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Forecasting intraday financial time series with sieve bootstrapping and dynamic updating

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  • Han Lin Shang
  • Kaiying Ji

Abstract

Intraday financial data often take the form of a collection of curves that can be observed sequentially over time, such as intraday stock price curves. These curves can be viewed as a time series of functions observed on equally spaced and dense grids. Due to the curse of dimensionality, high‐dimensional data pose challenges from a statistical aspect; however, it also provides opportunities to analyze a rich source of information so that the dynamic changes within short‐time intervals can be better understood. We consider a sieve bootstrap method to construct 1‐day‐ahead point and interval forecasts in a model‐free way. As we sequentially observe new data, we also implement two dynamic updating methods to update point and interval forecasts for achieving improved accuracy. The forecasting methods are validated through an empirical study of 5‐min cumulative intraday returns of the S&P/ASX All Ordinaries Index.

Suggested Citation

  • Han Lin Shang & Kaiying Ji, 2023. "Forecasting intraday financial time series with sieve bootstrapping and dynamic updating," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 1973-1988, December.
    • Handle: RePEc:wly:jforec:v:42:y:2023:i:8:p:1973-1988
    DOI: 10.1002/for.3000
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    1. Horváth, Lajos & Kokoszka, Piotr & Rice, Gregory, 2014. "Testing stationarity of functional time series," Journal of Econometrics, Elsevier, vol. 179(1), pages 66-82.
    2. Wood, Robert A, 2000. "Market Microstructure Research Databases: History and Projections," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(2), pages 140-145, April.
    3. Hausman, Jerry A. & Lo, Andrew W. & MacKinlay, A. Craig, 1992. "An ordered probit analysis of transaction stock prices," Journal of Financial Economics, Elsevier, vol. 31(3), pages 319-379, June.
    4. Han Lin Shang & Yang Yang & Fearghal Kearney, 2019. "Intraday forecasts of a volatility index: functional time series methods with dynamic updating," Annals of Operations Research, Springer, vol. 282(1), pages 331-354, November.
    5. Han Lin Shang, 2017. "Forecasting intraday S&P 500 index returns: A functional time series approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(7), pages 741-755, November.
    6. Klepsch, J. & Klüppelberg, C. & Wei, T., 2017. "Prediction of functional ARMA processes with an application to traffic data," Econometrics and Statistics, Elsevier, vol. 1(C), pages 128-149.
    7. Gregory Rice & Tony Wirjanto & Yuqian Zhao, 2020. "Tests for conditional heteroscedasticity of functional data," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(6), pages 733-758, November.
    8. Alexander Aue & Diogo Dubart Norinho & Siegfried Hörmann, 2015. "On the Prediction of Stationary Functional Time Series," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 378-392, March.
    9. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    10. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    11. Gabrys, Robertas & Kokoszka, Piotr, 2007. "Portmanteau Test of Independence for Functional Observations," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1338-1348, December.
    12. Piotr Kokoszka & Matthew Reimherr, 2013. "Determining the order of the functional autoregressive model," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(1), pages 116-129, January.
    13. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    14. Clifford M. Hurvich & Chih‐Ling Tsai, 1993. "A Corrected Akaike Information Criterion For Vector Autoregressive Model Selection," Journal of Time Series Analysis, Wiley Blackwell, vol. 14(3), pages 271-279, May.
    15. Hansen, Peter R. & Lunde, Asger, 2006. "Realized Variance and Market Microstructure Noise," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 127-161, April.
    16. Efstathios Paparoditis & Han Lin Shang, 2023. "Bootstrap Prediction Bands for Functional Time Series," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(542), pages 972-986, April.
    17. Klepsch, J. & Klüppelberg, C., 2017. "An innovations algorithm for the prediction of functional linear processes," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 252-271.
    18. Wang, Shanshan & Jank, Wolfgang & Shmueli, Galit, 2008. "Explaining and Forecasting Online Auction Prices and Their Dynamics Using Functional Data Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 144-160, April.
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