’s stock, bond, commodity futures, and foreign exchange (FX) markets and their evolution during the period 2005–2015. We find that these four financial markets are weakly integrated. The stock market is the largest net sender of volatility spillovers to other markets, followed by the bond market, and the FX and commodity futures markets are net recipients. The time-varying volatility spillovers show that the recent global financial crisis and the European sovereign debt crisis strongly influenced China’s financial markets.