Volatility spillover shifts in global financial markets

BenSaïda, Ahmed; Litimi, Houda; Abdallah, Oussama

doi:10.1016/j.econmod.2018.04.011

Cited by 85 publications

(44 citation statements)

References 33 publications

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“…These results inform that there are volatility spillovers from Russia as a major stock market to all stock markets observed in Eastern Europe region, except to Ukraine stock market. The model specifications in variance equation using the GFC sample period for each ten recipient stock market, as presented in Table 3, are expressed as follow: Volatility spillover is the causality in variance among markets (BenSaïda et al, 2018). The results from causality analyses of volatilities using overall sample period are not distantly different with the results using the GFC sample period.…”

Section: Empirical Results and Discussionmentioning

confidence: 99%

“…This empirical study on volatility spillover from the global market to a stock market has an important role from the particular perspective of portfolio diversification and hedging strategies (Majdoub & Mansour, 2014). Moreover, studying spillover volatility has direct implication in designing optimal portfolios and building policies to prevent harmful shock transmission and to limit the propagation of financial crises across borders (BenSaïda et al, 2018). Therefore, understanding the volatility across markets is crucial for risk managers, hedgers, and policy makers, especially volatility spillover due to the financial crisis.…”

Section: Empirical Results and Discussionmentioning

confidence: 99%

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Volatility spillovers under difference in the degree of market integration: Evidence from the selected Asian and Eastern European stock markets

Muharam¹,

Mawardi²,

Arfinto³

et al. 2019

Journal of International Studies

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This research aims to investigate volatility transmitted from the world market to ten Asian and Eastern European stock markets and from major stock market in the region to the rest of stock markets by considering their degree of integrations. To assess this, we apply GARCH(p,q) model and involve dynamic conditional correlation (DCC) model to generate the dynamic degree of integration. The monthly market indices data over the period from May 2002 to March 2018 are taken from 11 markets --5 Asian ones (China, Indonesia, Malaysia, Pakistan, and the Philippines), 5 Eastern European (Czech Republic, Poland, Romania, Russia, and Ukraine), and the world market data. Furthermore, the volatility spillover was analysed during the global financial crisis period, from The findings show that volatility spillovers from the world and the major regional markets to domestic stock markets are conditional on the degree of integrations. Specifically, there is no volatility spillover from the world and regional major markets on segmented Volatility spillovers under difference in the degree of market integration: Evidence from… 135 stock markets. In contrast, domestic stock markets which are integrated could experience the volatility spillover. Moreover, this confirms for both crisis circumstances and the overall period.

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Section: Empirical Results and Discussionmentioning

confidence: 99%

Section: Empirical Results and Discussionmentioning

confidence: 99%

Volatility spillovers under difference in the degree of market integration: Evidence from the selected Asian and Eastern European stock markets

Muharam¹,

Mawardi²,

Arfinto³

et al. 2019

Journal of International Studies

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show abstract

“…In this vein, the seminal work of Pastor and Veronesi [5] laid out theoretical foundations for the potential effects of economic policy uncertainty (hereafter EPU) on stock returns and volatility. Subsequently, a growing number of scholars began to investigate the transmission of fear across the country or regional stock markets [6,7]. However, the literature on the driving potential of EPU connectedness for fear connectedness remains scant.…”

Section: Introductionmentioning

confidence: 99%

Role of Economic Policy Uncertainty in the Connectedness of Cross-Country Stock Market Volatilities

et al. 2020

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The implied volatility index is a forward-looking indicator of fear among stock market participants. We examine the extent to which the connectedness of fear among global stock markets is driven by the cross-country connectedness of economic policy uncertainty (EPU). We use data on stock market fear and EPU indices for 13 countries, which spans from January 2011 to December 2018. To measure the connectedness among stock market fear and EPU of our sample countries, we employ two connectedness models. A cross-sectional regression model is further employed to ascertain the extent to which EPU connectedness between two countries explains the connectedness of fear between their stock markets, while controlling for bilateral linkage and country-specific factors. We find that EPU connectedness between any two partner countries significantly drives the connectedness of fear between their stock markets. The driving potential not only holds for short- and long-term connectedness, but also after controlling for bilateral linkages (bilateral trade, geographical distance, common language) and country-specific (trade and financial openness of the transmitter country) factors indicating robustness in our results.

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“…Studies such as [19] find that during tranquil times there are particular countries that are net transmitters of risk and others are net receivers of risk in global financial markets. The study particularly analyses the global financial shifts of volatility spillovers by employing the [20] forecast-error variance decomposition and incorporating a Markov switching framework which considers economic regime changes, into the generalised vector autoregressive (VAR) model.…”

Section: Equitiesmentioning

confidence: 99%

The Independence of Indexed Volatilities

Kola¹,

Sebehela²

2021

Linear and Non-Linear Financial Econometrics -Theory and Practice

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Studies on indexed volatility spillovers are unique because indices encompass more information than other parameters used in illustrating volatility movements. Further, indices encompass most of the constituents listed on different stock exchanges around the globe. This chapter uses vector autoregression (VAR) for volatility spills and the Markov regime switching model to understand how different volatility regimes behave among bonds, commodities, equities and real estate indices of emerging markets. The results illustrate that volatility spillovers occur within (same) indices and across different indices. Moreover, those spillovers are within and across emerging countries. Interestingly, illiquid indices in certain situations move in between different volatility regimes more than liquid indices. Volatility strategies emanating from this study are equally applicable to both sell and buy sides in securities markets.

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Volatility spillover shifts in global financial markets

Cited by 85 publications

References 33 publications

Volatility spillovers under difference in the degree of market integration: Evidence from the selected Asian and Eastern European stock markets

Volatility spillovers under difference in the degree of market integration: Evidence from the selected Asian and Eastern European stock markets

Role of Economic Policy Uncertainty in the Connectedness of Cross-Country Stock Market Volatilities

The Independence of Indexed Volatilities

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