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Big-Data for High-Frequency Volatility Analysis with Time-Deformed Observations

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • António A. F. Santos

    (Financial and Monetary Research Group (GEMF); Center for Business and Economics Research (CeBER); University of Coimbra)

Abstract

High-frequency volatility analysis has become possible with the increasing availability of intraday data. With Big-data, parameter estimates usually present lower levels of estimation uncertainty. However, intraday data embody a relative increase in noise. One of the most used models that support the analysis is the Stochastic Volatility model. With an increased number of data, it can create the impression that there is enough information able to deal with added components for characterizing the evolution of financial returns. One example is when the volatility process includes jumps. These elements impose substantial challenges for parameter estimation. We propose a framework using time-deformed observations, capable of delivering through new information sources, more robust parameter estimates.

Suggested Citation

  • António A. F. Santos, 2021. "Big-Data for High-Frequency Volatility Analysis with Time-Deformed Observations," Springer Books, in: Marco Corazza & Manfred Gilli & Cira Perna & Claudio Pizzi & Marilena Sibillo (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 383-388, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-78965-7_56
    DOI: 10.1007/978-3-030-78965-7_56
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