IDEAS home Printed from
   My bibliography  Save this paper

Structural positions and risk budgeting - Quantifying the impact of structural positions and deriving implications for active portfolio management


  • Ulf Herold


  • Raimond Maurer



Structural positions are very common in investment practice. A structural position is defined as a permanent overweighting of a riskier asset class relative to a prespecified benchmark portfolio. The most prominent example for a structural position is the equity bias in a balanced fund that arises by consistently overweighting equities in tactical asset allocation. Another example is the permanent allocation of credit in a fixed income portfolio with a government benchmark. The analysis provided in this article shows that whenever possible, structural positions should be avoided. Graphical illustrations based on Pythagorean theorem are used to make a connection between the active risk/return and the total risk/return framework. Structural positions alter the risk profile of the portfolio substantially, and the appeal of active management – to provide active returns uncorrelated to benchmark returns and hence to shift the efficient frontier outwards – gets lost. The article demonstrates that the commonly used alpha – tracking error criterion is not sufficient for active management. In addition, structural positions complicate measuring managers’ skill. The paper also develops normative implications for active portfolio management. Tactical asset allocation should be based on the comparison of expected excess returns of an asset class to the equilibrium risk premium of the same asset class and not to expected excess returns of other asset classes. For the cases, where structural positions cannot be avoided, a risk budgeting approach is introduced and applied to determine the optimal position size. Finally, investors are advised not to base performance evaluation only on simple manager rankings because this encourages managers to take structural positions and does not reward efforts to produce alpha. The same holds true for comparing managers’ information ratios. Information ratios, in investment practice defined as the ratio of active return to active risk, do not uncover structural positions.

Suggested Citation

  • Ulf Herold & Raimond Maurer, 2008. "Structural positions and risk budgeting - Quantifying the impact of structural positions and deriving implications for active portfolio management," Working Paper Series: Finance and Accounting 74, Department of Finance, Goethe University Frankfurt am Main.
  • Handle: RePEc:fra:franaf:74

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Danielsson, J. & Payne, R., 2002. "Real trading patterns and prices in spot foreign exchange markets," Journal of International Money and Finance, Elsevier, vol. 21(2), pages 203-222, April.
    2. Gonzalo, Jesus & Granger, Clive W J, 1995. "Estimation of Common Long-Memory Components in Cointegrated Systems," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 27-35, January.
    3. Paruolo, Paolo, 2002. "Asymptotic Inference On The Moving Average Impact Matrix In Cointegrated I (2) Var Systems," Econometric Theory, Cambridge University Press, vol. 18(03), pages 673-690, June.
    4. Wang, Steven Shuye & Meng Rui, Oliver & Firth, Michael, 2002. "Return and volatility behavior of dually-traded stocks: the case of Hong Kong," Journal of International Money and Finance, Elsevier, vol. 21(2), pages 265-293, April.
    5. de Jong, Frank, 2002. "Measures of contributions to price discovery: a comparison," Journal of Financial Markets, Elsevier, vol. 5(3), pages 323-327, July.
    6. Cheol S. Eun & Sanjiv Sabherwal, 2003. "Cross-Border Listings and Price Discovery: Evidence from U.S.-Listed Canadian Stocks," Journal of Finance, American Finance Association, vol. 58(2), pages 549-576, April.
    7. Melvin, Michael, 2003. "A stock market boom during a financial crisis?: ADRs and capital outflows in Argentina," Economics Letters, Elsevier, vol. 81(1), pages 129-136, October.
    8. Granger, C. W. J., 1988. "Some recent development in a concept of causality," Journal of Econometrics, Elsevier, vol. 39(1-2), pages 199-211.
    9. Kim, Minho & Szakmary, Andrew C. & Mathur, Ike, 2000. "Price transmission dynamics between ADRs and their underlying foreign securities," Journal of Banking & Finance, Elsevier, vol. 24(8), pages 1359-1382, August.
    10. Chen, Song Xi, 1999. "Beta kernel estimators for density functions," Computational Statistics & Data Analysis, Elsevier, vol. 31(2), pages 131-145, August.
    11. Shleifer, Andrei & Vishny, Robert W, 1997. " The Limits of Arbitrage," Journal of Finance, American Finance Association, vol. 52(1), pages 35-55, March.
    12. Li, Hongyi & Maddala, G. S., 1997. "Bootstrapping cointegrating regressions," Journal of Econometrics, Elsevier, vol. 80(2), pages 297-318, October.
    13. Lau, Sie Ting & Diltz, J. David, 1994. "Stock returns and the transfer of information between the New York and Tokyo stock exchanges," Journal of International Money and Finance, Elsevier, vol. 13(2), pages 211-222, April.
    14. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 39(3), pages 106-135.
    15. Hasbrouck, Joel, 1995. " One Security, Many Markets: Determining the Contributions to Price Discovery," Journal of Finance, American Finance Association, vol. 50(4), pages 1175-1199, September.
    16. Lehmann, Bruce N., 2002. "Some desiderata for the measurement of price discovery across markets," Journal of Financial Markets, Elsevier, vol. 5(3), pages 259-276, July.
    17. Theissen, Erik, 2002. "Price discovery in floor and screen trading systems," Journal of Empirical Finance, Elsevier, vol. 9(4), pages 455-474, November.
    18. Lieberman, Offer & Ben-Zion, Uri & Hauser, Shmuel, 1999. "A characterization of the price behavior of international dual stocks: an error correction approach," Journal of International Money and Finance, Elsevier, vol. 18(2), pages 289-304, February.
    19. Paruolo, Paolo, 1997. "Standard Errors for the Long-Run Variance Matrix," Econometric Theory, Cambridge University Press, vol. 13(02), pages 305-306, April.
    20. Baillie, Richard T. & Geoffrey Booth, G. & Tse, Yiuman & Zabotina, Tatyana, 2002. "Price discovery and common factor models," Journal of Financial Markets, Elsevier, vol. 5(3), pages 309-321, July.
    21. Warren Bailey & Kalok Chan & Y. Peter Chung, 2000. "Depositary Receipts, Country Funds, and the Peso Crash: The Intraday Evidence," Journal of Finance, American Finance Association, vol. 55(6), pages 2693-2717, December.
    22. Karolyi, G. Andrew, 2003. "DaimlerChrysler AG, the first truly global share," Journal of Corporate Finance, Elsevier, vol. 9(4), pages 409-430, September.
    23. Ding, David K. & Harris, Frederick H. deB. & Lau, Sie Ting & McInish, Thomas H., 1999. "An investigation of price discovery in informationally-linked markets: equity trading in Malaysia and Singapore," Journal of Multinational Financial Management, Elsevier, vol. 9(3-4), pages 317-329, November.
    24. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-1580, November.
    25. Harris, Frederick H. deB. & McInish, Thomas H. & Wood, Robert A., 2002. "Common factor components versus information shares: a reply," Journal of Financial Markets, Elsevier, vol. 5(3), pages 341-348, July.
    Full references (including those not matched with items on IDEAS)

    More about this item


    active management; structural positions; information ratios; Pythagorean theorem; risk budgeting; tactical asset allocation;

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fra:franaf:74. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Reinhard H. Schmidt). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.