Cross-market Volatility Spillovers Research Articles

Does economic policy uncertainty drive volatility spillovers in electricity markets: Time and frequency evidence

Although the close relationship between economic conditions and electricity price variations has been well documented in previous studies, the issue of how volatility spillovers in electricity markets respond to the uncertainty of economic circumstances remains unclear. Using a novel dataset of 12 European electricity markets from 2009 to 2020, this paper applies recently developed volatility spillover approaches to quantify the time-frequency volatility connectedness of European electricity markets for the first time and, on this basis, investigates the impact of economic policy uncertainty on the cross-market volatility spillover effects. Our first key finding is that the overall connectedness of the European electricity markets is relatively high, but it varies over time and exhibits drastic changes during economic and geopolitical events. Second, volatility spillovers at different frequencies show strong heterogeneity, with the short-term effects accounting for the largest proportion of cross-market volatility spillovers in the European electricity system. Finally, increases in economic policy uncertainty in Europe significantly drive up volatility spillovers in the electricity markets but this effect holds mainly for the medium- and long-term effects, while the short-term spillover effects are almost independent of European economic policy uncertainty. Our results have important risk management implications for participants and regulators in European electricity markets.

How does news flow affect cross-market volatility spillovers? Evidence from China’s stock index futures and spot markets

This paper examines how news flow affects cross-market volatility spillovers and price discovery process in China’s stock market and index futures market. We find robust evidence confirming dominant predicting power of the stock market in the price discovery process, and presence of asymmetric and persistent volatility effects. The results show that volatility spillovers are bidirectional between stock index futures and spot prices, and news release has significant and positive association with the dynamic conditional correlation between the index spot and the index futures markets. These have important implications for effective hedging and portfolio management decision in emerging markets.

A Bibliometric Review of Volatility Spillovers in Financial Markets: Knowledge Bases and Research Fronts

ABSTRACT This paper uses the bibliometric method of knowledge mapping analysis to clearly present the knowledge base and research fronts of cross-market volatility spillovers. The results provide strong evidence that, first, the general theme of volatility spillovers can be divided into a variety of research topics, four of which are on the dynamics of volatility spillovers in world financial markets of various types based on multivariate GARCH or VAR models and construct a crucial knowledge base for this field; second, three research fronts can be identified using burst analysis, and they focus on examining spillover directions and magnitudes, testing volatility spillovers related to oil markets and international risk transmission mechanism of emerging markets; and, third, the major contributing scholars come from institutions in the United States,China and European economies. Our conclusions offer some recommendations for market practitioners in their risk management and policy-making.

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.

Volatility spillover dynamics and relationship across G7 financial markets

This paper examines conditional volatility spillovers among five major asset classes (public real estate, stock, bond, money and currency) domestically and internationally in G7 countries from January 1997 to December 2013, utilizing the generalized spillover framework of Diebold and Yilmaz (2012). Results indicate that the extent of cross-asset volatility spillovers within each G7 country is low. Volatility persistence is dominant in all domestic asset markets. The main contributor to the total volatility spillovers is the general equity portfolio. Evidence of co-integration among the volatility spillover cycles implies the presence of unobserved common shocks. Finally, the co-movements between the spillover cycles of domestic business cycle fluctuations and financial/asset market return volatility cycles are correlated, but in different manner according to the frequencies considered. These empirical findings provide fresh insights in cross-asset and cross-market volatility spillovers for central bank policy makers, as well as risk management and portfolio diversification for international investors.

Volatility spillovers in the European bank CDS market

From the 2007 subprime crisis to the recent Eurozone debt crisis, the banking industry has experienced terrible financial instability with increasing volatility levels of bank default probability. Using European CDS spreads data from January 2006 to March 2013, this paper sheds light on the impact of three recent significant events of credit risk volatility transmission between, firstly, Eurozone and non-Eurozone banks, and then between distressed peripheral and core countries inside the Eurozone. We employ an asymmetric multivariate BEKK model to measure cross-market volatility spillovers. We find that both recent crises are distinct episodes. The global financial crisis that originated outside Europe is characterized by unidirectional volatility spillovers in credit risk from inside to outside the Eurozone. By contrast, the Eurozone debt crisis is revealed to be local in nature with the euro as the key element, suggesting a financial market fragmentation within the Eurozone between distressed peripheral and non-distressed core Eurozone countries, whereas retaining the local currency has acted as a firewall.

The dynamics of return co-movements and volatility spillover effects in Greater China public property markets and international linkages

Purpose– The purpose of this paper is to examine weekly dynamic conditional correlations (DCC) and vector autoregressive (VAR)-based volatility spillover effects within the three Greater China (GC) public property markets, as well as across the GC property markets, three Asian emerging markets and two developed markets of the USA and Japan over the period from January 1999 through December 2013.Design/methodology/approach– First, the author employ the DCC methodology proposed by Engle (2002) to examine the time-varying nature in return co-movements among the public property markets. Second, the author appeal to the generalized VAR methodology, variance decomposition and the generalized spillover index of Diebold and Yilmaz (2012) to investigate the volatility spillover effects across the real estate markets. Finally, the spillover framework is able to combine with recent developments in time series econometrics to provide a comprehensive analysis of the dynamic volatility co-movements regionally and globally. The author also examine whether there are volatility spillover regimes, as well as explore the relationship between the volatility spillover cycles and the correlation spillover cycles.Findings– Results indicate moderate return co-movements and volatility spillover effects within and across the GC region. Cross-market volatility spillovers are bidirectional with the highest spillovers occur during the global financial crisis (GFC) period. Comparatively, the Chinese public property market's volatility is more exogenous and less influenced by other markets. The volatility spillover effects are subject to regime switching with two structural breaks detected for the five sub-groups of markets examined. There is evidence of significant dependence between the volatility spillover cycles across stock and public real estate, due to the presence of unobserved common shocks.Research limitations/implications– Because international investors incorporate into their portfolio allocation not only the long-term price relationship but also the short-term market volatility interaction and return correlation structure, the results of this study can shed more light on the extent to which investors can benefit from regional and international diversification in the long run and short-term within and across the GC securitized property sector, with Asian emerging market and global developed markets of Japan and USA. Although it is beyond the scope of this paper, it would be interesting to examine how the two co-movement measures (volatility spillovers and correlation spillovers) can be combined in optimal covariance forecasting in global investing that includes stock and public real estate markets.Originality/value– This is one of very few papers that comprehensively analyze the dynamic return correlations and conditional volatility spillover effects among the three GC public property markets, as well as with their selected emerging and developed partners over the last decade and during the GFC period, which is the main contribution of the study. The specific contribution is to characterize and measure cross-public real estate market volatility transmission in asset pricing through estimates of several conditional “volatility spillover” indices. In this case, a volatility spillover index is defined as share of total return variability in one public real estate market attributable to volatility surprises in another public real estate market.

Asymmetric cross-market volatility spillovers: evidence from Indian equity and foreign exchange markets

In order to capture the volatility and asymmetric transformation effect in Indian stock and foreign exchange market, two exchange rates [US Dollar (USD) and Euro] were tested with stock index (SENSEX). The study focused on the 2008 US subprime financial crisis period and analyzed data for the two sub periods like pre subprime and post subprime crisis period. The whole of the study period covers from 2nd April 2004 to 30th March 2012 with total of 1,930 daily observations. Descriptive statistics were drawn from the data series to know the nature of the data. All return series were found to be stationary at level. To proceed, for examining the volatility spillover between the variables generalized autoregressive conditional heteroskedasticity (GARCH) and exponential GARCH (EGARCH) models was used. The benefit of using EGARCH model is that, it is able to capture the asymmetric nature or the impact of good and bad news separately. The inferences drawn by applying these models in the two sub periods revealed that post crisis period comprised highest bidirectional volatility and asymmetric spillover effect between these two markets compared to pre-crisis period. The study observed that unlike volatility in Euro currency, volatility in Indian stock market was more caused by volatility in USD. Likewise post crisis period is pronouncing more volatility compared to pre-crisis period. This kind of research is of higher relevance in present day context because it allows the players of the financial markets to hedge their risk accordingly.

Exchange return co-movements and volatility spillovers before and after the introduction of euro

This paper examines return co-movements and volatility spillovers between major exchange rates before and after the introduction of euro. Dynamic correlations and VAR-based spillover index results suggest significant return co-movements and volatility spillovers, however, their extend is, on average, lower in the post-euro period. Co-movements and spillovers are positively associated with extreme episodes and US dollar appreciations. The euro (Deutsche mark) is the dominant net transmitter of volatility, while the British pound the dominant net receiver of volatility in both periods. Nevertheless, cross-market volatility spillovers are bidirectional, and the highest spillovers occur between European markets.

Volatility spillovers between oil prices and stock sector returns: Implications for portfolio management

In this article we take a recent generalized VAR-GARCH approach to examine the extent of volatility transmission between oil and stock markets in Europe and the United States at the sector-level. The empirical model is advantageous in that it typically allows simultaneous shock transmission in the conditional returns and volatilities. Insofar as volatility transmission across oil and stock sector markets is a crucial element for portfolio designs and risk management, we also analyze the optimal weights and hedge ratios for oil-stock portfolio holdings with respect to the results. Our findings point to the existence of significant volatility spillover between oil and sector stock returns. However, the spillover is usually unidirectional from oil markets to stock markets in Europe, but bidirectional in the United States. Our back-testing procedures, finally, suggest that taking the cross-market volatility spillovers estimated from the VAR-GARCH models often leads to diversification benefits and hedging effectiveness better than those of commonly used multivariate volatility models such as the CCC-GARCH of Bollerslev (1990), the diagonal BEKK-GARCH of Engle and Kroner (1995) and the DCC-GARCH of Engle (2002).

Better to give than to receive: Predictive directional measurement of volatility spillovers

Abstract Using a generalized vector autoregressive framework in which forecast-error variance decompositions are invariant to the variable ordering, we propose measures of both the total and directional volatility spillovers. We use our methods to characterize daily volatility spillovers across US stock, bond, foreign exchange and commodities markets, from January 1999 to January 2010. We show that despite significant volatility fluctuations in all four markets during the sample, cross-market volatility spillovers were quite limited until the global financial crisis, which began in 2007. As the crisis intensified, so too did the volatility spillovers, with particularly important spillovers from the stock market to other markets taking place after the collapse of the Lehman Brothers in September 2008.

Asset prices and output growth volatility: the effects of financial crises

In this paper we investigate the real effects of financial crises. Empirical evidence on the consequences of the 1997 East Asian crisis for the casual relationship between stock prices and output growth volatility is provided. The effects of the crisis on cross-market volatility spillovers have been taken into account by including a dummy variable in the conditional variance specification.