European equity market integration and joint relationship of conditional volatility and correlations

Abstract

We analyse the integration patterns of seven leading European stock markets from 1990 to 2013 using daily data and mismatched monthly macroeconomic data. To study the mismatch of data frequencies we use the DCC-MIDAS (Dynamic Conditional Correlation-Mixed Data Sampling) technique developed by Colacito, Engle and Ghysels (Journal of Econometrics, 2011). We benchmark European integration patterns against the German stock market. The reported integration patterns show a clear divide between large and (relatively) small equity markets' short run and long run return correlations: the small markets display higher short run European convergences than the large markets and vice versa. The across-the-board divergence from Greek risk, during the crisis period, is the most unambiguous conclusion of our study. During this period, crosscountry joint relationships of conditional variances and return correlations-a 'convergence of risks' resulting in global/regional contagious spillovers-are typically positive. Only exceptions are the German stock market's joint relationships.

The Comovement of Exchange Rates and Stock Markets in Central and Eastern Europe

“…Further, we estimate the DCC-MIDAS parameters by using the quasi-maximum likelihood function, as stated in Engle [78] and Colacito, et al [77]. In setting the number of lags in the MIDAS equation (K in Equations (3), (5) and 7), we follow other studies [54,81] and use K = 12, which corresponds to a so-called three MIDAS years period. Furthermore, we follow Engle, et al [79] and fix the weight ω 1 to one.…”

“…The first group of literature emphasizes that extreme events generate a high volatility in equity markets comovements and lead to contagion and tail dependence [36][37][38][39][40][41][42][43][44][45][46][47][48]. The second group of studies focuses on the issue of stock market integration [49][50][51][52][53][54].…”

Changes in co-movement and risk transmission between South Asian stock markets amidst the development of regional co-operation

“…Some recent studies focus on regional blocs of countries like the European Union (EU), ASEAN and China and countries along the "Belt-and-Road". They document that integration among stock markets increased after the establishment of trading blocs (Dedi and Yavas 2016;Virk and Javed 2017;Chevallier et al 2018;Lu et al 2019).…”

A large constrained time‐varying portfolio selection model withDCC‐MIDAS: Evidence from Chinese stock market

“…Owing to the advantage in exploring dynamic correlations, the DCC‐MIDAS model has been widely used in many areas. In equity market, Virk and Javed (2017) apply the DCC‐MIDAS model to estimate dynamic pairwise correlations and analyse the integration patterns of seven leading European stock markets; Fang, Yu, and Li (2017) study how economic policy uncertainty influences the time‐varying long‐term correlation of U.S. stock and bond markets based on the DCC‐MIDAS model; Niţoi and Pochea (2019) apply it to study European Union stock market co‐movements and their determinants. In energy market, the DCC‐MIDAS model is used by Turhan, Sensoy, Ozturk, and Hacihasanoglu (2014) to analyse the time‐varying long‐run correlations between crude oil and the major asset classes, is applied by Conrad, Loch, and Rittler (2014) to investigate the macroeconomic determinants of the long‐term volatilities and correlations in crude oil and U.S. stock price returns, and is employed by Yang, Cai, and Hamori (2018) to explore the long‐term correlation between oil prices and exchange rates.…”