Asymmetric semi-volatility spillover effects in EMU stock markets

Caloia, Francesco; Cipollini, Andrea; Muzzioli, Silvia

doi:10.1016/j.irfa.2018.03.001

Cited by 32 publications

(19 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A possible extension of this study could be the use of a multivariate extension of the Heterogenous Autoregressive Model (HAR) developed by Corsi (2009) which is able to capture different stylized facts associated with volatility and its dynamics. See Fengler and Gisler (2015) for an analysis of U.S. stock, bond, and gold financial markets and Caloia, Cipollini, and Muzzioli (2018) for an analysis of European stock markets.…”

Section: Notesmentioning

confidence: 99%

A high-frequency analysis of return and volatility spillovers in the European sovereign bond market

O’Sullivan

Papavassiliou

2021

The European Journal of Finance

View full text Add to dashboard Cite

Using high-frequency data from the MTS trading platform, we examine return and volatility spillover effects across different maturities in the European sovereign bond market over tranquil and crisis periods. The longer-term benchmark securities of core countries are the largest net volatility transmitters, whereas the shorter-term benchmarks of periphery countries are the leading net receivers of volatility shocks. Moreover, the short-end and the long-end of the yield curve in both regions emerge as the sole net recipients of return spillovers. We note that bonds of periphery countries become volatility spillover transmitters during important macroeconomic events such as credit rating downgrades and financial assistance packages to financially distressed countries.

show abstract

Section: Notesmentioning

confidence: 99%

A high-frequency analysis of return and volatility spillovers in the European sovereign bond market

O’Sullivan

Papavassiliou

2021

The European Journal of Finance

View full text Add to dashboard Cite

show abstract

“…The earlier studies illustrated slight integration and spillover effect between the stock markets (Ko -Lee 1991;Panton et al 1976;Kim et al 2015;Bhar -Nikolova 2009). However, most recent investigations indicated strong interdependence between them because of the development of advanced technology and financial deregulations of the financial markets (Jebran et al 2017;Oki ci c 2015;Johsi 2011;Baum€ ohl et al 2018;Mensi et al 2018;Morales Zumaquero -Sosvilla Rivero 2018;Caloia et al 2018;Alotaibi -Mishra 2015;Lau -Sheng 2018;BenSaı €da et al 2018;Tiwari et al 2018;Bala -Takimoto 2017;Ghouse -Khan 2017;Ahmed -Huo 2018;Naresh et al 2018;Huo -Ahmed 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Does volatility transmission between stock market returns of Central and Eastern European countries vary from normal to turbulent periods?

Hung

2020

AOecon

View full text Add to dashboard Cite

This study investigates the transmission mechanism of price and volatility spillovers across the Budapest, Warsaw, Prague, Bucharest, and Zagreb stock markets in the pre- and post-financial crisis periods under the framework of the multivariate Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model. By using daily closing prices, the results highlight certain interesting findings. I found evidence of price spillovers of the intraregional linkages among the stock price movements in five countries. This analysis shows the existence of bi-directional volatility spillovers between stock markets of the Czech Republic and Croatia in the pre-crisis period, and between Hungary and Romania in the post-crisis period. Also, there are significant volatility spillovers from Croatia to Poland and from Poland to the Czech Republic during two periods. The volatility is found to respond asymmetrically to innovations in other markets. The findings also indicate that the stock markets are more substantially integrated into crisis, as well as the persistence of volatility spillovers between the stock markets increases, and the financial stock markets become more integrated after the crisis period.

show abstract

“…Demirer et al (2018) reported that banking stock connectedness increases during crises, with cross-country linkages providing more fluctuations than within-country bank linkages. Caloia et al (2018) built on Diebold and Yilmaz's (2012) model. They used a multivariate extension of the heterogeneous autoregressive model to show asymmetric risk transmission between Germany, France, the Netherlands, Italy, and Spain.…”

Section: Introductionmentioning

confidence: 99%

A Network Analysis of the Asia-Pacific and Other Developed Stock Markets: Pre and Post Global Financial Crisis

Guhathakurtha

Bhattacharya

2020

AFL

View full text Add to dashboard Cite

This paper examines the volatility spillover and connectedness between Asia-Pacific, US, UK, and eurozone stock markets. A spillover index is built using forecast error variance decomposition in a vector autoregression framework and the spillover index is used to build network diagrams. It shows evidence of how the increase in risk transfer (volatility spillover) between the markets led to the global financial crisis and of the higher level of connectedness since. Network diagrams show the direction and strength of the connectedness. The network strength estimation enables us to understand the risk associated with connectedness across the markets in the event of a trigger and its influence in portfolio management decisions of international funds. The Chinese market appears to be the most insulated, while the South Korean, Hong Kong, and Singapore stock markets dominate in terms of risk transfer. The US, UK, EU, Singapore and Hong Kong are the top five volatility spillover recipient markets, both during pre and post global financial crisis periods. We find the market size to be irrelevant in the determination of the level of connectedness, whereas the role of geographical proximity cannot be ruled out. The findings are relevant to multinational investment strategies and in understanding the relative risk of investment in the Asia-Pacific region.

show abstract

Asymmetric semi-volatility spillover effects in EMU stock markets

Cited by 32 publications

References 14 publications

A high-frequency analysis of return and volatility spillovers in the European sovereign bond market

A high-frequency analysis of return and volatility spillovers in the European sovereign bond market

Does volatility transmission between stock market returns of Central and Eastern European countries vary from normal to turbulent periods?

A Network Analysis of the Asia-Pacific and Other Developed Stock Markets: Pre and Post Global Financial Crisis

Contact Info

Product

Resources

About