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Jump Clustering, Information Flows, and Stock Price Efficiency†

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  • Jian Chen
Abstract
We study the clustering behavior of stock return jumps modeled by a self/cross-exciting process embedded in a stochastic volatility model. Based on the model estimates, we propose a novel measurement of stock price efficiency characterized by the extent of jump clustering that stock returns exhibit. This measurement demonstrates the capability of capturing the speed at which stock prices assimilate new information, especially at the firm-specific level. Furthermore, we assess the predictability of self-exciting (clustered) jumps in stock returns. We employ a particle filter to sample latent states in the out-of-sample period and perform one-step-ahead probabilistic forecasting on upcoming jumps. We introduce a new statistic derived from predicted probabilities of positive and negative jumps, which has been shown to be effective in return predictions.

Suggested Citation

  • Jian Chen, 2024. "Jump Clustering, Information Flows, and Stock Price Efficiency†," Journal of Financial Econometrics, Oxford University Press, vol. 22(5), pages 1588-1615.
    • Handle: RePEc:oup:jfinec:v:22:y:2024:i:5:p:1588-1615.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbae009
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    More about this item

    Keywords

    Bayesian inference; information flows; jump clustering; jump prediction; stock price efficiency;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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