Concepts of Portfolio Management; Part 1: Measuring and Controlling Risk. (Oldies, Still Goodies)

Article excerpt

Part 1 of this two-part article presents a nontechnical profile of portfolio management concepts with emphasis on their use as an important risk management tool. Part 2, with additional information about the model, will appear in the May issue.

The principles of portfolio management have been around for a long time, whether they were applied by the Rothschilds, the Medici, or the moneylenders of biblical times. This article is a nontechnical profile of the concepts of portfolio management being explored and implemented by banks around the country. These concepts apply to a broad spectrum of banks, not necessarily to any particular bank. To present some of the important issues and elements associated with portfolio management, we are writing as traditional bankers interested in an important risk management tool, not as experts in the quantitative analysis used in creating the tool.

Definitions

While many banks have differing definitions, for our purposes portfolio management is the identification of measured risk exposures that are controlled in the context of defined risk tolerance. Other acceptable definitions include managing a portfolio of assets to achieve the best possible mix as distinct from managing individual assets within the portfolio. Some others may prefer describing portfolio management as a tool to minimize aggregate risk problems while achieving or exceeding hurdle returns for risks the lender or investor is willing to accept. Whichever definition is selected, it must incorporate the concepts of risk aggregation in relationship to some sort of measured standard.

Background

Historically, banks, more often than not, have limited their efforts to reliance on legal lending limits, imposition of house lending limits, and occasional restriction of loan exposures to selected industry segments. The management process, in the implementation of these limits, usually has relied on intuition, bias, and the bitter experience of senior management.

The weaknesses of this historical or traditional approach are inadequate definition of risk measurement and risk tolerance, as well as vulnerability to unknown concentrations, if not loss. Another unfortunate byproduct can be rejection of acceptable business.

Modern portfolio management is based on four pillars: information, process, policy, and the model. It is important to understand the symbiotic relationship of the four pillars. To do so, it may be useful to picture a roof held up by four columns. To keep the roof level, assuming the ground is level, the columns must be the same height.

For example, there is no point in having a highly sophisticated and complex model on a powerful computer if the bank's information systems cannot provide the necessary information. There is no point in having a complicated policy if the model is simplistic or no process is in place to make decisions. The awareness that the roof of portfolio management can only bc raised by the careful integration of the four pillars is all-important to establishing and improving this risk control tool in any bank.

Four Dimensions

In most cases, banks that have gone beyond the historical approach are conducting risk management based on one, two, or three dimensions applied to loans or near-loan risks, as defined by risk rating and tenor. These basic dimensions are usually business or industry, products, and geography. The problem with this approach is the critical absence of a fourth dimension: dynamic change factors.

The three-dimensional approach can provide only a snapshot of the current situation or one frame in a movie. Without the full picture, the bank must react as if the environment is static, which experience shows often does not allow sufficient flexibility to avoid loss. The fourth dimension allows management to act against a perception of future possibility.

Dynamic change factors (described later in some detail) are possible future events that change risk rating and loss likelihood and cut across the three basic dimensions. …