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The fine structure of volatility feedback II: Overnight and intra-day effects

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  • Blanc, Pierre
  • Chicheportiche, Rémy
  • Bouchaud, Jean-Philippe

Abstract

We decompose, within an ARCH framework, the daily volatility of stocks into overnight and intra-day contributions. We find, as perhaps expected, that the overnight and intra-day returns behave completely differently. For example, while past intra-day returns affect equally the future intra-day and overnight volatilities, past overnight returns have a weak effect on future intra-day volatilities (except for the very next one) but impact substantially future overnight volatilities. The exogenous component of overnight volatilities is found to be close to zero, which means that the lion’s share of overnight volatility comes from feedback effects. The residual kurtosis of returns is small for intra-day returns but infinite for overnight returns. We provide a plausible interpretation for these findings, and show that our Intra-day/Overnight model significantly outperforms the standard ARCH framework based on daily returns for Out-of-Sample predictions.

Suggested Citation

  • Blanc, Pierre & Chicheportiche, Rémy & Bouchaud, Jean-Philippe, 2014. "The fine structure of volatility feedback II: Overnight and intra-day effects," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 58-75.
    • Handle: RePEc:eee:phsmap:v:402:y:2014:i:c:p:58-75
    DOI: 10.1016/j.physa.2014.01.047
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    Cited by:

    1. Linton, Oliver & Wu, Jianbin, 2020. "A coupled component DCS-EGARCH model for intraday and overnight volatility," Journal of Econometrics, Elsevier, vol. 217(1), pages 176-201.
    2. Chuo Chang, 2020. "Dynamic correlations and distributions of stock returns on China's stock markets," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 10(1), pages 1-6.
    3. Pierre Blanc & Jonathan Donier & Jean-Philippe Bouchaud, 2015. "Quadratic Hawkes processes for financial prices," Papers 1509.07710, arXiv.org.
    4. Mathieu Rosenbaum & Jianfei Zhang, 2022. "On the universality of the volatility formation process: when machine learning and rough volatility agree," Papers 2206.14114, arXiv.org.
    5. Tianyi Wang & Sicong Cheng & Fangsheng Yin & Mei Yu, 2022. "Overnight volatility, realized volatility, and option pricing," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(7), pages 1264-1283, July.
    6. Dhaene, Geert & Wu, Jianbin, 2020. "Incorporating overnight and intraday returns into multivariate GARCH volatility models," Journal of Econometrics, Elsevier, vol. 217(2), pages 471-495.
    7. Kusen, Alex & Rudolf, Markus, 2019. "Feedback trading: Strategies during day and night with global interconnectedness," Research in International Business and Finance, Elsevier, vol. 48(C), pages 438-463.
    8. Todorova, Neda & Souček, Michael, 2014. "Overnight information flow and realized volatility forecasting," Finance Research Letters, Elsevier, vol. 11(4), pages 420-428.
    9. Liu, Chang & Chang, Chuo, 2021. "Combination of transition probability distribution and stable Lorentz distribution in stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).

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