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Volatility extraction using the Kalman filter



This paper focuses on the extraction of volatility of financial returns. The volatility process is modeled as a superposition of two autoregressive processes which represent the more persistent factor and the quickly mean-reverting factor. As the volatility is not observable, the logarithm of the daily high-low range is employed as its proxy. The estimation of parameters and volatility extraction are performed using a modified version of the Kalman filter which takes into account the finite sample distribution of the proxy.

Suggested Citation

  • Alexandr Kuchynka, 2008. "Volatility extraction using the Kalman filter," Working Papers IES 2008/10, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Jun 2008.
  • Handle: RePEc:fau:wpaper:wp2008_10

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    volatility; stochastic volatility models; Kalman filter; volatility proxy;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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