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Simple Factor Realized Stochastic Volatility Models

Hiroyuki Kawakatsu

Journal of Time Series Econometrics2023https://doi.org/10.1515/jtse-2021-0049article
AJG 2ABDC B
Weight
0.26

What the paper says

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.

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https://doi.org/https://doi.org/10.1515/jtse-2021-0049

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@article{hiroyuki2023,
  title        = {{Simple Factor Realized Stochastic Volatility Models}},
  author       = {Hiroyuki Kawakatsu},
  journal      = {Journal of Time Series Econometrics},
  year         = {2023},
  doi          = {https://doi.org/https://doi.org/10.1515/jtse-2021-0049},
}

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Simple Factor Realized Stochastic Volatility Models

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Evidence weight

0.26

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.00 × 0.4 = 0.00
M · momentum0.20 × 0.15 = 0.03
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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