Real estate in the mixed-asset portfolio: the question of consistency

The recent poor performance of the equity market in the UK has meant that real estate is increasingly been seen as an attractive addition to the mixed-asset portfolio. However, determining whether the good return enjoyed by real estate is a temporary or long-term phenomenon is a question that remains largely unanswered. In other words, there is little or no evidence to indicate whether real estate should play a consistent role in the mixed-asset portfolio over short- and long-term investment horizons. Consistency in this context refers to the ability of an asset to maintain a positive allocation in an efficient portfolio over different holding periods. Such consistency is a desirable trait for any investment, but takes on particular significance when real estate is considered, as the asset class is generally perceived to be a long-term investment due to illiquidity. From an institutional investor’s perspective, it is therefore crucial to determine whether real estate can be reasonably expected to maintain a consistent allocation in the mixed-asset portfolio in both the short and long run and at what percentage. To address the question of consistency the allocation of real estate in the mixed-asset portfolio was calculated over different holding periods varying from 5- to 25-years.

The impact of news on measures of undiversifiable risk: evidence from the UK stock market

Using UK equity index data, this paper considers the impact of news on time varying measures of beta, the usual measure of undiversifiable risk. The empirical model implies that beta depends on news about the market and news about the sector. The asymmetric response of beta to news about the market is consistent across all sectors considered. Recent research is divided as to whether abnormalities in equity returns arise from changes in expected returns in an efficient market or over-reactions to new information. The evidence suggests that such abnormalities may be due to changes in expected returns caused by time-variation and asymmetry in beta.

Connectivity measures for internet topologies on the level of autonomous systems

Classical measures of network connectivity are the number of disjoint paths between a pair of nodes and the size of a minimum cut. For standard graphs, these measures can be computed efficiently using network flow techniques. However, in the Internet on the level of autonomous systems (ASs), referred to as AS-level Internet, routing policies impose restrictions on the paths that traffic can take in the network. These restrictions can be captured by the valley-free path model, which assumes a special directed graph model in which edge types represent relationships between ASs. We consider the adaptation of the classical connectivity measures to the valley-free path model, where it is -hard to compute them. Our first main contribution consists of presenting algorithms for the computation of disjoint paths, and minimum cuts, in the valley-free path model. These algorithms are useful for ASs that want to evaluate different options for selecting upstream providers to improve the robustness of their connection to the Internet. Our second main contribution is an experimental evaluation of our algorithms on four types of directed graph models of the AS-level Internet produced by different inference algorithms. Most importantly, the evaluation shows that our algorithms are able to compute optimal solutions to instances of realistic size of the connectivity problems in the valley-free path model in reasonable time. Furthermore, our experimental results provide information about the characteristics of the directed graph models of the AS-level Internet produced by different inference algorithms. It turns out that (i) we can quantify the difference between the undirected AS-level topology and the directed graph models with respect to fundamental connectivity measures, and (ii) the different inference algorithms yield topologies that are similar with respect to connectivity and are different with respect to the types of paths that exist between pairs of ASs.

An analysis of commercial real estate returns: an anatomy of smoothing in asset and index returns

In this article, we investigate the commonly used autoregressive filter method of adjusting appraisal-based real estate returns to correct for the perceived biases induced in the appraisal process. Many articles have been written on appraisal smoothing but remarkably few have considered the relationship between smoothing at the individual property level and the amount of persistence in the aggregate appraisal-based index. To investigate this issue we analyze a large sample of appraisal data at the individual property level from the Investment Property Databank. We find that commonly used unsmoothing estimates at the index level overstate the extent of smoothing that takes place at the individual property level. There is also strong support for an ARFIMA representation of appraisal returns at the index level and an ARMA model at the individual property level.