Managing Credit Risk in Corporate Bond Portfolios : A Practitioner's ...

ForewordSome of the greatest advances in finance over the past two to threedecades have come in the field of risk management. Theoretical developmentshave enabled us to disaggregate risk elements and thus better identifyand price risk factors. New instruments have been created to enable practitionersto more actively manage their risk profiles by shedding those exposuresthey are not well placed to hold while retaining (or leveraging) thosethat reflect their comparative advantage. The practical consequence is thatthe market for risk management instruments has grown exponentially.These instruments are now actively used by all categories of institution andportfolio managers.Partly as a result of this, the business of portfolio management hasbecome enormously more competitive. Falling interest rates have motivatedclients to be more demanding in their search for yield. But it would probablyhave happened anyway. Institutional investors are continuously seekinga more efficient risk–return combination as well as deciding exactly whereon the risk–return frontier they wish to position themselves. All thisrequires constant refinement of portfolio management techniques to keepup with evolving best practice.The basic insights behind the new techniques of risk managementdepend on mathematical innovations. The sophistication of the emergingmethodology has important strengths, but it also has limitations. The keystrength is analytic rigor. This rigor, coupled with the computational powerof modern information technology, allows portfolio managers to quicklyassess the risk characteristics of an individual instrument as well as measureits impact on the overall risk structure of a portfolio.The opposite side of the coin to analytic rigor is the complexity of themodels used. This complexity opens a gap between the statistical measurementof risk and the economic intuition that lies behind it. This would notmatter too much if models could always be relied on to produce the “right’’results. After all, we do not need to understand internal combustion orhydraulic braking to drive a car. Most of the time, of course, models do producemore or less the right answers. However, in times of stress, we becomeaware of two key limitations. First, because statistical applications must bebased on available data, they implicitly assume that the past is a good guideto the future. In extreme circumstances, that assumption may break down.xi