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Long-Range Dependence, Fractal Processes, and Intra-Daily Data

In: Handbook on Information Technology in Finance

Author

Listed:
  • Wei Sun

    (University of Karlsruhe)

  • Svetlozar Zari Rachev

    (University of Karlsruhe
    University of California)

  • Frank Fabozzi

    (Yale School of Management)

Abstract

With the adoption of electronic trading and order routing systems, an enormous quantity of trading data in electronic form is now available. A complete data set of transactions recorded and their associated characteristics such as transaction time, transaction price, posted bid/ask prices, and volumes are provided. These data are gathered at the ultimate frequency level in the financial markets and usually referred to as intra-daily data or high-frequency data.

Suggested Citation

  • Wei Sun & Svetlozar Zari Rachev & Frank Fabozzi, 2008. "Long-Range Dependence, Fractal Processes, and Intra-Daily Data," International Handbooks on Information Systems, in: Detlef Seese & Christof Weinhardt & Frank Schlottmann (ed.), Handbook on Information Technology in Finance, chapter 23, pages 543-585, Springer.
    • Handle: RePEc:spr:ihichp:978-3-540-49487-4_23
    DOI: 10.1007/978-3-540-49487-4_23
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    Cited by:

    1. Sung Ik Kim, 2022. "ARMA–GARCH model with fractional generalized hyperbolic innovations," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-25, December.
    2. Samet Günay, 2014. "Are the Scaling Properties of Bull and Bear Markets Identical? Evidence from Oil and Gold Markets," IJFS, MDPI, vol. 2(4), pages 1-20, October.

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