The predictive ability of closed-end value-at-risk model on changes to portfolio composition for selected investment intermediaries in Kenya.
Managers are confronted with the need to continuously shift resources among competing uses. The interest is to ensure allocation to the best venture. A good risk measurement tool affords meaningful estimation of exposure of earnings to lose among competing venture hence facilitating strategic resource allocation. A proper risk measurement tool also aids in evaluation of management performance and competencies- indirectly providing an 'index on efficient resource management Modern portfolio theory suggests that the risk in a portfolio can be proxied by the portfolio standard deviation. This means the latter is all that one needs to; i) encapsulate all the information about risk that is relevant and ii) construct risk- based rules for optimal risk "management" decisions. However, in reality, managers think of risk in terms of dollars of loss, not deviations. Standard deviation is therefore not intuitive. Value at Risk methodologies present information about the distribution of possible future losses on a portfolio. From such distribution, the loss exposure of a portfolio at any time can be calculated and used by management to make decisions on the propriety of portfolio composition. This study attempted to test the predictive ability of one such model among Kenyan financial Intermediaries and in the process to also establish the methods used by those institutions to determine the timing for portfolio composition changes. The study found that internally developed models are the most popular means by which management decides on portfolio composition. These models are unstructured and change from time to time. The methods applied are tailored to conform to the regulatory framework within which these institutions operate. Value at Risk models are little known among Financial Intermediaries in Kenya. The study found Closed end VaR model to be a good predictor of portfolio composition changes among financial intermediaries in Kenya. Most companies however tend to continue applying own tailor made approaches which tend to be rule of thumb based in determining portfolio composition