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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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Suggested Citation

  • Ole Peters, 2011. "Optimal leverage from non-ergodicity," Quantitative Finance, Taylor & Francis Journals, vol. 11(11), pages 1593-1602.
  • Handle: RePEc:taf:quantf:v:11:y:2011:i:11:p:1593-1602
    DOI: 10.1080/14697688.2010.513338
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    Cited by:

    1. Ole Peters & Murray Gell-Mann, 2014. "Evaluating gambles using dynamics," Papers 1405.0585, arXiv.org, revised Jun 2015.
    2. Viktor Stojkoski & Trifce Sandev & Lasko Basnarkov & Ljupco Kocarev & Ralf Metzler, 2020. "Generalised geometric Brownian motion: Theory and applications to option pricing," Papers 2011.00312, arXiv.org.
    3. Zhao, Shengli, 2024. "Objective acceptability indexes," International Review of Financial Analysis, Elsevier, vol. 95(PC).
    4. Simon Gluzman, 2023. "Market Crashes and Time-Translation Invariance," FinTech, MDPI, vol. 2(2), pages 1-27, March.
    5. Marco Molinari & Victor Shao & Luca Imeneo & Mateusz Mikolajczak & Vladimir Tregubiak & Abhimanyu Pandey & Sebastian Kuznetsov Ryder Torres Pereira, 2024. "Interpretable Company Similarity with Sparse Autoencoders," Papers 2412.02605, arXiv.org, revised May 2025.
    6. 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.
    7. Matej Uhr'in & Gustav v{S}ourek & Ondv{r}ej Hub'av{c}ek & Filip v{Z}elezn'y, 2021. "Optimal sports betting strategies in practice: an experimental review," Papers 2107.08827, arXiv.org.
    8. J. Doyne Farmer & Spyros Skouras, 2013. "An ecological perspective on the future of computer trading," Quantitative Finance, Taylor & Francis Journals, vol. 13(3), pages 325-346, February.
    9. Rosella Giacometti & Sergio Ortobelli & Tomáš Tichý, 2015. "Portfolio Selection with Uncertainty Measures Consistent with Additive Shifts," Prague Economic Papers, Prague University of Economics and Business, vol. 2015(1), pages 3-16.
    10. Risto Heikkinen & Juha Karvanen & Kaisa Miettinen, 2025. "A Bayesian model for portfolio decisions based on debiased and regularized expert predictions," Journal of Business Economics, Springer, vol. 95(5), pages 669-706, July.
    11. Mihail Turlakov, 2016. "Leverage and Uncertainty," Papers 1612.07194, arXiv.org.
    12. El Mouden, Claire, 2013. "The Sciences Of Risk: Implications For Regulation Of The Financial Sector," INET Oxford Working Papers 2013-01, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
    13. Bell, Peter Newton, 2014. "Properties of time averages in a risk management simulation," MPRA Paper 55803, University Library of Munich, Germany.
    14. Smirnov Alexander D., 2018. "Stochastic Logistic Model of the Global Financial Leverage," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 18(1), pages 1-20, January.
    15. Mariam Thalos & Oliver Richardson, 2014. "Capitalization in the St. Petersburg game," Politics, Philosophy & Economics, , vol. 13(3), pages 292-313, August.

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