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• W4319340812 abstract "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." @default.
• W4319340812 created "2023-02-08" @default.
• W4319340812 creator A5037589858 @default.
• W4319340812 date "2023-01-01" @default.
• W4319340812 modified "2023-10-18" @default.
• W4319340812 title "Simple Factor Realized Stochastic Volatility Models" @default.
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• W4319340812 doi "https://doi.org/10.1515/jtse-2021-0049" @default.
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