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Robust Estimation of Risk-Neutral Moments

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  • Manuel Ammann
  • Alexander Feser

Abstract

This study provides an in-depth analysis of how to estimate risk-neutral moments robustly. A simulation and an empirical study show that estimating risk-neutral moments presents a trade-off-between (1) the bias of estimates caused by a limited strike price domain and (2) the variance of estimates induced by mirco-structural noise. The best trade-off is offered by option-implied quantile moments estimated from a volatility surface interpolated with a local-linear kernel regression and extrapolated linearly. A similarly good trade-off is achieved by estimating regular central option-implied moments from a volatility surface interpolated with a cubic smoothing spline and flat extrapolation.

Suggested Citation

  • Manuel Ammann & Alexander Feser, 2019. "Robust Estimation of Risk-Neutral Moments," Working Papers on Finance 1902, University of St. Gallen, School of Finance.
    • Handle: RePEc:usg:sfwpfi:2019:02
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    References listed on IDEAS

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    1. Soderlind, Paul & Svensson, Lars, 1997. "New techniques to extract market expectations from financial instruments," Journal of Monetary Economics, Elsevier, vol. 40(2), pages 383-429, October.
    2. Steve Ross, 2015. "The Recovery Theorem," Journal of Finance, American Finance Association, vol. 70(2), pages 615-648, April.
    3. Jennifer Conrad & Robert F. Dittmar & Eric Ghysels, 2013. "Ex Ante Skewness and Expected Stock Returns," Journal of Finance, American Finance Association, vol. 68(1), pages 85-124, February.
    4. Neumann, Michael & Skiadopoulos, George, 2013. "Predictable Dynamics in Higher-Order Risk-Neutral Moments: Evidence from the S&P 500 Options," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(3), pages 947-977, June.
    5. Adrian Buss & Grigory Vilkov, 2012. "Measuring Equity Risk with Option-implied Correlations," Review of Financial Studies, Society for Financial Studies, vol. 25(10), pages 3113-3140.
    6. Heston, Steven L, 1993. "A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options," Review of Financial Studies, Society for Financial Studies, vol. 6(2), pages 327-343.
    7. Bo-Young Chang & Peter Christoffersen & Kris Jacobs & Gregory Vainberg, 2011. "Option-Implied Measures of Equity Risk," Review of Finance, European Finance Association, vol. 16(2), pages 385-428.
    8. M. C. Jones & Arthur Pewsey, 2009. "Sinh-arcsinh distributions," Biometrika, Biometrika Trust, vol. 96(4), pages 761-780.
    9. Jackwerth, Jens Carsten & Rubinstein, Mark, 1996. "Recovering Probability Distributions from Option Prices," Journal of Finance, American Finance Association, vol. 51(5), pages 1611-1632, December.
    10. Jurek, Jakub W., 2014. "Crash-neutral currency carry trades," Journal of Financial Economics, Elsevier, vol. 113(3), pages 325-347.
    11. Bates, David S, 1996. "Jumps and Stochastic Volatility: Exchange Rate Processes Implicit in Deutsche Mark Options," Review of Financial Studies, Society for Financial Studies, vol. 9(1), pages 69-107.
    12. Song, Zhaogang & Xiu, Dacheng, 2016. "A tale of two option markets: Pricing kernels and volatility risk," Journal of Econometrics, Elsevier, vol. 190(1), pages 176-196.
    13. Roman Kozhan & Anthony Neuberger & Paul Schneider, 2013. "The Skew Risk Premium in the Equity Index Market," Review of Financial Studies, Society for Financial Studies, vol. 26(9), pages 2174-2203.
    14. Bliss, Robert R. & Panigirtzoglou, Nikolaos, 2002. "Testing the stability of implied probability density functions," Journal of Banking & Finance, Elsevier, vol. 26(2-3), pages 381-422, March.
    15. Cremers, Martijn & Weinbaum, David, 2010. "Deviations from Put-Call Parity and Stock Return Predictability," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 45(2), pages 335-367, April.
    16. George P Gao & Pengjie Gao & Zhaogang Song, 2018. "Do Hedge Funds Exploit Rare Disaster Concerns?," Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2650-2692.
    17. Ian Martin, 2017. "What is the Expected Return on the Market?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(1), pages 367-433.
    18. 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.
    19. Patrick Dennis & Stewart Mayhew, 2009. "Microstructural biases in empirical tests of option pricing models," Review of Derivatives Research, Springer, vol. 12(3), pages 169-191, October.
    20. Merton, Robert C., 1976. "Option pricing when underlying stock returns are discontinuous," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 125-144.
    21. Schmid, Friedrich & Trede, Mark, 2003. "Simple tests for peakedness, fat tails and leptokurtosis based on quantiles," Computational Statistics & Data Analysis, Elsevier, vol. 43(1), pages 1-12, May.
    22. Gurdip Bakshi & Nikunj Kapadia & Dilip Madan, 2003. "Stock Return Characteristics, Skew Laws, and the Differential Pricing of Individual Equity Options," Review of Financial Studies, Society for Financial Studies, vol. 16(1), pages 101-143.
    23. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-654, May-June.
    24. Peter Carr & Liuren Wu, 2009. "Variance Risk Premiums," Review of Financial Studies, Society for Financial Studies, vol. 22(3), pages 1311-1341, March.
    25. Yacine Aït-Sahalia & Andrew W. Lo, 1998. "Nonparametric Estimation of State-Price Densities Implicit in Financial Asset Prices," Journal of Finance, American Finance Association, vol. 53(2), pages 499-547, April.
    26. Birru, Justin & Figlewski, Stephen, 2012. "Anatomy of a meltdown: The risk neutral density for the S&P 500 in the fall of 2008," Journal of Financial Markets, Elsevier, vol. 15(2), pages 151-180.
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    Cited by:

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    3. Matthias Muck, 2022. "Arbitrage-free smile construction on FX option markets using Garman-Kohlhagen deltas and implied volatilities," Review of Derivatives Research, Springer, vol. 25(3), pages 293-314, October.

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    More about this item

    Keywords

    risk-neutral moments; risk-neutral distribution;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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