IDEAS home Printed from https://ideas.repec.org/a/bpj/jtsmet/v5y2013i2p87-116n1.html

Monitoring the Intraday Volatility Pattern

Author

Listed:
  • Gabrys Robertas

    (Department of Information and Operations Management, Marshall School of Business, University of Southern California, Los Angeles, CA, USA)

  • Hörmann Siegfried

    (Department of Mathematics, Université Libre de Bruxelles, CP 210 Bd. du Triomphe, Brussels 1050, Belgium)

  • Kokoszka Piotr

    (Department of Statistics, Colorado State University, Fort Collins, CO, USA)

Abstract

A functional time series consists of curves, typically one curve per day. The most important parameter of such a series is the mean curve. We propose two methods of detecting a change in the mean function of a functional time series. The change is detected on line, as new functional observations arrive. The general methodology is motivated by, and applied to, the detection of a change in the mean intraday volatility pattern. The methodology is asymptotically justified by applying a new notion of weak dependence for functional time series. It is calibrated and validated by simulations based on real intraday volatility curves.

Suggested Citation

  • Gabrys Robertas & Hörmann Siegfried & Kokoszka Piotr, 2013. "Monitoring the Intraday Volatility Pattern," Journal of Time Series Econometrics, De Gruyter, vol. 5(2), pages 87-116, July.
    • Handle: RePEc:bpj:jtsmet:v:5:y:2013:i:2:p:87-116:n:1
    DOI: 10.1515/jtse-2012-0006
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jtse-2012-0006
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/jtse-2012-0006?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Torben G. Andersen & Tim Bollerslev & Ashish Das, 2001. "Variance‐ratio Statistics and High‐frequency Data: Testing for Changes in Intraday Volatility Patterns," Journal of Finance, American Finance Association, vol. 56(1), pages 305-327, February.
    2. Kargin, V. & Onatski, A., 2008. "Curve forecasting by functional autoregression," Journal of Multivariate Analysis, Elsevier, vol. 99(10), pages 2508-2526, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Torben G. Andersen & Yingwen Tan & Viktor Todorov & Zhiyuan Zhang, 2025. "Testing mean stationarity of intraday volatility curves," Quantitative Economics, Econometric Society, vol. 16(3), pages 1059-1091, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Atanda Mustapha Saidi, 2017. "Working Paper 273 - Stock (Mis)pricing and investment dynamics in Africa," Working Paper Series 2390, African Development Bank.
    2. Zhang, Xianyang, 2016. "White noise testing and model diagnostic checking for functional time series," Journal of Econometrics, Elsevier, vol. 194(1), pages 76-95.
    3. Chen, Yichao & Pun, Chi Seng, 2019. "A bootstrap-based KPSS test for functional time series," Journal of Multivariate Analysis, Elsevier, vol. 174(C).
    4. Kyong Shik Eom & Kyung Yoon Kwon & Sung Chae La & Jong-Ho Park, 2022. "Dynamic and Static Volatility Interruptions: Evidence from the Korean Stock Markets," JRFM, MDPI, vol. 15(3), pages 1-19, February.
    5. Kim Christensen & Ulrich Hounyo & Mark Podolskij, 2017. "Is the diurnal pattern sufficient to explain the intraday variation in volatility? A nonparametric assessment," CREATES Research Papers 2017-30, Department of Economics and Business Economics, Aarhus University.
    6. Voges, Michelle & Leschinski, Christian & Sibbertsen, Philipp, 2017. "Seasonal long memory in intraday volatility and trading volume of Dow Jones stocks," Hannover Economic Papers (HEP) dp-599, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    7. Hai-Chuan Xu & Wei Chen & Xiong Xiong & Wei Zhang & Wei-Xing Zhou & H Eugene Stanley, 2016. "Limit-order book resiliency after effective market orders: Spread, depth and intensity," Papers 1602.00731, arXiv.org, revised Feb 2017.
    8. Álvarez-Liébana, Javier & Bosq, Denis & Ruiz-Medina, María D., 2016. "Consistency of the plug-in functional predictor of the Ornstein–Uhlenbeck process in Hilbert and Banach spaces," Statistics & Probability Letters, Elsevier, vol. 117(C), pages 12-22.
    9. Boukhiar, Souad & Mourid, Tahar, 2022. "Resolvent estimators for functional autoregressive processes with random coefficients," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    10. Lajos Horváth & Gregory Rice, 2014. "Extensions of some classical methods in change point analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(2), pages 219-255, June.
    11. Alva, Kenedy & Romo, Juan & Ruiz Ortega, Esther, 2009. "Modelling intra-daily volatility by functional data analysis: an empirical application to the spanish stock market," DES - Working Papers. Statistics and Econometrics. WS ws092809, Universidad Carlos III de Madrid. Departamento de Estadística.
    12. Singh, Ritvik & Gangwar, Rachna, 2018. "A Temporal Analysis of Intraday Volatility of Nifty Futures on the National Stock Exchange," EconStor Preprints 183471, ZBW - Leibniz Information Centre for Economics.
    13. Haixu Wang & Jiguo Cao, 2023. "Nonlinear prediction of functional time series," Environmetrics, John Wiley & Sons, Ltd., vol. 34(5), August.
    14. Lajos Horváth & Zhenya Liu & Curtis Miller & Weiqing Tang, 2024. "Breaks in term structures: Evidence from the oil futures markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2317-2341, April.
    15. Devin Didericksen & Piotr Kokoszka & Xi Zhang, 2012. "Empirical properties of forecasts with the functional autoregressive model," Computational Statistics, Springer, vol. 27(2), pages 285-298, June.
    16. Caio Almeida & Romeu Gomes & André Leite & Axel Simonsen & José Vicente, 2009. "Does Curvature Enhance Forecasting?," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 12(08), pages 1171-1196.
    17. Dominique Guegan & Matteo Iacopini, 2018. "Nonparameteric forecasting of multivariate probability density functions," Documents de travail du Centre d'Economie de la Sorbonne 18012, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    18. Macaro, Christian, 2010. "Bayesian non-parametric signal extraction for Gaussian time series," Journal of Econometrics, Elsevier, vol. 157(2), pages 381-395, August.
    19. Kim Christensen & Ulrich Hounyo & Mark Podolskij, 2026. "Is the diurnal pattern sufficient to explain intraday variation in volatility? A nonparametric assessment," Papers 2601.16613, arXiv.org.
    20. Chamil W SENARATHNE & Wei JIANGUO, 2020. "Testing for Heteroskedastic Mixture of Ordinary Least Squares Errors," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 73-91, July.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:jtsmet:v:5:y:2013:i:2:p:87-116:n:1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyterbrill.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.