No abstract

No abstract

Purpose -The purpose of this paper is to explore a regime switching asset allocation model that includes six major real estate security markets (USA, UK, Japan, Australia, Hong Kong and Singapore) and focuses on how the presence of regimes affects portfolio composition. Design/methodology/approach -A Markov switching model is first developed to characterize real estate security markets' risk-return in two regimes. The mean-variance portfolio construction methodology is then deployed in the presence of the two regimes. Finally, the out-of-sample analyzes are conducted to examine whether the regime switching allocation outperforms the conventional allocation strategy. Findings -Strong evidence of regimes in the six real estate security markets in detected. The correlations between the various real estate security markets' returns are higher in the bear market regime than in the bull market regime. Consequently the optimal real estate portfolio in the bear market regime is very different from that in the bull market regime. The out-of-sample tests reveal that the regime-switching model outperforms the non-regime dependent model, the world real estate portfolio and equally-weighted portfolio from risk-adjusted performance perspective. Originality/value -The application of the Markov switching technique to real estate markets is relatively new and has great significance for international real estate diversification. With increased significance of international securitized property as a real estate investment vehicle for institutional investors to gain worldwide real estate exposure, this study provides significant insights into the investment behavior and optimal asset allocation implications of the listed real estate when returns follow a regime switching process.

This paper examines whether the Shanghai and Hong Kong property stock markets are closely related in the period 1993-2003. As two economically promising cities in Asia, Hong Kong and Shanghai are held tightly together, by social, cultural and business ties. Therefore, it is important for international real estate investors, who want to enter China markets, to understand the relationships between the two markets in order to develop the right investment strategy. In this research, we analyse risk-return performance and the dynamic relationships between these two markets. Furthermore, we employ cointegration with structural break, errorcorrection model (ECM) and Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models to the property stock data of the two markets. The empirical results suggest strong evidence of long-run and short-run relationships between the two markets.

No abstract