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Optimal leverage from non-ergodicity

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  • Ole Peters

Abstract

In modern portfolio theory, the balancing of expected returns on investments against uncertainties in those returns is aided by the use of utility functions. The Kelly criterion offers another approach, rooted in information theory, that always implies logarithmic utility. The two approaches seem incompatible, too loosely or too tightly constraining investors' risk preferences, from their respective perspectives. The conflict can be understood on the basis that the multiplicative models used in both approaches are non-ergodic which leads to ensemble-average returns differing from time-average returns in single realizations. The classic treatments, from the very beginning of probability theory, use ensemble-averages, whereas the Kelly-result is obtained by considering time-averages. Maximizing the time-average growth rates for an investment defines an optimal leverage, whereas growth rates derived from ensemble-average returns depend linearly on leverage. The latter measure can thus incentivize investors to maximize leverage, which is detrimental to time-average growth and overall market stability. The Sharpe ratio is insensitive to leverage. Its relation to optimal leverage is discussed. A better understanding of the significance of time-irreversibility and non-ergodicity and the resulting bounds on leverage may help policy makers in reshaping financial risk controls.

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  • Ole Peters, 2009. "Optimal leverage from non-ergodicity," Papers 0902.2965, arXiv.org, revised Aug 2010.
  • Handle: RePEc:arx:papers:0902.2965
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    References listed on IDEAS

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    1. J. Tobin, 1958. "Liquidity Preference as Behavior Towards Risk," Review of Economic Studies, Oxford University Press, vol. 25(2), pages 65-86.
    2. Harry M. Markowitz, 2011. "Investment for the Long Run: New Evidence for an Old Rule," World Scientific Book Chapters,in: THE KELLY CAPITAL GROWTH INVESTMENT CRITERION THEORY and PRACTICE, chapter 35, pages 495-508 World Scientific Publishing Co. Pte. Ltd..
    3. Allan G. Timmermann, 1993. "How Learning in Financial Markets Generates Excess Volatility and Predictability in Stock Prices," The Quarterly Journal of Economics, Oxford University Press, vol. 108(4), pages 1135-1145.
    4. Merton, Robert C. & Samuelson, Paul A., 1974. "Fallacy of the log-normal approximation to optimal portfolio decision-making over many periods," Journal of Financial Economics, Elsevier, vol. 1(1), pages 67-94, May.
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    Cited by:

    1. repec:bpj:bejtec:v:18:y:2018:i:1:p:20:n:3 is not listed on IDEAS
    2. Ole Peters & Murray Gell-Mann, 2014. "Evaluating gambles using dynamics," Papers 1405.0585, arXiv.org, revised Jun 2015.
    3. Stefan Thurner & J. Doyne Farmer & John Geanakoplos, 2012. "Leverage causes fat tails and clustered volatility," Quantitative Finance, Taylor & Francis Journals, vol. 12(5), pages 695-707, February.
    4. Rosella Giacometti & Sergio Ortobelli & Tomáš Tichý, 2015. "Portfolio Selection with Uncertainty Measures Consistent with Additive Shifts," Prague Economic Papers, University of Economics, Prague, vol. 2015(1), pages 3-16.
    5. Bell, Peter Newton, 2014. "Properties of time averages in a risk management simulation," MPRA Paper 55803, University Library of Munich, Germany.
    6. Mihail Turlakov, 2016. "Leverage and Uncertainty," Papers 1612.07194, arXiv.org.

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