Simple Factor Realized Stochastic Volatility Models

Kawakatsu Hiroyuki ()
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Kawakatsu Hiroyuki: Business School, Dublin City University, Dublin 9, Ireland

Journal of Time Series Econometrics, 2023, vol. 15, issue 1, 79-110

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.

Keywords: realized variance; stochastic volatility; Kalman filtering; state space model (search for similar items in EconPapers)
JEL-codes: C14 C32 C58 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1515/jtse-2021-0049

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