Will the crisis “tear us apart”? Evidence from the EU

Abstract

We examine the synchronisation of the European Union (EU) financial markets before and during the 2007 global financial crisis. We use an Asymmetric Dynamic Conditional Correlation (ADCC)-GARCH framework to control for the time-varying correlations and a Markov-Switching model to identify regime changes. Our sample considers 27 EU nations for the period 2000-2011. For each nation we formulate several characteristics of the crisis such as, synchronicity, duration and intensity measures. We find that the more recent EU members had a lagged entry to the crisis regime, were less adversely affected, show higher correlation between their stock markets and have their credit scores being revised more frequently relative to established EU members. We also find that higher levels of sovereign debt and lower levels of industrialisation positively impact crisis duration and intensity. Our results refute the notion of uniform integration of EU financial markets as evident from the highly non-synchronised observed crisis experience among the EU members. As such, one-size fits all policies are likely to be ineffective.

Transmission of the Greek crisis on the sovereign debt markets in the euro area

“…The number of contagious episodes as well as their durations changed from one pair to another, indicating the heterogeneity of the impact of the subprime crisis on the sovereign markets in our sample. This finding rejects the hypothesis of a perfect integration of the EMU sovereign debt markets, confirming the study of Pappas et al ( 2016 ). Using an ADCC-GARCH framework and a Markov-Switching model, these authors found the same result in European stock markets during the subprime crisis.…”

“…Using an ADCC-GARCH framework and a Markov-Switching model, these authors found the same result in European stock markets during the subprime crisis. Therefore, Pappas et al ( 2016 ) concluded that one-size fits all policies are likely to be ineffective.…”

The impact of the Russian-Ukrainian war on global financial markets

“…3 For example, Izzeldin, Muradoglu, Pappas, and Sivaprasad (2021) utilize a smooth-transition HAR (ST-HAR) to identify intensity, timeliness, and homogeneity of the Covid-19 crisis upon G7 stock markets. Parametric models of volatility estimation can also allow for similar dynamics, see for example Pappas, Ingham, Izzeldin, and Steele (2016) who use a Markov-switching multivariate DCC-GARCH model to examine the synchronicity of the GFC crisis upon European stock markets. Yip, Brooks, Do, and Nguyen (2020) examine the volatility spill-over effects between oil and agricultural products using a Markov-switching setup.…”

The impact of Covid-19 on G7 stock markets volatility: Evidence from a ST-HAR model

“…1 Such non-linear models may be embedded within other models to allow non-linearities and/or regime changes. Pappas, Ingham, Izzeldin, and Steele (2016) combine a Markov-switching model with the multivariate DCC-GARCH models, to incorporate the delay and intensity to a crisis regime following the 2008 Global Financial Crisis. Papanicolaou and Sircar (2014) combine a Markov-switching model with the Heston stochastic volatility model to capture the option strikes that lie in the tail of the distribution of the volatility process.…”

“…The empirical literature on volatility modelling is extensive; focusing upon commodities including, silver ( Li, Cheng, & Fang, 2020 ), gold ( Chkili, 2017 ; Lucey & O'Connor, 2013 ), electricity ( Ciarreta, Pizarro-Irizar, & Zarraga, 2020 ) and oil ( Nademi & Nademi, 2018 ); and upon financial instruments, namely options Elliott, Nishide, and Osakwe (2016) , bonds ( Tamakoshi & Hamori, 2014 ), futures (N. Taylor, 2019 ) and equity indices ( Pappas et al, 2016 ). Other studies focus upon key political events, economic uncertainty, macroeconomic announcements and financial crises upon volatility ( Moore & Wang, 2007 ; Tiwari, Aye, Gupta, & Gkillas, 2020 ).…”