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Implicit quantiles and expectiles

Author

Listed:
  • Fabio Bellini

    (University of Milano-Bicocca)

  • Edit Rroji

    (University of Milano-Bicocca)

  • Carlo Sala

    (University Ramon Llull, ESADE)

Abstract

We compute nonparametric and forward-looking option-implied quantile and expectile curves, and we study their properties on a 5-year dataset of weekly options written on the S&P 500 Index. After studying the dynamics of the single curves and their joint behaviour, we investigate the potentiality of these quantities for risk management and forecasting purposes. As an alternative form of variability mesaures, we compute option-implied interquantile and interexpectile differences, that are compared with a weekly VIX-like index. In terms of forecasting power we investigate how different quantities related to the implicit quantile and expectile curves predict future logreturns and future realized variances.

Suggested Citation

  • Fabio Bellini & Edit Rroji & Carlo Sala, 2022. "Implicit quantiles and expectiles," Annals of Operations Research, Springer, vol. 313(2), pages 733-753, June.
    • Handle: RePEc:spr:annopr:v:313:y:2022:i:2:d:10.1007_s10479-021-04054-8
    DOI: 10.1007/s10479-021-04054-8
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    References listed on IDEAS

    as
    1. Giovanni Barone Adesi, 2016. "VaR and CVaR Implied in Option Prices," JRFM, MDPI, vol. 9(1), pages 1-6, February.
    2. Konstantinos Metaxoglou & Aaron Smith, 2017. "Forecasting Stock Returns Using Option-Implied State Prices," Journal of Financial Econometrics, Oxford University Press, vol. 15(3), pages 427-473.
    3. Giovanni Barone‐Adesi & Marinela Adriana Finta & Chiara Legnazzi & Carlo Sala, 2019. "WTI crude oil option implied VaR and CVaR: An empirical application," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(6), pages 552-563, September.
    4. Breeden, Douglas T & Litzenberger, Robert H, 1978. "Prices of State-contingent Claims Implicit in Option Prices," The Journal of Business, University of Chicago Press, vol. 51(4), pages 621-651, October.
    5. Fabio Bellini & Elena Di Bernardino, 2017. "Risk management with expectiles," The European Journal of Finance, Taylor & Francis Journals, vol. 23(6), pages 487-506, May.
    6. Banerjee, Prithviraj S. & Doran, James S. & Peterson, David R., 2007. "Implied volatility and future portfolio returns," Journal of Banking & Finance, Elsevier, vol. 31(10), pages 3183-3199, October.
    7. Konstantinos Metaxoglou & Aaron Smith, 2017. "State Prices of Conditional Quantiles: New Evidence on Time Variation in the Pricing Kernel," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(1), pages 192-217, January.
    8. G. Rubbaniy & Robel Asmerom & Syed Kumail Abbas Rizvi & Bushra Naqvi, 2014. "Do fear indices help predict stock returns?," Quantitative Finance, Taylor & Francis Journals, vol. 14(5), pages 831-847, May.
    9. Newey, Whitney K & Powell, James L, 1987. "Asymmetric Least Squares Estimation and Testing," Econometrica, Econometric Society, vol. 55(4), pages 819-847, July.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Risk-neutral distribution; Weekly options; Quantiles; Expectiles; Risk management; Forecasting;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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