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Generalized Risk Premia

Author

Listed:
  • Paul SCHNEIDER

    (University of Lugano and Swiss Finance Institute)

Abstract

This paper develops an optimal trading strategy explicitly linked to an agent's preferences and assessment of the distribution of asset returns. The price of this strategy is a portfolio of implied moments, and its expected excess returns naturally accommodate compensation for higher-order moment risk. Variance risk and the equity premium approximate it to first order and it nests cross-sectional asset pricing models such as the CAPM. An empirical study in the US index market compares the investment behavior of an agent with recursive long-run risk preferences to one who merely uses an i.i.d. time series model and takes market prices as given. The two agents exhibit very similar behavior during crises and can be distinguished mostly during calm periods.

Suggested Citation

  • Paul SCHNEIDER, 2014. "Generalized Risk Premia," Swiss Finance Institute Research Paper Series 14-29, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp1429
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    File URL: http://ssrn.com/abstract=2188349
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    Cited by:

    1. Fousseni Chabi-Yo & Chukwuma Dim & Grigory Vilkov, 2023. "Generalized Bounds on the Conditional Expected Excess Return on Individual Stocks," Management Science, INFORMS, vol. 69(2), pages 922-939, February.
    2. Barletta, Andrea & Santucci de Magistris, Paolo & Violante, Francesco, 2019. "A non-structural investigation of VIX risk neutral density," Journal of Banking & Finance, Elsevier, vol. 99(C), pages 1-20.
    3. Hautsch, Nikolaus & Scheuch, Christoph & Voigt, Stefan, 2018. "Limits to arbitrage in markets with stochastic settlement latency," CFS Working Paper Series 616, Center for Financial Studies (CFS).
    4. Paul Schneider & Christian Wagner & Josef Zechner, 2020. "Low‐Risk Anomalies?," Journal of Finance, American Finance Association, vol. 75(5), pages 2673-2718, October.
    5. Xue Jiang & Liyan Han & Yang Xu, 2021. "How does skewness perform in the Chinese commodity futures market?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(8), pages 1268-1285, August.
    6. Ebert, Sebastian & Hilpert, Christian, 2019. "Skewness preference and the popularity of technical analysis," Journal of Banking & Finance, Elsevier, vol. 109(C).
    7. Koëter, Joren, 2021. "Essays on asset pricing, investor preferences, and derivative markets," Other publications TiSEM 9e88a66e-b972-4af3-91d6-0, Tilburg University, School of Economics and Management.
    8. George Chalamandaris & Leonidas S. Rompolis, 2021. "Recovering the market risk premium from higher‐order moment risks," European Financial Management, European Financial Management Association, vol. 27(1), pages 147-186, January.
    9. Nikolaus Hautsch & Christoph Scheu & Stefan Voigt, 2024. "Building trust takes time: limits to arbitrage for blockchain-based assets," Review of Finance, European Finance Association, vol. 28(4), pages 1345-1381.
    10. Barletta, Andrea & Santucci de Magistris, Paolo & Sloth, David, 2019. "It only takes a few moments to hedge options," Journal of Economic Dynamics and Control, Elsevier, vol. 100(C), pages 251-269.
    11. Carbajal-De-Nova, Carolina & Venegas-Martínez, Francisco, 2019. "On the paradigm shift of asset pricing models, before and after the global financial crisis: a literature review," Panorama Económico, Escuela Superior de Economía, Instituto Politécnico Nacional, vol. 15(29), pages 7-38, Primer se.
    12. Brinkmann, Felix & Korn, Olaf, 2014. "Risk-adjusted option-implied moments," CFR Working Papers 14-07, University of Cologne, Centre for Financial Research (CFR).
    13. Hoyong Choi & Philippe Mueller & Andrea Vedolin, 2017. "Bond Variance Risk Premiums," Review of Finance, European Finance Association, vol. 21(3), pages 987-1022.
    14. Felix Brinkmann & Olaf Korn, 2018. "Risk-adjusted option-implied moments," Review of Derivatives Research, Springer, vol. 21(2), pages 149-173, July.
    15. Cui, Zhenyu & Lars Kirkby, J. & Nguyen, Duy, 2017. "A general framework for discretely sampled realized variance derivatives in stochastic volatility models with jumps," European Journal of Operational Research, Elsevier, vol. 262(1), pages 381-400.

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    Keywords

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    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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