Time series behavior of average dynamic conditional correlations in European real estate securities markets: An empirical exploration

Abstract

Purpose: The purpose of this paper is to investigate the time series behavior of co-movements among 11 European real estate securities markets, with each other as well as between country-averages, over the sample period from January 1999 to January 2010 by utilizing the asymmetric dynamic conditional correlation (ADCC) technique, long-memory tests and multiple structural break methodology. Design/methodology/approach: First the ADCC from the multivariate GJR-GARCH model is used to estimate the pair-wise conditional correlations between the 11 securitized real estate markets. Then, the 11 country-average conditional correlation series is subject to a battery of four long-memory tests to form an "on the balance of evidence" picture; the semi-parametric Geweke and Porter-Hudak procedure and Robinson test, as well as the non-parametric Hurst-Mandelbrot R/S and Lo's modified R/S tests. Finally, the Bai and Perron's multiple structural break methodology seeks to test whether the average conditional correlations are subject to regime switching via the detection of breaks in the co-movements of real estate securities returns. Findings: Low to moderate conditional correlations are found for these European real estate securities market and a higher level of correlation in the aftermath of the global financial crisis. The long-memory correlation effect is present for nine European real estate securities markets. In addition, the conditional correlations are subject to regime switching with two structural breaks in four country-average correlation series. Across the regimes, a higher level of correlation is linked to a higher level of volatility and a lower level of return, and this happened around the global financial crisis period. Research limitations/implications: The findings that national real estate securities correlations exhibit time-varying and asymmetric behavior can help investors understand how real estate securities will co-move in different market scenarios (e.g. "crisis" and "non-crisis" times). Moreover, the process of dynamic covariance analysis and forecasting (the ultimate objective in portfolio management) should not rely too much on short-term autoregressive moving average models. Instead, a combination of some appropriate long-range dependence models and regime-switching specifications is needed. Originality/value: This paper offers useful insights into the time series behavior of average dynamic conditional correlations in European public property markets. © Emerald Group Publishing Limited.