Abstract:
In recent years there has been an increased interest in the extent to which managers can improve
their property portfolio position through international diversification. Much of this interest has
centred on the use of various statistical/econometric tests of time-varying correlations and longrun
equilibrium positions using whole of country property indices. In this paper, a short-run
tactical asset allocation approach to securitized property is adopted. Using neural network
methodology, a neural network model that ‘learns’ well-established rules of portfolio investment
is built. The model uses a set of individual property companies across three of the most highly
securitized property markets in the world viz. the US, the UK and Australia. The standpoint of a
UK investor is adopted and the model is asked to compare portfolios constructed purely from
domestic assets with portfolios constructed from internationally held assets allowing for foreign
exchange adjustments. When the foreign exchange risk is actively managed, the outcomes from
the analysis suggest that the gains from hedging are conditional on both the return to the
unhedged position and the volatility of the underlying currency being hedged.
