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Different Strokes for Different Folks: Long Memory and Roughness

Author

Listed:
  • Shi, Shuping

    (Department of Economics, Macquarie University)

  • Yu, Jun

    (School of Economics and Lee Kong Chian School of Business, SingaporeManagementUniversity)

Abstract

The log realized volatility of financial assets is often modeled as an autoregressive fractionally integrated moving average model (ARFIMA) process, denoted by ARFIMA(p, d, q), with p = 1 and q = 0. Two conflicting results have been found in the literature regarding the dynamics. One stream shows that the data series has a long memory (i.e., the fractional parameter d > 0) with strong mean reversion (i.e., the autoregressive coefficient |α1| ≈ 0). The other stream suggests that the volatil-ity is rough (i.e., d

Suggested Citation

  • Shi, Shuping & Yu, Jun, 2021. "Different Strokes for Different Folks: Long Memory and Roughness," Economics and Statistics Working Papers 7-2021, Singapore Management University, School of Economics.
    • Handle: RePEc:ris:smuesw:2021_007
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    More about this item

    Keywords

    Long memory; fractional integration; roughness; short-run dynamics; realized volatility;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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

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