Dynamic Volatility Spillovers Research Articles

Causal relation and dynamic volatility spillover between commodity market and stock market: empirical evidence from India

Causal relation and dynamic volatility spillover between commodity market and stock market: empirical evidence from India

Causal relation and dynamic volatility spillover between commodity market and stock market: empirical evidence from India

Causal relation and dynamic volatility spillover between commodity market and stock market: empirical evidence from India

Dynamic volatility spillovers between industries in the US stock market: Evidence from the COVID-19 pandemic and Black Monday

We examine the volatility spillovers among various industries during the COVID-19 pandemic period. We measure volatility spillovers by defining the volatility of each sector in the S&P 500 index and implement a static and rolling-window analysis following the Diebold and Yilmaz (2012) approach. We find that the pandemic enhanced volatility spillovers, which reveals the financial contagion effects on the US stock market. Second, there were sudden, large changes in the dynamic volatility spillovers on Black Monday (March 9, 2020), much of it due to the energy sector shock. These findings have important implications for portfolio managers and policymakers.

Dynamic interdependence and volatility spillovers across bunker fuel markets and shipping freight markets

ABSTRACT This study firstly explores dynamic volatility spillovers across bunker fuel markets in shipping industry. Volatilities in bunker markets are measured by using the dynamic conditional correlation GARCH model. And then bunker volatility spillovers across markets are studied. Our analysis provides an evidence of unidirectional volatility spillovers within Asian (European/American) region and across regions, and also documents that Singapore bunker market is a leading market in transmitting volatility within Asian region and across regions. Furthermore, we measure time-varying volatility spillover effects among Singapore bunker market and shipping freight markets, and between Singapore bunker spot and futures market. The results reveal information transmission and could assist market participants and stakeholders to adjust hedging strategies and minimize risks according to the interrelationships across markets.

Volatility Spillover Dynamics between Large-, Mid-, and Small-Cap Stocks in the Time-Frequency Domain: Implications for Portfolio Management

The connectedness dynamics between large-, mid-, and small-cap stocks is investigated using the forecasted error variance decomposition (FEVD) spillover framework of Diebold and Yilmaz in the time-frequency domain. Total volatility spillover (i.e., connectedness) is elevated between large-, mid-, and small-cap stocks during the study period. This high level of spillover exists in the short run only, and declines gradually in the medium to long run, thus providing opportunities for portfolio diversification (hedging) in multi-cap investing during the medium-to-long run (short run) only. Like total connectedness, a similar pattern of bilateral connectedness is observed between either of the two indices, thus providing a similar opportunity in the short and long runs. The mid-cap index emerges as the major contributor to total volatility in the system, followed by the small- and large-cap indices, during the analyzed period. The volatility spillover is time-varying in both the time and frequency domains.

Volatility spillovers between oil and equity markets and portfolio risk implications in the US and vulnerable EU countries

This paper examines the frequency dynamics of volatility spillovers between Brent crude oil and stock markets in the US (S&P500 index), Europe (STOXX600 index), Asia (Dow Jones Asia index) and stock markets of five vulnerable European Union (EU) countries known as the GIPSI (Greece, Ireland, Portugal, Spain, and Italy). We use the methodologies developed by Diebold and Yilmaz (2012) and Baruník and Křehlík (2018) and the in-sample VaR performance to achieve our objectives. The results show that the spillover effect between the oil and the considered stock markets is time-varying, crisis-sensitive, and frequency-dependent. In addition, this effect had intensified during the onset of the 2008–2009 Global Financial Crisis, the plunge of oil prices that started in the mid-2014, and the occurrence of COVID-19. Furthermore, the results also demonstrate that oil is a net receiver of risk in the system, irrespective of the time horizon. Among the GIPSI markets, the larger and highly indebted EU economies of Italy and Spain are net contributors of risk, while the smaller EU economies of Greece, Ireland, and Portugal as well as Asia are net receivers of risk, also regardless of the time horizons. The US and European stock markets are net contributors of the spillovers, whereas the Asia equity index is a net receiver, irrespective of the frequencies. Finally, the AR(1)-FIGARCH model with the skewed Student-t distribution provides more accurate in-sample estimates in all markets. The hedging is more expensive in the long term than in the short term. The hedging is also more expensive during the ESDC period than the other turbulent periods considered in the study. The hedging effectiveness is also higher in the long term than in the short term, is crisis-sensitive, and depends on the time-investment horizon factor.

Dynamic Co-movement and Volatility Spillover Effect Between Sukuk and Conventional Bonds:A Comparison Study Between ASEAN and GCC

Sukuk and conventional bonds gain their popularity in the global market. Hence, an observation of the dynamic correlation and transmission of volatility between these two instruments is relevant. This article investigates the volatility spillover of sukuk and conventional bond markets by using GARCH-BEKK model. Then, we measure the dynamics of the co-movement of both markets by using GARCH-DCC model, and finally, we examine the macroeconomic factors that determine the dynamic conditional correlation between sukuk and conventional bonds in two Association of Southeast Asian Nations (ASEAN) markets (i.e., Indonesia and Malaysia) and four Gulf Cooperation Council (GCC) markets (i.e., Kingdom of Saudi Arabia, UAE, Qatar and Oman). The data reveal unidirectional and bidirectional volatility spillovers of sukuk and bond indices. The results also show strong evidence of dynamic conditional correlation for all markets. On the basis of the BEKK and dynamic conditional correlation (DCC) results, we infer that bonds and sukuk in ASEAN and GCC markets show the efficiency of the markets, which do not offer any diversification benefits to investors for having both instruments in their portfolios. As regards portfolio diversification strategies, investors must pay attention to the co-movements and spillover of both markets accordingly. Finally, only Oman market is influenced by all macroeconomic variables.

Analyzing the frequency dynamics of volatility spillovers across precious and industrial metal markets

AbstractThis paper investigates the volatility spillovers across precious and industrial metal markets over the period 1993–2019 based on the DY and ​BK methods. Results are summarized as follows: (1) while volatility spillovers across industrial metals are higher than across precious metals, the opposite occurs during crisis periods where precious metals cause net volatility spillovers to industrial metals; (2) volatility spillovers of the two metal groups show different dynamics in the short‐, medium‐ and long‐term components, especially in the short‐ and medium‐term components.

Dynamic volatility spillovers across oil and natural gas futures markets based on a time-varying spillover method

This paper analyzes dynamic volatility spillovers between four major energy commodities (i.e., crude oil, gasoline, heating oil and natural gas) in the oil-natural gas future markets. We construct a time-varying spillover method by combining the TVP-VAR-SV model and the spillover method of Diebold and Yilmaz (2009, 2012, 2014). We use the spillover method to obtain time-varying total, directional and pairwise volatility spillover indices. Our results summarize as follows: (1) The volatility spillover indices present peaks and troughs during some periods, such as shale gas revolution, financial crisis, and oil price crash; (2) After the U.S. shale gas revolution, the size of volatility spillover from natural gas future market has reduced sharply, but volatility doesn't decouple from the other three oil future markets; (3) The directional spillover is asymmetric. The crude oil and heating oil futures market are main net transmitter of volatility risk information, while the gasoline and natural gas futures markets are the net receiver; (4) For natural gas future market, the pairwise volatility spillover from crude oil future market has the most significant influence.

Dynamic volatility spillovers and investment strategies between the Chinese stock market and commodity markets

Building on the increased interest in the volatility spillover effects between Chinese stock market and commodity markets, this paper investigates the dynamic volatility spillovers of Chinese stock market and Chinese commodity markets based on the volatility spillover index under the framework of TVP-VAR. The result shows that there is a highly dependent relationship between the stock market and commodity markets. On average, the Chinese stock market is the net recipient of spillover, non-ferrous metals and chemical industry have a very obvious spillover impact on the stock market. The degree of total volatility spillover is different in different periods. After major crisis events, the volatility correlation between markets increases. Since the outbreak of COVID-19, the spillover effect of the stock market on the commodity market has been significantly enhanced. Then optimal portfolio weights and hedge ratios are calculated for portfolio diversification and risk management. The result shows that the ability of most commodities to hedge against risks is significantly reduced when the crisis occurs; NMFI (precious metals) and CRFI (grain) still have good hedging ability after the crisis, but the effectiveness of hedging risk is relatively low. Besides, the combination of CRFI and SHCI (the Shanghai composite index) is the most effective for risk reduction.

Dynamic volatility spillover and network connectedness across ASX sector markets

This study measures dynamic volatility spillovers and identifies the connectedness network across 11 Australian Securities Exchange (ASX) sector indices using the spillover index methodology of Diebold and Yilmaz (J Econ 182:119–134, 2014). Additionally, we visualize volatility connectedness relationships as links within a complex network to capture the propagation path of volatility connectedness across the 11 ASX sectors. Our results indicate that recent financial crises intensified the degree of volatility connectedness across the 11 ASX sectors, supporting the contagion hypothesis. Importantly, the financial sector is the main transmitter of volatility connectedness across the 11 ASX sector markets.

Volatility spillovers between WTI and Brent spot crude oil prices: an analysis of granger causality in variance patterns over time

There has been an increase in price volatility in oil prices during and since the global financial crisis (GFC). This study investigates the Granger causality patterns in volatility spillovers between West Texas International (WTI) and Brent crude oil spot prices using daily data. We use Hafner and Herwartz’s (2006) test and employ a rolling sample approach to investigate the changes in the dynamics of volatility spillovers between WTI and Brent oil prices over time. Volatility spillovers from Brent to WTI prices are found to be more pronounced at the beginning of the analysis period, around the GFC, and more recently in 2020. Between 2015 and 2019, the direction of volatility spillovers runs unidirectionally from WTI to Brent oil prices. In 2020, however, a Granger-causal feedback relation between the volatility of WTI and Brent crude oil prices is again detected. This is due to the uncertainty surrounding how the COVID-19 pandemic will evolve and how long the economies and financial markets will be affected. In this uncertain environment, commodities markets participants could be reacting to prices and volatility signals on both WTI and Brent, leading to the detection of a feedback relation.

Dynamic Volatility Spillover Among Chinese Black Series Futures Under Structural Breaks

This paper explores the dynamic volatility spillovers among five major futures in China, including rebar, hot rolled coils, iron ore, cooking coal and coke. We employ Dynamic Conditional Correlation (DCC) GARCH model to examine the volatility spillover effects among the markets with considering of structural breaks in variance. What’s more, in this study we use modified Iterated Cumulative Sum of Squares (ICSS) algorithm to detect the structural breaks. The empirical results show there are strong correlations across black series futures market. Especially, the relation between rebar and hot rolled coils, coke and cooking coal are more closely than other pairs. This research provides the insight to the information transmission in black series futures market which are meaningful to market participants make hedging and trading strategies.

The volatility spillover effect of the European Union (EU) carbon financial market

This paper modifies the BEKK-GARCH model based on the empirical results of the VAR model to analyze the dynamic volatility spillover effect between the European Union allowance (EUA) and certified emissions reduction (CER) markets during the second and third phases of the European Union Emission Trading System (EU ETS). The empirical results show that (1) an asymmetric volatility spillover effect exists between the EUA and CER markets and that the EUA market has a more significant volatility spillover effect on the CER market, and (2) the volatility spillover effect between the EUA and CER becomes weaker in phase III since the European Commission has limited the substitution of CER for EUA more strictly and the global carbon reduction requirements have become less demanding. Our study can help investors and managers of carbon market to have a more comprehensive understanding of the information and risk transmission mechanism between the EUA and CER markets, thus, providing them with a basis to make investment decisions and formulate policies.

Risk Dynamics of Sectoral Stocks in BRICS Countries

This study attempts to contribute to the literature on risk exposures by investigating the dynamic volatility spillover transmissions and volatility co-movements between oil-risk factors and sectoral stocks in BRICS countries. A spillover index and DCC-GARCH estimation techniques are applied to identify the volatility transmission mechanism and co-movement among the series using daily data from 5th May 2007 to December 31st, 2016. To provide practical implications of the volatility transmissions, the estimated results are in turn used to compute and analyze the optimal weights and hedge ratios for oil-stock portfolio holdings. Our findings indicate the existence of significant volatility spillover interdependences and a time-varying volatility co-movement between oil-risk factors and sectoral stocks. However, the direction of spillover is shown to be somewhat unidirectional mainly from some selected sectors to oil-risk factors. Thus, we show that although volatility spillovers from oil-risk factors to sectors exist, the effect is at best, marginal. Finally, the optimal weights and hedge ratios show that oil-risk factors would be better suited as instruments for portfolio diversification rather than hedging to minimize oil price and portfolio risks which is important for risk management and diversification benefits.

Dynamic Spillover and Hedging among Carbon, Biofuel and Oil

In recent years, there has been growing interest in the market interactions between carbon (or clean/renewable energy) and traditional fossil energy such as coal and oil, but few studies have discussed their dynamic volatility spillover and time-varying correlation. To investigate these issues, we used the weekly data of the European Union carbon emission allowance (EUA) futures, biofuel and Brent oil prices from 25 October 2009 to 5 July 2020. We employed the vector autoregressive-generalized autoregressive conditional heteroscedasticity (VAR-GARCH) model with the Baba, Engle, Kraft and Krone (BEKK) specification. Our main findings are summarized as follows: First, we identified the sudden changes and the volatility persistence in the EUA, biofuel, and Brent oil markets, and also confirmed that the volatility of the markets has changed significantly over time. Second, we found a weak volatility spillover effect among the three markets, and a strong spillover effect between the EUA and Brent oil markets. In particular, the effect of volatility spillover from the Brent oil market to the EUA market was the strongest than the other cases. Lastly, in financial market, by holding the EUA and energy sources together as assets, investors can effectively hedge their investment risk. The possibility of hedging is more pronounced between the EUA and biofuel markets.

Frequency dynamics of volatility spillovers among crude oil and international stock markets: The role of the interest rate

This paper explores the frequency dynamics of volatility spillovers among crude oil and international stock markets using implied volatility indices. I find evidence of volatility spillovers driven mainly by short-term spillovers. Moreover, low interest rate is the primary driver of volatility spillovers, whose roles mainly stem from its impact on short-term spillovers. The impact of interest rate on long-term spillovers is significantly positive, but relatively limited. The findings highlight that although the low interest rate offers a anticipation of the stability of financial system in the long run, it can be a source of global system risk, especially in the short run. • Volatility spillover is driven mainly by short-term spillovers. • Low interest rate is the primary driver of volatility spillovers • Its role mainly stems from the impact on short-term spillovers. • Its role in long-term spillovers is positive, but limited.

The dynamics of volatility spillovers between oil prices and stock market returns at the sector level and hedging strategies: evidence from Pakistan.

This study investigates the transmission of volatility between OPEC-oil and sector stock returns in Pakistan. The issue of volatility spillovers across the oil and sector stocks is a crucial part of risk management and portfolio designs, as all firms are not expecting to be equally affected by changes in oil price. Empirically, we estimate a bivariate VAR-GARCH model using daily data sampled from January 1, 2003 to December 29, 2017. We also analyze the optimal weights and hedge ratios for oil-stock portfolio holdings based on our model results. Our findings reveal that negative and significant spillover effects from the oil market to agriculture, energy, and machinery sector stocks are present. However, our findings show that volatility spillover effects are insignificant from stock returns to oil. The findings of the study illustrate that development of stock market will motivate highly polluting firms to invest more in renewable and clean energy, which will help reduce carbon emissions.

International stock markets Integration and dynamics of volatility spillover between the USA and South Asian markets: evidence from Global financial crisis

PurposeThe purpose of this paper is to investigate the cointegration and volatility spillover dynamics between the USA and South Asian stock markets, namely, India, Pakistan and Sri Lanka. The main objective of this study is to provide the knowledge about integration of financial market and volatility spillovers before, during and after global financial crisis to investors, fund managers and policy-makers.Design/methodology/approachThe Johansen and Juselius cointegration test, Granger Causality test and bivaraite EGARCH model have been applied in this study to examine integration and volatility spillovers between selected stock markets.FindingsThe findings show that long-term integration between the USA market and South Asian emerging stock markets. It is found that USA stock market has causal relationship with emerging stock markets in short-term. The findings of EGARCH model reveal that asymmetric volatility spillover effects significant in all selected stock markets in pre, during and post-crisis periods. Furthermore, significant volatility spillover is found from stock markets of USA to all selected South Asian markets during and post-crisis periods. However, volatility spillovers from USA to India and Sri-Lanka markets are significant, while insignificant in case of Pakistani market in pre-crisis period. Overall, we find that returns and volatility spillover effects are higher in financial crisis period as compared to non-financial crisis period.Practical implicationsThe findings of this paper have important implications for investors, portfolio managers and policy-makers. They can take potential benefits from international portfolio diversification by considering all these facts. The understanding and knowledge of across volatility transmission help them to maximize the gains from diversification and minimize the risk. Policy-makers can develop such strategies which protect the markets of these economies from future financial crisis.Originality/valueAlthough in finance literature numerous studies have been conducted on integration between different stock markets, most of the studies investigated the integration and volatility spillovers between developed stock markets. However, many studies also analyzed the integration among emerging stock markets in literature review but it is hard to find studies in the context of South Asian stock markets on the effect of global financial crisis on stock markets. The main contribution of this study is to investigate the stock markets integration and volatility transmission between the USA and South Asia by considering the effect of recent 2007 US subprime financial crisis.

Dynamic behaviors and contributing factors of volatility spillovers across G7 stock markets

This paper investigates the evolutions and determinants of volatility spillover dynamics in G7 stock markets in a time-frequency framework. We decompose volatility spillovers into short-, medium-, and long-term components, using a spectral representation of variance decompositions. The impacts of hypothesized factors on the decomposed volatility spillovers are also examined, using a linear regression model and fixed effects panel model. We find that the volatility spillovers across G7 stock markets are crisis-sensitive and are, in fact, closer to a memory-less process. The low-frequency components are the main contributors to the volatility spillovers; the high-frequency components are very sensitive to market event shocks. Moreover, our results reveal that the contributing factors have different effects on short-, medium-, and long-term volatility spillovers. There is no systematic pattern of the impacts of the contributing factors on volatility spillovers. However, whether the country is the transmitter or recipient of volatility spillovers could be a potential reason.