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A Volatility-Driven Asset Allocation (VDAA)

  • Morel, Christophe
  • Michel, Thierry
  • Michel, Laurent
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    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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    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
    Contact details of provider: Web page: http://www.dauphine.fr/en/welcome.html

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    1. Wessel Marquering & Marno Verbeek, 2000. "The Economic Value of Predicting Stock Index Returns and Volatility," Center for Economic Studies - Discussion papers ces0020, Katholieke Universiteit Leuven, Centrum voor Economische Studiën.
    2. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-55, January.
    3. Jun Liu & Francis A. Longstaff & Jun Pan, 2002. "Dynamic Asset Allocation With Event Risk," NBER Working Papers 9103, National Bureau of Economic Research, Inc.
    4. Francis X. Diebold & Kamil Yılmaz, 2007. "Macroeconomic Volatility and Stock Market Volatility,World-Wide," Koç University-TUSIAD Economic Research Forum Working Papers 0711, Koc University-TUSIAD Economic Research Forum.
    5. Merton, Robert C, 1973. "An Intertemporal Capital Asset Pricing Model," Econometrica, Econometric Society, vol. 41(5), pages 867-87, September.
    6. Robert C. Merton, 1980. "On Estimating the Expected Return on the Market: An Exploratory Investigation," NBER Working Papers 0444, National Bureau of Economic Research, Inc.
    7. Fleming, Jeff & Kirby, Chris & Ostdiek, Barbara, 2003. "The economic value of volatility timing using "realized" volatility," Journal of Financial Economics, Elsevier, vol. 67(3), pages 473-509, March.
    8. Yang K. Lu & Pierre Perron, 2008. "Modeling and Forecasting Stock Return Volatility Using a Random Level Shift Model," Boston University - Department of Economics - Working Papers Series wp2008-012, Boston University - Department of Economics.
    9. Banerjee, Anindya & Urga, Giovanni, 2005. "Modelling structural breaks, long memory and stock market volatility: an overview," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 1-34.
    10. Jeff Fleming, 2001. "The Economic Value of Volatility Timing," Journal of Finance, American Finance Association, vol. 56(1), pages 329-352, 02.
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