IDEAS home Printed from https://ideas.repec.org/a/spr/metrik/v21y2010i1p59-80.html
   My bibliography  Save this article

Auswirkungen der Risikomessmethode auf die Anlageperformance – Eine empirische Untersuchung für den Fall definierter Risikolimite in der Planungsrechnung anhand des DAX

Author

Listed:
  • Michael Pohl

Abstract

Risikomessmodelle werden als Controllinginstrumente hinsichtlich ihrer Güte oft einseitig unter reinen Risikogesichtspunkten getestet. Der Einfluss, den sie auf die Anlageperformance besitzen, wird dabei in der Regel vernachlässigt. Der vorliegende Beitrag zeigt den konzeptionellen Zusammenhang zwischen Risikomessmodellen und Anlageperformance auf und weist ihn empirisch nach. Dabei wird deutlich, dass die Anwendung von Normalverteilungsannahme, historischer Simulation und impliziter Volatilität zur Risikomessung im Rahmen von Limitsystemen zu deutlich unterschiedlichen Portfoliorenditen bei gleichem Portfoliorisiko führen kann. In einer Betrachtung des Risikomanagements aus Performancesicht kann somit erhebliches Potential liegen. Copyright Springer-Verlag 2010

Suggested Citation

  • Michael Pohl, 2010. "Auswirkungen der Risikomessmethode auf die Anlageperformance – Eine empirische Untersuchung für den Fall definierter Risikolimite in der Planungsrechnung anhand des DAX," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 21(1), pages 59-80, June.
    • Handle: RePEc:spr:metrik:v:21:y:2010:i:1:p:59-80
    DOI: 10.1007/s00187-010-0087-2
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00187-010-0087-2
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s00187-010-0087-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Edward Zaik & John Walter & Gabriela Retting & Christopher James, 1996. "Raroc At Bank Of America: From Theory To Practice," Journal of Applied Corporate Finance, Morgan Stanley, vol. 9(2), pages 83-93, June.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Dowd, Kevin, 2000. "Adjusting for risk:: An improved Sharpe ratio," International Review of Economics & Finance, Elsevier, vol. 9(3), pages 209-222, July.
    4. GIOT, Pierre, 2005. "Implied volatility indexes and daily Value at Risk models," LIDAM Reprints CORE 1840, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    6. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    7. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Catania, Leopoldo & Proietti, Tommaso, 2020. "Forecasting volatility with time-varying leverage and volatility of volatility effects," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1301-1317.
    2. Dicle, Mehmet F. & Levendis, John, 2020. "Historic risk and implied volatility," Global Finance Journal, Elsevier, vol. 45(C).
    3. Borkowski, Bolesław & Krawiec, Monika & Shachmurove, Yochanan, 2013. "Impact of volatility estimation method on theoretical option values," Global Finance Journal, Elsevier, vol. 24(2), pages 119-128.
    4. Boleslaw Borkowski & Monika Krawiec & Yochanan Shachmurove, 2013. "Modeling and Estimating Volatility of Options on Standard & Poor’s 500 Index," PIER Working Paper Archive 13-015, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    5. Jovanovic, Franck & Schinckus, Christophe, 2017. "Econophysics and Financial Economics: An Emerging Dialogue," OUP Catalogue, Oxford University Press, number 9780190205034.
    6. Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2019. "Forecasting the KOSPI200 spot volatility using various volatility measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 156-166.
    7. Judy Hsu & Kuo-An Li, 2013. "Performance assessments of Taiwan’s financial holding companies," Journal of Productivity Analysis, Springer, vol. 40(1), pages 137-151, August.
    8. Shi, Leilei, 2006. "Does security transaction volume–price behavior resemble a probability wave?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 366(C), pages 419-436.
    9. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2015. "Wave function method to forecast foreign currencies exchange rates at ultra high frequency electronic trading in foreign currencies exchange markets," MPRA Paper 67470, University Library of Munich, Germany.
    10. Robert Ślepaczuk & Grzegorz Zakrzewski, 2009. "High-Frequency and Model-Free Volatility Estimators," Working Papers 2009-13, Faculty of Economic Sciences, University of Warsaw.
    11. Samet Günay & Yanlin Shi, 2016. "Long-Memory in Volatilities of CDS Spreads: Evidences from the Emerging Markets," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 122-137, March.
    12. Subbotin, Alexandre, 2009. "Volatility Models: from Conditional Heteroscedasticity to Cascades at Multiple Horizons," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 15(3), pages 94-138.
    13. Alagidede, Paul & Panagiotidis, Theodore, 2009. "Modelling stock returns in Africa's emerging equity markets," International Review of Financial Analysis, Elsevier, vol. 18(1-2), pages 1-11, March.
    14. Chia-Lin Chang & Shu-Han Hsu & Michael McAleer, 2018. "An Event Study Analysis of Political Events, Disasters, and Accidents for Chinese Tourists to Taiwan," Sustainability, MDPI, vol. 10(11), pages 1-77, November.
    15. Stentoft, Lars, 2005. "Pricing American options when the underlying asset follows GARCH processes," Journal of Empirical Finance, Elsevier, vol. 12(4), pages 576-611, September.
    16. Nathan Jensen, 2007. "International institutions and market expectations: Stock price responses to the WTO ruling on the 2002 U.S. steel tariffs," The Review of International Organizations, Springer, vol. 2(3), pages 261-280, September.
    17. Yok-Yong Lee & M. H. Yahya & A. M. Bany-Ariffin & S. Aslam, 2018. "Leverage Effect and Switching of Market Efficiency Post Goods and Services Tax (GST) Imposition," International Business Research, Canadian Center of Science and Education, vol. 11(3), pages 162-178, March.
    18. Bauer, Rob M M J & Nieuwland, Frederick G M C & Verschoor, Willem F C, 1994. "German Stock Market Dynamics," Empirical Economics, Springer, vol. 19(3), pages 397-418.
    19. Dutta, Shantanu & Essaddam, Naceur & Kumar, Vinod & Saadi, Samir, 2017. "How does electronic trading affect efficiency of stock market and conditional volatility? Evidence from Toronto Stock Exchange," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 867-877.
    20. Lars Stentoft, 2008. "American Option Pricing Using GARCH Models and the Normal Inverse Gaussian Distribution," Journal of Financial Econometrics, Oxford University Press, vol. 6(4), pages 540-582, Fall.

    Corrections

    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:spr:metrik:v:21:y:2010:i:1:p:59-80. See general information about how to correct material in RePEc.

    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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.