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The Impact of Trade Intensity and Market Characteristics on Asymmetric Volatility, Spillovers and Asymmetric Spillovers: Evidence from the Response of International Stock Markets to US Shocks
Abstract
This study examines the relationship between trade intensity and three stock market phenomena: asymmetric volatility, spillovers and asymmetric spillovers between the US and 74 international stock markets. The evidence is provided based on the cross-market models using the various measures from multivariate volatility models and the spillover indices. As stock trading gets more intensive, the market volatility responds proportionally stronger to a negative domestic shock than a positive one. Trade intensity also increases the spillovers of US shocks to the local markets. However, its impact diminishes at a higher level. Regarding other market characteristics, geographical distance, representing transaction costs or psychological /cultural obstacles, has a significant negative association with spillovers and the asymmetric spillovers. Less negative skewness, as stronger short-selling constraints, is associated with weaker spillovers. More volatile markets are generally linked to stronger asymmetries and spillovers.
... Specifically, researchers have sought to explore whether significant crises in specific markets, such as the Asian crisis, GFC, European debt crisis, and the COVID-19 pandemics, have triggered fluctuations in share prices across international financial markets. Several investigations, including those conducted by Wang et al. (2005), Jondeau and Rockinger (2006), Iqbal et al. (2012), Li and Giles (2015), Mokni and Mansouri (2017), Caloia et al. (2018), Pereira (2018), and Newaz (2019), have highlighted that these crises have amplified the interconnectedness among markets. A significant portion of these studies has primarily concentrated on the interdependence among equity markets, especially in the aftermath of major occurrences such as the 1987 stock market crash, the 1997 Asian crisis, and the GFC. ...
This study investigates the volatility spillover between emerging South Asian equity markets and developed U.S. market. Specifically, the study examines the entire sample period and three sub-periods: pre-crisis, crisis, and post-Global Financial Crisis (GFC) periods in the emerging markets of Pakistan, India, and Sri Lanka, as well as the U.S. as developed stock market. The study analyses both unidirectional and bidirectional shock and volatility spillover among the markets to determine the direction of risk transmissions. The analysis utilizes a multivariate GARCH-BEKK model. The results indicate that volatility transmissions increased due to the GFC. Furthermore, the South Asian emerging markets exhibit shock and volatility spillover during the crisis period compared to the pre-and post-crisis periods. Additionally, the patterns of risk transmission were time-varying. These findings support the notion that market volatility increases during turbulent events, such as the Global Financial Crisis. The implications of these results are important for policymakers, investors, and researchers interested in international diversification and market efficiency within the studied markets especially during turbulent periods. Keywords Volatility spillover, M-GARCH-BEKK, South Asia, Emerging Markets, GFC JEL Classification G01, G11, G15
... The downside realized semi variance indicates that the downward movements in stock prices have a higher correlation than positive or upside realized semi variance. A detailed analysis of the volatility spillover between the U.S. and 74 international stock markets shows that market volatility and associated asymmetric spillover to adverse domestic shocks increase as stock trading becomes more intensive than positive shocks (Newaz and Park 2019). Extending the framework of Diebold and Yilmaz (2012), Su (2020) to analyze the spillover from the G7 stock markets to BRICS and finds that the U.S., Germany, France and Canada are net transmitters of risk while the U.K., Japan, Italy and BRICS are net receivers. ...
The primary objective of this paper is to analyze the volatility dynamics and spillover, symmetric and asymmetric, among BRICS countries' stock markets. The paper employed dynamic conditional correlation and asymmetric generalized dynamic conditional correlation models to examine the bidirectional volatility spillover. The study preferred these sophisticated and flexible models as they have several advantages over other econometric models. The findings of the study indicate a long-term integration and a significant bidirectional spillover effect (both symmetric and asymmetric), suggesting a close relationship among the stock markets of BRICS countries. Consequently, diversifying one's portfolio between these markets would not yield substantial economic value for investors. Also, the study finds the same pattern of flow in asymmetric volatility spillover but at a different significance level. To the best of the authors' knowledge, this is the first study investigating the asymmetric volatility spillover effect among BRICS countries' stock indices using the DCC-MGARCH and AGDCC-MGARCH models.
... Studies find the presence of asymmetric volatility spillovers across markets. For example, the asymmetric and volatility spillover is more in the case of more volatile markets (Newaz & Park, 2019). Further, asymmetric volatility spillovers exist between Indian and developed countries' stock markets (Nandy & Chattopadhyay, 2019). ...
This study uses the MGARCH-BEKK model and Diebold–Yilmaz (DY) volatility spillover index to examine volatility spillovers among BRICS countries’ stock markets. The study finds that the own volatility spillover is more than the cross-markets and has increased during the financial crisis. In contrast, the cross-markets volatility spillovers have decreased after the financial crisis. The total net return spillover increased during the crisis period (27.30%) and the pre-crisis period (25.50%) in comparison with the post-crisis period (6.30%) and the whole sample period (10.70%). Brazil is the highest net volatility transmitter among the BRICS countries’ stock markets, and China is the highest net volatility receiver. We learned from the volatility network connectedness that China is highly connected with India regarding volatility. Foreign institutional investors may use this study’s result to find diversification opportunities across the BRICS stock markets.
JEL Codes: F3, G11, G12, G15
... 2 different angles, and put forward such as the information asymmetry theory, the financial system's own vulnerability theory and the financial institution association theory [6][7][8][9][10]. In comparison, the financial institution association theory holds that the high correlation of market subjects and the externality of financial risks will inevitably lead to risk spillover and systemic risk, which has attracted more and more attention and recognition in academic community in recent years, so the financial risk spillover effect has become the focus and hot issue of economic management research. ...
The stock risk spillovers of 31 associated enterprises of Evergrande supply chain in China were measured with DCC-GARCH and CoVaR model, and high, moderate and low risk overflow networks in four periods were constructed, finally the overall metrics and dynamic evolution of risk spillover network were explored. The results showed that: With COVID-19 under control in China, the risk spillover of Evergrande supply chain associated enterprises continues to diverge, with the quantity and scope of high risk declining and moderate and low risk rising; The infection scope of high risk spillover has narrowed, from indirect to direct infection; Evergrande subsidiaries play obvious bridge roles in moderate and low risk networks, have strong control over the risk spread; Commercial banks suffer and trigger more risk spillovers, a number of risk spillover groups with commercial banks as cores formed in high and moderate risk networks.
... Both the source of innovation and whether it is associated with good or bad news impact the behavior of volatility, which suggests that both volatility behaviors and volatility spillovers are asymmetric. Stock return volatility increases to a greater extent in the presence of negative than positive shocks of the same size (BenSaïda, 2019Newaz & Park, 2019Segal et al., 2015, with volatility spillovers similarly displaying asymmetric behaviors in response to good or bad news (Baruník et al., 2016BenSaïda, 2019. Existing studies, surprisingly, pay limited attention to the quantification of asymmetric volatility spillovers resulting from bad and good volatilities (Baruník et al., 2016, 2017Wang & Wu, 2018BenSaïda, 2019Xu, Ma, Chen, & Zhang, 2019. ...
This paper examines the asymmetric volatility connectedness amongst the Dow Jones Islamic Market Index (DJIM) and the Brent crude oil, gold, and silver markets. We use the Diebold and Yilmaz methodology to examine asymmetric volatility connectedness associated with bad (negative semi-variance) and good (positive semi-variance) volatility in these markets. We identify significant volatility connectedness between the DJIM and commodity markets, with the DJIM and Brent oil markets being the largest net contributors of spillovers. Furthermore, the evidence on semi-volatility connectedness displays asymmetric behavior. Bad volatilities are associated with net transmission of spillovers to other markets, except for silver. Our results have significant implications for investors dealing with the DJIM and commodity markets in terms of asset management and diversification.
... The issue of integration among international stock markets is the focus of a great deal of research. In particular, research in the area seeks to investigate whether turmoil which occurs in one market has consequences for share prices in other markets (Arshanapalli and Doukas 1993;Click and Plummer 2005;Wang et al. 2005;Iqbal et al. 2012;Li and Giles 2015;Baumöhl et al. 2018;BenSaïda et al. 2018;Caloia et al. 2018;Davidson 2019;Jain and Sehgal 2019;Newaz 2019). Numerous studies investigate the extent of any interdependence among equity markets especially after the October 1987 stock market crash and the 1997 Asian crisis, which affected not only US and Asian equities but also transmitted shocks to other markets throughout the world (Wang et al. 2005). ...
This paper documents evidence of changes in the co-movement of stock returns and risk transmission among four South Asian stock markets over periods of regional market reform and global market instability. The sample period (1993–2015) is disaggregated into three sub-periods: before and after the establishment of the South Asian Federation of Exchanges (SAFE) and after the 2008 Global Financial Crisis. The principal components investigation and cointegration analysis conclude that the co-movement among stock returns in this region altered amidst a change in the institutional context and global economic uncertainty. Using a tetra-variate GARCH-BEKK model, we find that, after the establishment of SAFE, the interactions among the markets increased through volatility spillovers, but decreased through shock spillovers. In addition, there were more shock and volatility spillovers in the last sub-period as compared to the first two sub-periods, indicating that risk transmission across countries increased during the period of uncertainty. In particular, the Indian stock market was a risk spreader in South Asia after the setup of SAFE and its influence on the regional stock markets increased even further after the 2008 Global Financial Crisis.
... In view of the asymmetric impact, many extension models have been put forward, e.g., Ding et al.'s [28] APARCH (asymmetric power ARCH) model. Since then, the GARCH model with asymmetric items has been widely used in the following studies of stock markets' volatility [23,[29][30][31]. Mensi et al. [23] employed the bivariate APARCH model to capture volatility spillover effects between the U.S. and BRICS stock markets. ...
Financial internationalization leads to similar fluctuations and spillover effects in financial markets around the world, resulting in cross-border financial risks. This study examines comovements across G20 international stock markets while considering the volatility similarity and spillover effects. We provide a new approach using an ICA- (independent component analysis-) based ARMA-APARCH-M model to shed light on whether there are spillover effects among G20 stock markets with similar dynamics. Specifically, we first identify which G20 stock markets have similar volatility features using a fuzzy C-means time series clustering method and then investigate the dominant source of volatility spillovers using the ICA-based ARMA-APARCH-M model. The evidence has shown that the ICA method can more accurately capture market comovements with nonnormal distributions of the financial time series data by transforming the multivariate time series into statistically independent components (ICs). Our findings indicate that the G20 stock markets are clustered into three categories according to volatility similarity. There are spillover effects in stock market comovements of each group and the dominant source can be identified. This study has important implications for investors in international financial markets and for policymakers in G20 countries.
... By combining the DY index and the realized semi-variances introduced by Baruník et al. (2017), Xu et al. (2019) utilize the asymmetric generalized dynamic conditional correlation (AG-DCC) and MS-VAR model to examine the dynamic asymmetric volatility spillover between oil and J o u r n a l P r e -p r o o f 6 US/Chinese stock markets during the period of 2007 to 2016. Moreover, some studies investigate the good or bad volatility spillover among various financial assets by using different kinds of multivariate dynamic conditional correlation generalized autoregressive conditionally heteroskedastic (DCC-GARCH) methods such as Tsuji (2018) and Newaz and Park (2019). ...
This study examines volatility spillover dynamics among the S&P 500 index, the US 10-year Treasury yield, the US dollar index futures, and the commodity price index. The focus of the study is to analyze the effects of the Fed’s unconventional monetary policy on the US financial markets. We use realized volatility measures based on daily data covering the period from December 29, 1996, to November 12, 2018. To address nonlinear and asymmetric spillover dynamics in low and high volatility states, we propose a new regime-dependent spillover index based on a smooth transition vector autoregressive (STVAR) model, extending the study of Diebold and Yilmaz (2008, 2012) to regime switching models. When applied to US financial data, we find strong evidence that the US financial market risk structure changes after the 2008 Global Financial Crisis and announcement of quantitative easing (QE) programmes through the portfolio balance channel. The risk spillover moves from purchased assets to non-purchased assets after the QE announcements.
... There is a large literature on the interdependence of financial markets (e.g. Gulzar, Kayani, Xiaofeng, Ayub, & Rafique, 2019;Marfatia, 2017;Newaz & Park, 2019), but many of these studies, however, analysed mostly West European markets (Boubaker & Raza, 2016;Burzala, 2016;Nikkinen, Piljak, & Rothovius, 2019). The motivation for choosing these emerging economies is that their financial markets are fully consistent with the world's major financial markets, with strong chances of interdependence and contagion effects on these Eastern European economies. ...
This article revisits the relationship between the contagion and co-movement of 10 Central and Eastern European (C.E.E.) financial markets in relation to two major Western European capital markets using wavelet-based methodology. Based upon an A.R.D.L. panel model we found that foreign monetary policy, national exchange rate and economic cycle play a key role in both short- and long-term co-movement between capital markets. While a stable economic environment coupled with a strong national currency can reduce the degree of short-term co-movement between capital markets, changes in foreign monetary policy could increase the effect of external shocks. Furthermore, we find that inflation, foreign exchange rate and foreign economic cycle play an important role after longer periods. By ensuring a stable economic environment national authorities can help mitigate the effects of external shocks on national capital markets.
... By combining the DY index and the realized semi-variances introduced by Baruník, Kočenda, and Vácha (2017);Xu, Ma, Chen, and Zhang (2019) utilize the asymmetric generalized dynamic conditional correlation (AG-DCC) and MS-VAR model to examine the dynamic asymmetric volatility spillover between oil and US/Chinese stock markets during the period of 2007-2016. Moreover, some studies investigate the good or bad volatility spillover among various financial assets by using different kinds of multivariate dynamic conditional correlation generalized autoregressive conditionally heteroskedastic (DCC-GARCH) methods such as Tsuji (2018) and Newaz and Park (2019). ...
This study examines volatility spillover dynamics among the S&P 500 index, the US 10-year Treasury yield, the US dollar index futures and the commodity price index. The focus of the study is to analyze effects of Fed’s unconventional monetary policy on the US financial markets. We use realized volatility measures based on daily data covering the period from December 29, 1996 to November 12, 2018. To address nonlinear and asymmetric spillover dynamics in low and high volatility states, we propose a new regime-dependent spillover index based on a smooth transition vector autoregressive (STVAR) model, extending the study of Diebold and Yilmaz (2009,2012) to regime switching models. When applied to US financial data, we find strong evidence that the US financial market risk structure changes after the announcement of quantitively easing (QE) through the portfolio balance channel. The risk spillover moves from purchased assets to non-purchased assets after the QE announcements.
This paper investigates the volatility spillover and connectedness among the stock markets of G7, BRICS countries, and other countries where COVID-19 is more severe. For this investigation, we perform static and rolling-window analysis to measure volatility spillovers using the spillover index approach and LASSO-VAR for estimating high-dimensional VARs. We also examine the network connectedness at different periods. Our findings indicate that the recent COVID-19 pandemic intensifies volatility spillovers, supporting the financial contagion effects. Furthermore, the United States, Spain, and Russia markets are net volatility transmitters for most of the period before and during the COVID-19 pandemic.
This paper studies risk transmissions in six global markets: the US, Japan, Canada, Germany, the UK, and France. The paper distinguishes between the good volatility from increases in intra-day prices and the bad volatility that arises from intra-day price declines. Our results indicate persistent and uniform asymmetries in the information transmission mechanism: the bad volatility spillovers in the system are higher than the spillovers of good volatility. These pronounced asymmetries are countercyclical because they strengthen after the interest rate declines and weaken after it rises. Finally, there are clear bursts in risk transfer and asymmetries during the times of the global financial crisis and the COVID pandemic crisis. These findings have important implications for risk forecasting, asset pricing, and portfolio diversification.
Owing to the growing importance of socially responsible investments in the wake of climate change mitigation goals, we estimate the asymmetric time- and frequency-spillovers between global sustainable investments. Additionally, we examine the influence of global risk factors such as US and UK economic policy uncertainties, stock market volatility, US treasury market volatility and infectious diseases related market volatility on the short- and long-run connectedness in these investments. To this end, we use daily returns and volatilities of 14 country-level Dow Jones Sustainability indices from January 2005 to March 2021. By employing the asymmetric versions of Diebold & Yilmaz (2012, 2014) and Barunik & Krehlik (2018) time-frequency connectedness, our study addresses both good and bad contagion among sustainable investments is unexplored in the recent literature. The results reveal significant time-frequency asymmetries in return spillovers across different regions in the short- and long-run. Germany, France, Netherlands, and the UK are the primary transmitters of returns and volatility shocks. We find more intra-regional connectedness among the Asian countries as opposed to inter-regional connectedness. Negative returns propagate more intensely than positive ones, and this contagion is considerably boosted during crises, including the COVID19. The VIX and COVID19 remain influential for financial contagion in the long run. The impact of MOVE is positive in the short-run while negative in the long-run, which shows an overreaction of connectedness to the US treasury market volatility in the short-run. Economic policy uncertainties in the US and the UK increase spillovers more intensely in the short-run. These results are robust to using volatility spillovers, the choice of rolling window and various forecast horizons. Our findings are distinctly important for socially responsible investors as we point out international portfolio diversification opportunities among sustainable investments. Understanding the dynamics of connectedness in sustainable investments can potentially boost financing in this market through portfolio choices and contribute to the climate change mitigation agenda of United Nations.
This paper considers the generalized autoregressive conditional heteroscedastic approach in modelling exchange rate volatility in a panel of nineteen of the Arab countries using daily observations over the period of 1st January 2000 to 19th November 2011. The paper applies both symmetric and asymmetric models that capture most common stylized facts about exchange rate returns such as volatility clustering and leverage effect. Based on the GARCH(1,1) model, the results show that for ten out of nineteen currencies the sum of the estimated persistent coefficients exceed one, implying that volatility is an explosive process, in contrast, it is quite persistent for seven currencies, a result which is required to have a mean reverting variance process. Furthermore, the asymmetrical EGARCH (1,1) results provide evidence of leverage effect for majority of currencies, indicating that negative shocks imply a higher next period volatility than positive shocks. Finally, the paper concludes that the exchange rates volatility can be adequately modelled by the class of GARCH models.
International stock markets worldwide experienced a downturn in stock prices and
activities following the subprime mortgage crisis in the U.S. in mid-2008. This suggests that
stock prices volatility do spillover from one market to another. Thus, the purpose of this paper is
to investigate the international transmission of daily stock index volatility movements from U.S.
and U.K. to selected MENA emerging markets: Egypt, Israel, and Turkey. Employing a multivariate
GARCH in Mean technique due to Engle, the study finds that Egypt and Israel are significantly
influenced by the U.S. stock market while Turkey is not.
The "leverage effect" refers to the well-established relationship between stock returns and both implied and realized volatility: volatility increases when the stock price falls. A standard explanation ties the phenomenon to the effect a change in market valuation of a firm's equity has on the degree of leverage in its capital structure, with an increase in leverage producing an increase in stock volatility. We use both returns and directly measured leverage to examine this hypothetical explanation for the "leverage effect" as it applies to the individual stocks in the S&P100 (OEX) index, and to the index itself. We find a strong "leverage effect" associated with falling stock prices, but also numerous anomalies that call into question leverage changes as the explanation. These include the facts that the effect is much weaker or nonexistent when positive stock returns reduce leverage; it is too small with measured leverage for individual firms, but much too large for OEX implied volatilities; the volatility change associated with a given change in leverage seems to die out over a few months; and there is no apparent effect on volatility when leverage changes because of a change in outstanding debt or shares, only when stock prices change. In short, our evidence suggests that the "leverage effect" is really a "down market effect" that may have little direct connection to firm leverage.
Using a generalized vector autoregressive framework in which forecast-error variance decompositions are invariant to variable ordering, we propose measures of both total and directional volatility spillovers. We use our methods to characterize daily volatility spillovers across U.S. stock, bond, foreign exchange and commodities markets, from January 1999 through 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 that 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 Lehman Brothers in September 2008.
This paper examines the linkages of stock markets across the USA, Japan and six Asian developing countries: China, India, Indonesia, Malaysia, the Philippines and Thailand over the period 1 January 1993 to 31 December 2012. The volatility spillover is modelled through an asymmetric multivariate generalized autoregressive conditional heteroscedastic model. We find significant unidirectional shock and volatility spillovers from the US market to both the Japanese and the Asian emerging markets. It is also found that the volatility spillovers between the US market and the Asian markets are stronger and bidirectional during the Asian financial crisis. Further, during the last 5 years, the linkages between the Japanese market and the Asian emerging markets became more apparent. Our paper contributes to the literature by examining both the long‐run and the short‐run periods and focusing on shock and volatility spillovers rather than return spillovers, which have been the primary focus of most other studies. Copyright © 2014 John Wiley & Sons, Ltd.
We show how bad and good volatility propagate through the forex market, i.e., we provide evidence for asymmetric volatility connectedness on the forex market. Using high-frequency, intra-day data of the most actively traded currencies over 2007 – 2015 we document the dominating asymmetries in spillovers that are due to bad, rather than good, volatility. We also show that negative spillovers are chiefly tied to the dragging sovereign debt crisis in Europe while positive spillovers are correlated with the subprime crisis, different monetary policies among key world central banks, and developments on commodities markets. It seems that a combination of monetary and real-economy events is behind the positive asymmetries in volatility spillovers, while fiscal factors are linked with negative spillovers.
This study examines the time-varying relationship between stock returns and volatility in sixteen stock markets during January 2001 to October 2014. After estimating the volatility process without assuming any specific form of its behavior, we find the volatility to be long-term dependent with the Hurst exponent on a verge of stationarity and nonstationarity. We then apply the detrended cross-correlation coefficient to overcome this complication and find evidence of a significant and negative relationship between current stock market returns and current market volatility. Additionally, we find the strength of the negative return-volatility relation is different for specific scales and is stronger in longer time horizon. We further investigate the presence of volatility feedback and leverage effects in international stock markets by examining the lead-lag relation between stock returns and volatility and confirm that the negative return-volatility relation seems to be return-driven (the leverage effect). Finally, we examine the dynamic behavior of the return-volatility relation by applying a rolling window approach and find that time-varying negative return-volatility relation is more likely to generate an asymmetric response with a greater effect when returns decline, which is the common characteristic of international stock markets.
We investigate the asymmetric relationship between returns and implied volatility for 20 developed and emerging international markets. In particular we examine how the sign and size of return innovations affect the expectations of daily changes in volatility. Our empirical findings indicate that the conditional contemporaneous return-volatility relationship varies not only based on the sign of the expected returns but also upon their magnitude, according to recent results from the behavioral finance literature. We find evidence of an asymmetric and reverse return-volatility relationship in many advanced, Asian, Latin-American, European and South African markets. We show that the US market displays the highest reaction to price falls, Asian markets present the lowest sensitivity to volatility expectations, while the Euro area is characterized by a homogeneous response both in terms of direction and impact. These results may be safely attributed to cultural and societal characteristics. An extensive quantile regression analysis demonstrates that the detected asymmetric pattern varies particularly across the extreme distribution tails i.e., in the highest/lowest quantile ranges. Indeed, the classical feedback and leverage hypotheses appear not plausible, whilst behavioral theories emerge as the new paradigm in real-world applications.
This paper features an analysis of volatility spillover effects from Australia's major trading partners, namely, China, Japan, Korea and the United States, for a period running from 1st January 2004 to 30th June 2014. This captures the impact of the Global Financial Crisis (GFC). These markets are represented by the following major indices: The Shanghai composite and the Hangseng. (in the case of China, as both China and Hong Kong appear in Australian trade statistics), the S&P500 index, the Nikkei225 and the Kospi index. We apply the Diebold and Yilmaz (2009) Spillover Index, constructed in a VAR framework, to assess spillovers across these markets in returns and in volatilities. The analysis confirms that the US and Hong Kong markets have the greatest influence on the Australian one. We then move to a GARCH framework to apply further analysis and apply a tri-variate Cholesky-GARCH model to explore the effects from the US and Chinese market, as represented by the Hang Seng Index. We further explore three sample sub-periods, Pre-GFC 2004/1/1/ until 2007/07/08/, GFC 2007/08/09 until 2010/05/07 and Post-GFC 2010/05/10 until 2014/06/20 and analyse the behaviour of time-varying conditional correlations in these sub-periods. The GARCH analysis re-affirms the strong influence of the Hang Seng Index and the S&P500 Index.
We extend the framework of Diebold and Yilmaz (2009b) and Diebold and Yilmaz (2012) and construct volatility spillover indexes using a DCC-GARCH framework to model the multivariate relationships of volatility among assets. We compute spillover indexes directly from the series of asset returns and recognize the time-variant nature of the covariance matrix. Our approach allows for a better understanding of the movements of financial returns within a framework of volatility spillovers. We apply our method to stock market indexes of the United States and four Latin American countries. Our results show that Brazil is a net volatility transmitter for most of the sample period, while Chile, Colombia and Mexico are net receivers. The total spillover index is substantially higher between 2008Q3 and 2012Q2, and shock transmission from the United States to Latin America substantially increased around the Lehman Brothers’ episode.
This study investigates the asymmetry of the intraday return-volatility relation at different return horizons ranging from 1, 5, 10, 15, up to 60 min and compares the empirical results with results for the daily return horizon. Using data on the S&P 500 (SPX) and the VIX from September 25, 2003 to December 30, 2011 and a Quantile-Regression approach, we observe strong negative return-volatility relation over all return horizons. However, this negative relation is asymmetric in three different aspects. First, the effects of positive and negative returns on volatility are different and more pronounced for negative returns. Second, for both positive and negative returns, the effect is conditional on the distribution of volatility changes. The absolute effect is up to five times larger in the extreme tails of the distribution. Third, at the intraday level, there is evidence of both autocorrelation in volatility changes and cross-autocorrelation with returns. This lead-lag relation with returns is also very asymmetric and more pronounced in the tails of the distribution. These effects are, however, not observed at the daily return horizon.
There is large variation in the leverage effect on each weekday. In the past 15 years, the average difference between the impact of negative and positive stock return innovations on future volatility in the S&P 500 Index is 45% on Monday, 14% on Tuesday, 60% on Wednesday, 6% on Thursday, and 28% on Friday. This variation is not predicted by any prevailing hypothesis on why there is a leverage effect.
This paper discusses a novel explanation for asymmetric volatility based on the anchoring behavioral pattern. Anchoring as a heuristic bias causes investors focusing on recent price changes and price levels, which two lead to a belief in continuing trend and mean-reversion respectively. The empirical results support our theoretical explanation through an analysis of large price fluctuations in the S&P 500 and the resulting effects on implied and realized volatility. These results indicate that asymmetry (a negative relationship) between shocks and volatility in the subsequent period indeed exists. Moreover, contrary to previous research, our empirical tests also suggest that implied volatility is not simply an upward biased predictor of future deviation compensating for the variance of the volatility but rather, due to investors' systematic anchoring to losses and gains in their volatility forecasts, it is a co-integrated yet asymmetric over/under estimated financial instrument. We also provide results indicating that the medium-term implied volatility (measured by the VIX Index) is an unbiased though inefficient estimation of realized volatility, while in contrast, the short-term volatility (measured by the recently introduced VXST Index representing the 9-day implied volatility) is also unbiased and yet efficient.
This paper investigates evidence of learning among thousands of new entrants to a population of professional futures floor traders over a six-year period. We document several empirical regularities consistent with different types of learning. First, traders appear to rationally learn about ability: only about 15% of the traders survive more than one year. Second, as traders become experienced, they increase trading intensity. Third, on average, surviving traders exhibit increasing comfort with risk (tolerating larger potential losses) as they gain experience compared to traders with the same experience level who do not survive. However, despite evidence consistent with learning about ability, we find no evidence that traders improve their risk-adjusted performance as they gain experience. Other than learning about ability, we conclude that the fundamental skill traders learn is the ability to be comfortable with risk.
The standard Anderson and van Wincoop gravity model has typically been estimated using a
fixed-effects approach. However, a fixed-effects approach has a major drawback: it does not allow
for the estimation of exporter- and importer-invariant variables. Thus, at least in a cross-sectional
context, economically relevant variables such as exporter and importer gross domestic product
are disregarded. We propose a random intercept model to address this gap. For large datasets,
this approach not only provides identical estimates like a fixed-effects approach, but also allows
for the estimation of exporter- and importer-invariant variables.
In this paper, we examine how to quantify asymmetries in volatility spillovers that emerge due to bad and good volatility. Using data covering most liquid U.S. stocks in seven sectors, we provide ample evidence of the asymmetric connectedness of stocks at the disaggregate level. Moreover, the spillovers of bad and good volatility are transmitted at different magnitudes that sizably change over time in different sectors. While negative spillovers are often of substantial magnitudes, they do not strictly dominate positive spillovers. We find that the overall intra-market connectedness of U.S. stocks increased substantially during the recent financial crisis.
I test Black's leverage effect hypothesis on a panel of U.S. stocks from 1997 to 2012. I find that negative stock return innovations increase the future volatility of equity returns by about 36% more than positive ones. There is a strong and positive relation between variation in the size of these leverage effects and variation in the firm's use of debt. I uncover this relation by applying the Fama/French/Carhart 4-factor asset pricing model in the exponential generalized autoregressive conditional heteroskedasticity mean equation and by using panel data to control for firm- and time-invariant unobservables via first differences and two-way fixed effects.
This article investigates empirically how returns and volatilities of stock indices are correlated between the Tokyo and New
York markets. Using intradaily data that define daytime and overnight returns for both markets, we find that Tokyo (New York)
daytime returns are correlated with New York (Tokyo) overnight returns. We interpret this result as evidence that information
revealed during the trading hours of one market has a global impact on the returns of the other market. In order to extract
the global factor from the daytime returns of one market, we propose and estimate a signal-extraction model with GARCH processes.
Does macroeconomic uncertainty increase or decrease aggregate growth and asset prices? To address this question, we decompose aggregate uncertainty into ‘good’ and ‘bad’ volatility components, associated with positive and negative innovations to macroeconomic growth. We document that in line with our theoretical framework, these two uncertainties have opposite impact on aggregate growth and asset prices. Good uncertainty predicts an increase in future economic activity, such as consumption, output, and investment, and is positively related to valuation ratios, while bad uncertainty forecasts a decline in economic growth and depresses asset prices. Further, the market price of risk and equity beta of good uncertainty are positive, while negative for bad uncertainty. Hence, both uncertainty risks contribute positively to risk premia, and help explain the cross-section of expected returns beyond cash flow risk.
This article extends the HAR-RV model to enable it to forecast volatility by including lunch-break returns, overnight returns, trading volume and leverage effects in the Chinese stock market. The findings show the significant role of additional leverage effects, captured by negative lunch-break returns and negative overnight returns, in volatility forecasting, in addition to the trading volume’s impact. Moreover, there is a strong significance of the usual leverage effects, which turn out to be persistent even for SHCI. Surprisingly, squared lunch-break returns, measured as additional volatilities during the lunch-break period, have a large long-run impact on the volatility for SHCI but not for SZCI. This new empirical evidence is robust to alternative realized measurements and unconditional variance, and, in particular, confirms the impact of intermittent trading, captured by the returns and volatilities outside the trading hours. Overall, our model performs much better than the benchmark HAR-RV model when various forecasting horizons are considered, and our findings have important implications for investors and policy makers.
Time varying correlations are often estimated with multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models that are linear in squares and cross products of the data. A new class of multivariate models called dynamic conditional correlation models is proposed. These have the flexibility of univariate GARCH models coupled with parsimonious parametric models for the correlations. They are not linear but can often be estimated very simply with univariate or two-step methods based on the likelihood function. It is shown that they perform well in a variety of situations and provide sensible empirical results.
Higher initial margin requirements are associated with lower subsequent stock market volatility during normal and bull periods,
but show no relationship during bear periods. Higher margins are also negatively related to the conditional mean of stock
returns, apparently because they reduce systemic risk. We conclude that a prudential rule for setting margins (or other regulatory
restrictions) is to lower them in sharply declining markets in order to enhance liquidity and avoid a depyramiding effect
in stock prices, but subsequently raise them and keep them at the higher level in order to prevent a future pyramiding effect.
In this study we test for the possible contagion effects of the 10-year Greek government bond yield. We first employ the well-documented adjusted correlation coefficient of Forbes and Rigobon (2002) and then we estimate an exponential generalized autoregressive conditional heteroskedasticity model extended for volatility spillovers. Finally, we propose an extension of the corrected Dynamic Conditional Correlation (cDCC) model, which allows for structural breaks in the correlation dynamics. The suggested cDCC specification provides a natural testing framework for the correlation contagion hypothesis. Compared with other similar approaches, the proposed structural break cDCC approach allows for consistent inferences. The results do not confirm any contagious effects stemming from the 10-year Greek sovereign bond.
This paper analyzes three major asymmetries in stock markets, namely, asymmetry in
return reversals, asymmetry in return persistency and asymmetry in return volatilities. It argues
for a case of return persistency as stock returns do not always reverse, in theory and in practice.
Patterns in return-volatility asymmetries are conjectured and investigated jointly, under different
stock market conditions. Results from modeling the world’s major stock return indexes render
support to the propositions of the paper. Return reversal asymmetry is illusionary arising from
ambiguous parameter estimations and deluding interpretations of parameter signs. Asymmetry
in return persistency, still weak though, is more prevalent.
We analyze the time-varying volatility and spillover effects in crude oil, heating oil, and natural gas futures markets by incorporating changes in important macroeconomic variables and major political and weather-related events into the conditional variance equations. We allow asymmetric responses to random disturbances in each market as well as to good and bad economic news related to the overall economy. Results show the presence of asymmetric effects in both random disturbances and macroeconomic variables. A bidirectional volatility spillover effect is found between natural gas and crude oil and between the natural gas and heating oil markets. Crude oil volatility is found to increase following major political, financial, and natural events. Seasonality and day-of-the-week effects are found in the crude oil and heating oil markets.
•countries with stock markets tend to grow faster compared to countries without stock markets•countries which are relatively developed and have stock markets tend to grow less faster compared to small countries with stock markets•Stock market development has a positive effect on economic growth•Investment, human capital formation and openness positively influence economic growth in the Africa region•Macroeconomic instability (inflation) and government consumption impact economic growth negatively•Countries that are politically stable and less corrupt tend to grow faster.
This study investigates the spillover effect in five leading stock markets (i.e., the United States, the United Kingdom, Germany, Japan, and France). It estimates the spillover indices of these countries and finds that information transmission between these stock markets increases considerably after 1998. Germany and the United States are the main stock markets conveying information to other international markets. Germany primarily influences the French stock market, and the United States significantly influences many other stock markets. Results show that the US stock market shows three periods during which its net spillover effect exceeds zero: the period prior to 1997, the dot-com bubble from 2000 to 2002, and the subprime mortgage crisis and Lehman Brothers bankruptcy from 2007 to 2008. The fear index correlates significantly with the spillover of the US stock market into other markets. The spillover effect of the US stock market demonstrates asymmetry and the likelihood to spread positive fundamental information and non-fundamental information (e.g., fear).
This study examines the relationship between time-varying correlations and conditional volatility among 32 worldwide emerging and frontier stock markets and the MSCI World stock market index from January 2000 to December 2012. Correlations are estimated in the standard and asymmetric dynamic conditional correlation model frameworks. The results can be summarized by three main findings: (1) asymmetry in volatility is not a common phenomenon in emerging and frontier markets; (2) asymmetry in correlations is found only with respect to the Hungarian stock market; and (3) the relationship between volatility and correlations is positive and significant in most countries. Thus, diversification benefits decrease during periods of higher volatility.
In recent years the Chinese stock market has experienced an astonishing growth and unprecedented development, but is also viewed as one of the most volatile markets, which has been called by many observers a “casino”. This study intends to examine the presence of heteroskedasticity and the leverage effect in the Chinese stock markets, and to capture the dynamics of conditional correlation between returns of China's stock markets and those of the U.S. in a bivariate VC-MGARCH framework. The results show that the leverage effect is significant in these markets during the sample period in 2000–2013, and the conditional correlation between mainland China's and the U.S. stock markets is quite low and highly volatile. The Chinese stock markets are found to be highly regimes persistent. These findings have important implication for investors seeking opportunity of portfolio diversification.
For the purpose of developing alternative approach for forecasting volatility, we consider heterogeneous VAR (HVAR) model which accommodates the market effects of different horizons, namely, daily, weekly and monthly effects, and examine the interdependence of stock markets in Brazil and the US, based on information of daily return, range and trading volume. To compare with the new approach, we also work with the univariate and multivariate GARCH models with asymmetric effects, trading volumes and fat-tails. The heteroskedasticity-corrected Granger causality tests based on the HVAR show the strong evidence of such spillover effects. We assess the value-at-risk thresholds for Brazil, based on the out-of-sample forecasts of the HVAR model, finding the new approach works satisfactory for the periods including the global financial crisis, without assuming heavy-tailed conditional distributions.
An asymmetric conditional mean returns model describing co-movements of three major European stock markets with the U.S. stock market is estimated. Multivariate conditional heteroskedasticity is captured by a VAR(p)-MGARCH(p,q)-BEKK parameterization. Conditional Sharpe ratios from alternative mean-volatility specifications are compared with multivariate t-tests. Results consistently indicate that France has offered the best risk-adjusted returns compared to the U.K. or Germany taken from a U.S. investor's viewpoint. All three European markets show a tendency to move counter-cyclically with the U.S. market, on average, suggesting ongoing substitution by global investors between U.S. and major European equities. Unconditional Sharpe ratios understate conditional Sharpe ratios for each market.
We use univariate and multivariate GARCH-type models to investigate the properties of conditional volatilities of stock returns and exchange rates, as well as their empirical relationships. Taking three European stock markets and two popular US dollar exchange rates as case study, our results show strong evidence of asymmetry and long memory in the conditional variances of all the series considered. In multivariate settings we find that bilateral relationships between stock and foreign exchange markets are highly significant for France and Germany. Moreover, both the univariate FIAPARCH and bivariate CCC-FIAPARCH models provide more accurate in-sample estimates and out-of-sample forecasts than the other competing GARCH-based specifications in almost all cases. Finally, there is evidence to support the suitability of the FIAPARCH model in forecasting portfolio's market risk exposure and the existence of diversification benefits between stock and foreign exchange markets.
This paper uses ARCH models to examine if there is a leverage effect and also to test if A- and B-share holdings have different risks in Chinese stock markets before and after B-share markets open to domestic investors in February 2001. The empirical results suggest that leverage effect was not present and shocks have symmetric impact on the volatility of Chinese B-share stock returns in both periods and A-share returns in Period I. Thus GARCH model would be a better model to fit the Chinese B-share stock returns than EGARCH or GJR-GARCH model. But EGARCH or GJR-GARCH model fits recent (Period II) A-share markets data better than GARCH model. Another finding of this paper is that holding A- or B-share bears different risk in returns in the two Chinese markets. Furthermore, news or shocks have a larger impact on volatility of B-share returns in Period I than in Period II.
We investigate the impact of financial crises on two fundamental features of stock returns, namely, the risk-return tradeoff and the leverage effect. We apply the fractionally integrated exponential GARCH-in-mean (FIEGARCH-M) model for daily stock return data, which includes both features and allows the co-existence of long memory in volatility and short memory in returns. We extend this model to allow the financial parameters governing the volatility-in-mean effect and the leverage effect to change during financial crises. An application to the daily U.S. stock index return series from 1926 through 2010 shows that both financial effects increase significantly during crises. Strikingly, the risk-return tradeoff is significantly positive only during financial crises, and insignificant during non-crisis periods. The leverage effect is negative throughout, but increases significantly by about 50% in magnitude during financial crises. No such changes are observed during NBER recessions, so in this sense financial crises are special. Applications to a number of major developed and emerging international stock markets confirm the increase in the leverage effect, whereas the international evidence on the risk-return tradeoff is mixed.
In his Nobel Laureate lecture Engle notes that asymmetric volatility has a significant impact on risk. In this article equity market volatility is estimated using an asymmetric power-GARCH model which nests many other popular models. We estimate the magnitude of asymmetric volatility for several emerging and mature markets for three sub-periods. Many mature markets exhibit large magnitudes of asymmetric volatility and several emerging markets do so as well. The magnitude of asymmetry varies by sub-period and is consistent with the suggestion in Campbell and Hentschel (1992) that asymmetry is greater when markets are more volatile.
We investigate the pricing of idiosyncratic volatility of seven frontier markets in six GCC countries. We find a significant (marginal) negative relationship between expected returns and lagged idiosyncratic volatility for individual stocks in Saudi Arabia (Qatar) but none in Kuwait and Abu Dhabi. However, when we estimate conditional idiosyncratic volatility either by EGARCH or AR Models, the relationship turns positive. Introducing unexpected idiosyncratic volatility as an explanatory variable to control for any unexpected returns uncovers the true relationship between expected idiosyncratic volatility and expected returns. The evidence turns out to be robust for return reversals and other control variables. Moreover, the pricing of idiosyncratic risk is less evident in higher country governance and seems to be unrelated to the degree of financial development.
Empirical research on European stock markets has shown that they behave differently according to the performance of the leading financial market identified as the US market. A positive sign is viewed as good news in the international financial markets, a negative sign means, conversely, bad news. As a result, we assume that European stock market returns are affected by endogenous and exogenous shocks. The former raise in the market itself, the latter come from the US market, because of its most influential role in the world. Under standard assumptions, the distribution of the European market index returns conditionally on the sign of the one-day lagged US return is skew-normal. The resulting model is denoted Skew-GARCH. We study the properties of this new model and illustrate its application to time-series data from three European financial markets.
A simple minimization problem yielding the ordinary sample quantiles in the location model is shown to generalize naturally to the linear model generating a new class of statistics we term "regression quantiles." The estimator which minimizes the sum of absolute residuals is an important special case. Some equivariance properties and the joint aymptotic distribution of regression quantiles are established. These results permit a natural generalization to the linear model of certain well-known robust estimators of location. Estimators are suggested, which have comparable efficiency to least squares for Gaussian linear models while substantially out-performing the least-squares estimator over a wide class of non-Gaussian error distributions.
We analyze cross-sectional and time-series information from 46 equity markets around the world to consider whether short sales restrictions affect the efficiency of the market and the distributional characteristics of returns to individual stocks and market indices. We find some evidence that prices incorporate negative information faster in countries where short sales are allowed and practiced. A common conjecture by regulators is that short sales restrictions can reduce the relative severity of a market panic. We find strong evidence that in markets where short selling is either prohibited or not practiced, market returns display significantly less negative skewness.
This paper explores the cross-market dependence between five popular equity indices (S&P 500, NASDAQ 100, DAX 30, FTSE 100,
and Nikkei 225), and their corresponding volatility indices (VIX, VXN, VDAX, VFTSE, and VXJ). In particular, we propose a
dynamic mixed copula approach which is able to capture the time-varying tail dependence coefficient (TDC). The findings indicate
the existence of financial contagion and significant asymmetric TDCs for major international equity markets. In some situations,
although contagion cannot be clearly detected by stock index movements, it can be captured by dependence between volatility
indices. The results imply that contagion is not only reflected in the first moment of index returns, but also the second
moment, i.e. the volatility. Results also show that dependence between volatility indices is more easily influenced by financial
shocks and reflects the instantaneous information faster than the stock market indices.
We provide an analysis of frontier market equities with respect to world market integration and diversification. Principal component results reveal that frontier markets exhibit low levels of integration. In contrast with developed and emerging markets, frontier markets offer no indication of increasing integration through time. Furthermore, individual frontier market countries do not exhibit consistent rates of changing integration. Structural break tests identify breakpoints in integration, as well as integration dynamics across countries. We show that frontier markets have low integration with the world market and thereby offer significant diversification benefits.
The paper examines the short-run spillover effects of daily stock returns and volatilities between the Standard & Poor's (S&P) 500 stock index in the US and the Shanghai Stock Exchange (SSE) index in China. First, we find that a structural break occurred in the SSE stock return mean in December 2005. Second, by analyzing modified general autoregressive conditional heteroscedasticity (GARCH)(1,1)-M models, we find evidence of a symmetric and asymmetric volatility spillover effect from the US to the China stock market in the post-break period. Third, we observe the symmetric volatility spillover effect from China to the US in the post-break period.
This paper presents theoretical results in the formulation and estimation of multivariate generalized ARCH models within simultaneous equations systems. A new parameterization of the multivariate ARCH process is proposed and equivalence relations are discussed for the various ARCH parameterizations. Constraints sufficient to guarantee the positive definiteness of the conditional covariance matrices are developed, and necessary and sufficient conditions for covariance stationarity are presented. Identification and maximum likelihood estimation of the parameters in the simultaneous equations context are also covered. * This paper began as a synthesis of at least three UCSD Ph.D. dissertations on various aspects of multivariate ARCH modelling, byYoshi Baba, Dennis Kraft and Ken Kroner. In fact, an early version of this paper was written by Baba, Engle, Kraft and Kroner, which led to the acronym (BEKK) used in this paper for the new parameterization presented. In the interests of continui...
We study the joint dynamics of overnight and daytime return volatility for the Nikkei Stock Average in Tokyo and the Standard and Poor's 500 Stock Index in New York over the recent 1988–92 period. We extend the GARCH framework of Engle (1982) and Bollerslev (1986) to allow for asymmetric effects of negative (“bad news”) and positive (“good news”) foreign market returns shocks for volatility. Our evidence demonstrates that the magnitude and persistence of shocks originating in New York or Tokyo that transmit to the other market are significantly understated if this asymmetric effect is ignored. Implications for pricing of securities within those markets, for hedging and other global trading strategies and for regulatory policies within these financial markets are also discussed.
This paper investigates financial contagion in a multivariate time-varying asymmetric framework, focusing on four emerging equity markets, namely Brazil, Russia, India, China (BRIC) and two developed markets (U.S. and U.K.), during five recent financial crises. Specifically, both a multivariate regime-switching Gaussian copula model and the asymmetric generalized dynamic conditional correlation (AG-DCC) approach are used to capture non-linear correlation dynamics during the period 1995–2006. The empirical evidence confirms a contagion effect from the crisis country to all others, for each of the examined financial crises. The results also suggest that emerging BRIC markets are more prone to financial contagion, while the industry-specific turmoil has a larger impact than country-specific crises. Our findings imply that policy responses to a crisis are unlikely to prevent the spread among countries, making fewer domestic risks internationally diversifiable when it is most desirable.
The transmission mechanism of price and volatility spillovers across the New York, Tokyo and London stock markets is investigated. The asymmetric impact of good news (market advances) and bad news (market declines) on volatility transmission is described by an extended multivariate Exponential Generalized Autoregressive Conditionally Heteroskedastic (EGARCH) model. Using daily open-to-close returns, we find strong evidence that volatility spillovers in a given market are much more pronounced when the news arriving from the last market to trade is bad. A before and after October 1987 crash analysis reveals that the linkages and interactions among the three markets have increased substantially in the post-crash era, suggesting that national markets have grown more interdependent.
This paper examines the relation between market volatility and investor trades by identifying who supplies and demands market liquidity on the Tokyo Stock Exchange. Because the different trading patterns of various investor types such as individual investors, institutional investors, and foreign investors affect market liquidity differently, we find that market volatility fluctuates significantly depending on which investor types participate in trade. We show that market volatility increases by more than 50% from the average level when there are greater buy trades by momentum investors that demand liquidity and there are less sell trades by contrarian (or profit-taking) investors that supply liquidity. On the other hand, volatility dampens by more than 57% when there are greater sell trades by profit-taking investors, mostly by domestic investors, while there are less momentum buy trades.