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Non-parametric estimation of historical volatility

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  • John Randal
  • Peter Thomson
  • Martin Lally

Abstract

Evolving volatility is a dominant feature observed in most financial time series and a key parameter used in option pricing and many other financial risk analyses. A number of methods for non-parametric scale estimation are reviewed and assessed with regard to the stylized features of financial time series. A new non-parametric procedure for estimating historical volatility is proposed based on local maximum likelihood estimation for the t-distribution. The performance of this procedure is assessed using simulated and real price data and is found to be the best among estimators we consider. We propose that it replaces the moving variance historical volatility estimator.

Suggested Citation

  • John Randal & Peter Thomson & Martin Lally, 2004. "Non-parametric estimation of historical volatility," Quantitative Finance, Taylor & Francis Journals, vol. 4(4), pages 427-440.
    • Handle: RePEc:taf:quantf:v:4:y:2004:i:4:p:427-440
    DOI: 10.1080/14697680400008692
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    Cited by:

    1. Grażyna Trzpiot & Justyna Majewska, 2008. "Investment decisions and portfolios classifications based on robust methods of estimation," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 18(1), pages 83-96.
    2. Fried, Roland, 2012. "On the online estimation of local constant volatilities," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3080-3090.
    3. Alexander Tchernitser & Dmitri Rubisov, 2009. "Robust estimation of historical volatility and correlations in risk management," Quantitative Finance, Taylor & Francis Journals, vol. 9(1), pages 43-54.

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