IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v15y2022i10p435-d926640.html
   My bibliography  Save this article

Testing of a Volatility-Based Trading Strategy Using Behavioral Modified Asset Allocation

Author

Listed:
  • Jonas Freibauer

    (Faculty of Computer Science and Mathematics, Munich University of Applied Sciences HM, 80335 Munich, Germany)

  • Silja Grawert

    (Faculty of Computer Science and Mathematics, Munich University of Applied Sciences HM, 80335 Munich, Germany)

Abstract

The performance of volatility-based trading strategies depends, among other factors, on the asset selection and the associated risk preference. For this study, we conducted a representative survey for Germany to determine the asset preferences of individuals with lower-risk and higher-risk preference. These two types of behavioral modified asset allocations (lower-risk and higher-risk) form the basis for testing our volatility-based trading strategy with different risk and loss levels. The tests are based on historical asset price data over a period of nearly the last eleven years. The goal was to historically outperform the broad market by changing various factors, such as the initial asset allocation, the asset reallocation, and the risk and loss level underlying the trading strategy. We achieve this by using the riskier initial asset allocation and applying our trading strategy with a risk and loss level of 10% each. In this case, a historical return of 326% could have been achieved with our trading strategy over the period under review.

Suggested Citation

  • Jonas Freibauer & Silja Grawert, 2022. "Testing of a Volatility-Based Trading Strategy Using Behavioral Modified Asset Allocation," JRFM, MDPI, vol. 15(10), pages 1-20, September.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:10:p:435-:d:926640
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/15/10/435/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/15/10/435/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    2. 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.
    3. Ballestra, Luca Vincenzo & Guizzardi, Andrea & Palladini, Fabio, 2019. "Forecasting and trading on the VIX futures market: A neural network approach based on open to close returns and coincident indicators," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1250-1262.
    4. Poterba, James M. & Summers, Lawrence H., 1988. "Mean reversion in stock prices : Evidence and Implications," Journal of Financial Economics, Elsevier, vol. 22(1), pages 27-59, October.
    5. 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. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.
    2. Abdmoulah, Walid, 2010. "Testing the evolving efficiency of Arab stock markets," International Review of Financial Analysis, Elsevier, vol. 19(1), pages 25-34, January.
    3. Michael Andersen & Robert Subbaraman, 1996. "Share Prices and Investment," RBA Research Discussion Papers rdp9610, Reserve Bank of Australia.
    4. Christoffersen, Peter & Jacobs, Kris & Ornthanalai, Chayawat & Wang, Yintian, 2008. "Option valuation with long-run and short-run volatility components," Journal of Financial Economics, Elsevier, vol. 90(3), pages 272-297, December.
    5. F. DePenya & L. Gil-Alana, 2006. "Testing of nonstationary cycles in financial time series data," Review of Quantitative Finance and Accounting, Springer, vol. 27(1), pages 47-65, August.
    6. Subrata ROY, 2021. "Volatility Forecasting, Market Efficiency and Effect of Recession of SRI Indices," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(2(627), S), pages 259-284, Summer.
    7. Phoebe Koundouri & Nikolaos Kourogenis & Nikitas Pittis & Panagiotis Samartzis, 2015. "Factor Models as 'Explanatory Unifiers' versus 'Explanatory Ideals' of Empirical Regularities of Stock Returns," DEOS Working Papers 1507, Athens University of Economics and Business.
    8. Bali, Turan G. & Demirtas, K. Ozgur & Levy, Haim, 2008. "Nonlinear mean reversion in stock prices," Journal of Banking & Finance, Elsevier, vol. 32(5), pages 767-782, May.
    9. Helmut Herwartz & Leonardo Morales-Arias, 2009. "In-sample and out-of-sample properties of international stock return dynamics conditional on equilibrium pricing factors," The European Journal of Finance, Taylor & Francis Journals, vol. 15(1), pages 1-28.
    10. Shi, Leilei & Wang, Binghong & Guo, Xinshuai & Li, Honggang, 2021. "A price dynamic equilibrium model with trading volume weights based on a price-volume probability wave differential equation," International Review of Financial Analysis, Elsevier, vol. 74(C).
    11. Barnett, William A. & Serletis, Apostolos & Serletis, Demitre, 2015. "Nonlinear And Complex Dynamics In Economics," Macroeconomic Dynamics, Cambridge University Press, vol. 19(8), pages 1749-1779, December.
    12. Narayan, Paresh Kumar & Liu, Ruipeng & Westerlund, Joakim, 2016. "A GARCH model for testing market efficiency," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 41(C), pages 121-138.
    13. Tran Van Quang, 2007. "Testování slabé formy efektivnosti na českém akciovém trhu [Testing the weak form of efficient market hypothesis for the czech stock market]," Politická ekonomie, Prague University of Economics and Business, vol. 2007(6), pages 751-772.
    14. 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.
    15. Ajwa, Martine Therese, 1995. "Technical trading patterns: can they truly predict price movements and can they be exploited for excess returns?," ISU General Staff Papers 1995010108000011754, Iowa State University, Department of Economics.
    16. Kin Lam & Li Wei, "undated". "Optimal Trading Strategy When Return Process is AR(1)," Computing in Economics and Finance 1997 16, Society for Computational Economics.
    17. Mouck, T., 1998. "Capital markets research and real world complexity: The emerging challenge of chaos theory," Accounting, Organizations and Society, Elsevier, vol. 23(2), pages 189-203, February.
    18. Nelson Manuel Areal & Manuel Jose Da Rocha Armada, 2002. "The long-horizon returns behaviour of the Portuguese stock market1," The European Journal of Finance, Taylor & Francis Journals, vol. 8(1), pages 93-122.
    19. 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.
    20. Attila Odabasl & Celal Asku & Vedat Akgiray, 2004. "The statistical evolution of prices on the Istanbul stock exchange," The European Journal of Finance, Taylor & Francis Journals, vol. 10(6), pages 510-525.

    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:gam:jjrfmx:v:15:y:2022:i:10:p:435-:d:926640. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.