Diversification when it hurts? The joint distributions of real estate and equity markets

Much of the literature on the construction of mixed asset portfolios and the case for property as a risk diversifier rests on correlations measured over the whole of a given time series. Recent developments in finance, however, focuses on dependence in the tails of the distribution. Does property offer diversification from equity markets when it is most needed - when equity returns are poor. The paper uses an empirical copula approach to test tail dependence between property and equity for the UK and for a global portfolio. Results show strong tail dependence: in the UK, the dependence in the lower tail is stronger than in the upper tail, casting doubt on the defensive properties of real estate stocks.

eRPUK 2003: eCommerce and Retail Property in the UK: third annual benchmarking survey

This new survey, which has just been completed and includes brand new data, has been funded by the RICS Education Trust and the European Shopping Centre Trust. It follows up our 2000 survey of UK retailers, investors and developers. The report presents results from our new 2001 survey. This continuing benchmark series of studies includes an extensive review of developments in ecommerce and retail in Europe and the USA. The survey reveals a cooling in attitude towards ecommerce in the UK, but there is rapid growth in some sectors and polarisation and marginalisation of secondary centres are likely to increase. In Europe the growth of a three tier system of ecommerce 'pioneers', 'followers' and 'laggards' is becoming established, and the research also reveals results from a recent joint survey on US and UK retailers conducted with Colorado State University. There is a danger of complacency as UK online sales (in percentage terms) now outstrip USA.

Joint multiple dictionary learning for tensor sparse coding

Traditional dictionary learning algorithms are used for finding a sparse representation on high dimensional data by transforming samples into a one-dimensional (1D) vector. This 1D model loses the inherent spatial structure property of data. An alternative solution is to employ Tensor Decomposition for dictionary learning on their original structural form —a tensor— by learning multiple dictionaries along each mode and the corresponding sparse representation in respect to the Kronecker product of these dictionaries. To learn tensor dictionaries along each mode, all the existing methods update each dictionary iteratively in an alternating manner. Because atoms from each mode dictionary jointly make contributions to the sparsity of tensor, existing works ignore atoms correlations between different mode dictionaries by treating each mode dictionary independently. In this paper, we propose a joint multiple dictionary learning method for tensor sparse coding, which explores atom correlations for sparse representation and updates multiple atoms from each mode dictionary simultaneously. In this algorithm, the Frequent-Pattern Tree (FP-tree) mining algorithm is employed to exploit frequent atom patterns in the sparse representation. Inspired by the idea of K-SVD, we develop a new dictionary update method that jointly updates elements in each pattern. Experimental results demonstrate our method outperforms other tensor based dictionary learning algorithms.