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A Volatility-Driven Asset Allocation (VDAA)

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  • Morel, Christophe
  • Michel, Thierry
  • Michel, Laurent
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    Abstract

    This article advocates a systematic rebalancing process –Volatility-Driven Asset Allocation or VDAA – for dynamically managing the strategic asset allocation. The goal of the suggested algorithm is to adjust the asset exposures so as to reflect the assumptions investors used when determining their strategic allocation, in terms of balance between risk contributions and expected returns. Such an idea makes sense from the economic point of view of a risk-adverse investor who wishes to achieve a smooth long-run performance. The stable risk contribution is determined by a long-run target, with short-term deviations from this target driving the rebalancing of the portfolio exposure. Rebalancing between asset classes allows smoothing the global volatility of the portfolio by decreasing exposure in asset classes yielding temporarily higher risk contributions and by increasing weight in asset classes with temporarily lower risk contributions. Both our backtests and robustness study demonstrate that this risk rebalancing strategy is superior in terms of information ratio to traditional rebalancing rules.

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    File URL: http://basepub.dauphine.fr/xmlui/bitstream/123456789/5954/2/VDAA.PDF
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    Bibliographic Info

    Paper provided by Paris Dauphine University in its series Economics Papers from University Paris Dauphine with number 123456789/5954.

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    Date of creation: 2010
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    Publication status: Published in CAHIER DE RECHERCHE DE DRM, 2010
    Handle: RePEc:dau:papers:123456789/5954

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    Related research

    Keywords: asset allocation; rebalancing strategy; volatility;

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    1. Marquering, W. & Verbeek, M.J.C.M., 2000. "The Economic Value of Predicting Stock Index Returns and Volatility," Discussion Paper, Tilburg University, Center for Economic Research 2000-78, Tilburg University, Center for Economic Research.
    2. Merton, Robert C., 1980. "On estimating the expected return on the market : An exploratory investigation," Journal of Financial Economics, Elsevier, Elsevier, vol. 8(4), pages 323-361, December.
    3. Jun Liu & Francis A. Longstaff & Jun Pan, 2003. "Dynamic Asset Allocation with Event Risk," Journal of Finance, American Finance Association, American Finance Association, vol. 58(1), pages 231-259, 02.
    4. Francis X. Diebold & Kamil Yılmaz, 2007. "Macroeconomic Volatility and Stock Market Volatility,World-Wide," Koç University-TUSIAD Economic Research Forum Working Papers, Koc University-TUSIAD Economic Research Forum 0711, Koc University-TUSIAD Economic Research Forum.
    5. Merton, Robert C, 1973. "An Intertemporal Capital Asset Pricing Model," Econometrica, Econometric Society, Econometric Society, vol. 41(5), pages 867-87, September.
    6. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, Econometric Society, vol. 41(1), pages 135-55, January.
    7. Fleming, Jeff & Kirby, Chris & Ostdiek, Barbara, 2003. "The economic value of volatility timing using "realized" volatility," Journal of Financial Economics, Elsevier, Elsevier, vol. 67(3), pages 473-509, March.
    8. Lu, Yang K. & Perron, Pierre, 2010. "Modeling and forecasting stock return volatility using a random level shift model," Journal of Empirical Finance, Elsevier, Elsevier, vol. 17(1), pages 138-156, January.
    9. Banerjee, Anindya & Urga, Giovanni, 2005. "Modelling structural breaks, long memory and stock market volatility: an overview," Journal of Econometrics, Elsevier, Elsevier, vol. 129(1-2), pages 1-34.
    10. Jeff Fleming, 2001. "The Economic Value of Volatility Timing," Journal of Finance, American Finance Association, American Finance Association, vol. 56(1), pages 329-352, 02.
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