IDEAS home Printed from https://ideas.repec.org/a/oup/jfinec/v13y2015i2p456-477..html
   My bibliography  Save this article

Functional Dynamic Factor Model for Intraday Price Curves

Author

Listed:
  • Piotr Kokoszka
  • Hong Miao
  • Xi Zhang

Abstract

This article proposes a functional dynamic factor model for the evaluation of the impact of scalar– and curve–valued factors on the shapes of intraday price curves. The asymptotic theory leads to practically useful confidence intervals for the factor coefficients. The main findings pertain to the impact of the shapes of intraday oil futures on the shapes of intraday prices of blue chip stocks.

Suggested Citation

  • Piotr Kokoszka & Hong Miao & Xi Zhang, 2015. "Functional Dynamic Factor Model for Intraday Price Curves," Journal of Financial Econometrics, Oxford University Press, vol. 13(2), pages 456-477.
    • Handle: RePEc:oup:jfinec:v:13:y:2015:i:2:p:456-477.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/jjfinec/nbu004
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Philip Nadler & Alessio Sancetta, 2023. "Empirical Asset Pricing with Functional Factors," Journal of Financial Econometrics, Oxford University Press, vol. 21(4), pages 1258-1281.
    2. Chenlei Leng & Degui Li & Hanlin Shang & Yingcun Xia, 2024. "Covariance Function Estimation for High-Dimensional Functional Time Series with Dual Factor Structures," Papers 2401.05784, arXiv.org, revised Jan 2024.
    3. Zhenjie Liang & Futian Weng & Yuanting Ma & Yan Xu & Miao Zhu & Cai Yang, 2022. "Measurement and Analysis of High Frequency Assert Volatility Based on Functional Data Analysis," Mathematics, MDPI, vol. 10(7), pages 1-11, April.
    4. Lajos Horváth & Piotr Kokoszka & Jeremy VanderDoes & Shixuan Wang, 2022. "Inference in functional factor models with applications to yield curves," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(6), pages 872-894, November.
    5. Li, Xuemei & Liu, Xiaoxing, 2023. "Functional classification and dynamic prediction of cumulative intraday returns in crude oil futures," Energy, Elsevier, vol. 284(C).
    6. Horváth, Lajos & Li, Bo & Li, Hemei & Liu, Zhenya, 2020. "Time-varying beta in functional factor models: Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    7. Kokoszka Piotr & Miao Hong & Stoev Stilian & Zheng Ben, 2019. "Risk Analysis of Cumulative Intraday Return Curves," Journal of Time Series Econometrics, De Gruyter, vol. 11(2), pages 1-31, July.
    8. Kokoszka Piotr & Miao Hong & Zheng Ben, 2017. "Testing for asymmetry in betas of cumulative returns: Impact of the financial crisis and crude oil price," Statistics & Risk Modeling, De Gruyter, vol. 34(1-2), pages 33-53, June.
    9. Larbi Ait-Hennani & Zoulikha Kaid & Ali Laksaci & Mustapha Rachdi, 2022. "Nonparametric Estimation of the Expected Shortfall Regression for Quasi-Associated Functional Data," Mathematics, MDPI, vol. 10(23), pages 1-23, November.
    10. Bouri, Elie & Lau, Chi Keung Marco & Saeed, Tareq & Wang, Shixuan & Zhao, Yuqian, 2021. "On the intraday return curves of Bitcoin: Predictability and trading opportunities," International Review of Financial Analysis, Elsevier, vol. 76(C).
    11. Chen Tang & Yanlin Shi, 2021. "Forecasting High-Dimensional Financial Functional Time Series: An Application to Constituent Stocks in Dow Jones Index," JRFM, MDPI, vol. 14(8), pages 1-13, July.

    More about this item

    Keywords

    functional factor model; intraday price curves; oil futures;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General

    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:oup:jfinec:v:13:y:2015:i:2:p:456-477.. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/sofieea.html .

    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.