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Inferring financial bubbles from option data

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  • Robert A. Jarrow
  • Simon S. Kwok

Abstract

Financial bubbles arise when the underlying asset's market price deviates from its fundamental value. Unlike other bubble tests that use time series data and assume a reduced‐form price process, we infer the existence of bubbles nonparametrically using option price data. Under no‐arbitrage and acknowledging data constraints, we can partially identify asset price bubbles using a cross section of European option prices. In the empirical analysis, we obtain interval estimates of price bubbles embedded in the S&P 500 Index. The estimated index bubbles are then used to construct profitable momentum trading strategies that consistently outperform a buy‐and‐hold trading strategy.

Suggested Citation

  • Robert A. Jarrow & Simon S. Kwok, 2021. "Inferring financial bubbles from option data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(7), pages 1013-1046, November.
    • Handle: RePEc:wly:japmet:v:36:y:2021:i:7:p:1013-1046
    DOI: 10.1002/jae.2862
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    References listed on IDEAS

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    Cited by:

    1. Xiaoting Dai & Linhai Wu, 2023. "The impact of capitalist profit-seeking behavior by online food delivery platforms on food safety risks and government regulation strategies," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
    2. Robert A. Jarrow & Simon S. Kwok, 2023. "An explosion time characterization of asset price bubbles," International Review of Finance, International Review of Finance Ltd., vol. 23(2), pages 469-479, June.
    3. Francesca Biagini & Lukas Gonon & Andrea Mazzon & Thilo Meyer-Brandis, 2022. "Detecting asset price bubbles using deep learning," Papers 2210.01726, arXiv.org, revised Dec 2022.

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