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Identifying financial instability conditions using high frequency data

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Abstract

We study an indicator of financial instability based on the computation of the decay rate for the propagation of a given market shock. The rate of variation through time of an initial perturbation of the price process enables us to understand if such a shock will be rapidly absorbed or, on the contrary, it will be amplified by the market. The indicator combines non-linearly volatility, leverage and covariance between leverage and price and is model-free. It provides an early warning indicator of instability for a given high frequency financial time series. A new consistency theorem for the estimator of each component of the proposed indicator is proved. The properties of the indicator are investigated numerically under the CEV model and empirically using tick-by-tick data of the S&P 500 index futures.

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Notes

  1. The last factor is shown to be related to the volatility of volatility in the paper.

  2. In Example 2.4 Barucci et al. (2003) the given expression for the feedback rate in the case of the CEV model contains a mistake due to an uncorrect change of variable. However, the CEV model is not further investigated in that paper, thus the error does not affect at all the results presented therein.

  3. The drift term can be added without any difficulty as done in Malliavin and Thalmaier (2006), Inkaya and Yolcu Ocur (2014).

  4. The prime stands here for the first derivative with respect to the level \(x_t\).

  5. Note that these conditions also imply that \(M^2/n \rightarrow 0\), as required by Theorem 3.2.

  6. Note that this corresponds to the Black-Scholes case.

  7. See the link https://deltatradinggroup.com/portfolio-items/the-eco-diy-collection/.

  8. See the link https://www.investopedia.com/financial-edge/0911/how-september-11-affected-the-u.s.-stock-market.aspx.

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Acknowledgements

We are indebted to Prof. Anne Opschoor and two anonymous referees for their helpful and constructive comments.

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Authors and Affiliations

  1. Dipartimento di Scienze per l’Economia e l’Impresa, University of Firenze, Florence, Italy

    Maria Elvira Mancino

  2. Dipartimento di Scienze Economiche e Aziendali, University of Parma, Parma, Italy

    Simona Sanfelici

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  1. Maria Elvira Mancino
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  2. Simona Sanfelici
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Correspondence to Maria Elvira Mancino.

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Mancino, M.E., Sanfelici, S. Identifying financial instability conditions using high frequency data. J Econ Interact Coord 15, 221–242 (2020). https://doi.org/10.1007/s11403-019-00253-6

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