An empirical investigation of sector correlation forecasting techniques and the potential benefits to investors

• Main Author: Metcalfe, Stephen Fraser
• Format: Others
• Language: en
• Published: 2009
• Online Access: http://hdl.handle.net/10539/6848

Financial modelling is of considerable value to portfolio management. The effectiveness of different methods of forecasting correlation between sub-sectors, as part of the sector-allocation stage of the portfolio-construction process, has not yet been investigated. This focus is useful since it is relatively practical to collect data pertaining to sector and sub-sector indices, and hence the calculation of figures necessary to determine their investment performance is simpler. The aim of this research paper was to examine the performance of various correlation estimation techniques under two assessment criteria and to identify, if possible, the most suitable methods to employ in the sub-sector allocation stage of the ‘top-down’ approach to portfolio construction. Monthly total returns were calculated for each of the market indices, the sectors and their sub-sectors from the relevant total return indices as part of the analysis. The first assessment criterion was the statistical performance of the methods, which measured their ability to estimate future correlation coefficients between different sub-sectors by analysing the distributions of their absolute forecast errors. The second assessment criterion was the economic performance of the forecast methods. MPT was used to select the optimal portfolios for certain levels of expected return and the economic performance of the efficient sub-sector allocations, selected using the different correlation estimation techniques, was then evaluated. The two models used to estimate correlation that stood out from the rest in terms of their overall performance were the full HCM model and the industry mean model. From the perspective of the statistical performance criterion, the industry mean model consistently performed the best and the full HCM model also performed well. The economic performance of all the models tested, with the exception of the overall mean model, outperformed the passive investment strategy of holding the market portfolio. The economic performance of the full HCM model was best overall and that of the industry mean model was also strong. Prior research has found that the industry mean model is useful in forecasting future correlation between individual shares. This research found that the industry mean model also has value in forecasting future correlation between sub-sectors. Furthermore, despite demonstration in prior research of the full HCM model’s poor ability to estimate future correlation between individual shares, it was one of the most effective models at forecasting correlation between sub-sectors. Both of these models therefore hold value to investors for the purposes of sub-sector allocation as part of a top-down approach to financial portfolio construction.