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Simple Factor Realized Stochastic Volatility Models

Author

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  • Kawakatsu Hiroyuki

    (Business School, Dublin City University, Dublin, 9, Ireland)

Abstract

This paper considers the use of multiple noisy daily realized variance measures to extract a denoised latent variance process. The class of stochastic volatility models used for signal extraction has the important feature that they can be written as a linear state space model. As a result, prediction of the denoised latent variance and likelihood evaluation can be carried out efficiently using the Kalman filter. This is in contrast to stochastic models that jointly model the return and variance, which require computationally expensive nonlinear filtering for prediction and inference. The gain from using multiple noisy daily variance measures is examined empirically for the S&P 500 index using daily OHLC (open-high-low-close) data.

Suggested Citation

  • Kawakatsu Hiroyuki, 2023. "Simple Factor Realized Stochastic Volatility Models," Journal of Time Series Econometrics, De Gruyter, vol. 15(1), pages 79-110, January.
    • Handle: RePEc:bpj:jtsmet:v:15:y:2023:i:1:p:79-110:n:2
    DOI: 10.1515/jtse-2021-0049
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    More about this item

    Keywords

    realized variance; stochastic volatility; Kalman filtering; state space model;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • 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
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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