Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-26T18:34:17.909Z Has data issue: false hasContentIssue false

Practical Risk Management for Equity Portfolio Managers

Published online by Cambridge University Press:  10 June 2011

G. M. Morrison
Affiliation:
Commerzbank Securities, 60 Gracechurch Street, London EC3V 0HR, U.K., Tel: +44 (0)20-7653-7642, Fax: +44 (0)20-7645-7442, Email: g.m.morrison.70@cantab.net

Abstract

The paper highlights the role of risk budgeting — how risk is ‘spent’ — in the investment management process and some of the practical issues encountered. Risk budgeting has received a great deal of interest from the investment management community recently, but no clear consensus has emerged on how it should be implemented. In this paper we outline a pragmatic risk budgeting method that can be applied at the portfolio level, and show that it can produce superior results when used in conjunction with cluster analysis techniques. There are practical implications for chief investment officers and chief executive officers on how they allocate human resources and capital in the investment management process.

A statistical factor model for stock returns is used to build a risk model of the market that separates the factor components (representing the market, investment themes and styles) and the stock specific component. Then cluster analysis techniques provide a visualisation of the changing risk structure of the market. Natural groupings of stocks emerge within the market often different to the classical industrial classification systems widely used today. These natural groupings clearly change over time reflecting the changing nature of equity markets, e.g. these techniques show very clearly the emergence of the telecommunications, media, technology phenomenon in the late 1990s and its subsequent demise in early 2001.

Using the framework of a statistical factor model, risk budgets can be aggregated or dis-aggregated. Aggregation can be to country, sector or any other group. Dis-aggregation will be to common factors (e.g. the market, growth, value and other styles) and stock specific factors, derived from a multi-factor model.

Type
Sessional meetings: papers and abstracts of discussions
Copyright
Copyright © Institute and Faculty of Actuaries 2003

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Dempster, A.P., Laird, N.M. & Rubin, D.B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 39B, 138.Google Scholar
Everitt, B. (1974). Cluster analysis. London U.K., Heinemann Educational Books.Google Scholar
Markowitz, H. (1959). Portfolio selection: diversification of investments. New Haven, U.S.A., Yale University Press.Google Scholar
Parker, V.R. (editor) (2000). Managing hedge fund risk: from the seat of the practitioner — views from investors, counterparties, hedge funds and consultants. London U.K., Risk Books.Google Scholar
Rahl, L. (editor) (2002). Risk budgeting: a new approach to investing. London U.K., Risk Books.Google Scholar
Ross, S.A. (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory, 13, 341360.CrossRefGoogle Scholar
Sharpe, W.F. (1963). A simplified model for portfolio analysis. Management Science, 9, 277293.CrossRefGoogle Scholar
Sharpe, W.F. (2002). Budgeting and monitoring pension fund risk. Financial Analysts Journal, 58:5, 7486.Google Scholar
Shukla, R.K. & Trzcinka, C.A. (1990). Sequential tests of the arbitrage pricing theory: a comparison of principal components and maximum likelihood factors. Journal of Finance, 45, 15411564.Google Scholar