 Estimation of Realized Asymmetric Stochastic Volatility Models Using Kalman FilterEconometrics, 2023, vol. 11, issue 3, 1-14 Abstract: Despite the growing interest in realized stochastic volatility models, their estimation techniques, such as simulated maximum likelihood (SML), are computationally intensive. Based on the realized volatility equation, this study demonstrates that, in a finite sample, the quasi-maximum likelihood estimator based on the Kalman filter is competitive with the two-step SML estimator, which is less efficient than the SML estimator. Regarding empirical results for the S&P 500 index, the quasi-likelihood ratio tests favored the two-factor realized asymmetric stochastic volatility model with the standardized t distribution among alternative specifications, and an analysis on out-of-sample forecasts prefers the realized stochastic volatility models, rejecting the model without the realized volatility measure. Furthermore, the forecasts of alternative RSV models are statistically equivalent for the data covering the global financial crisis. Keywords: realized volatility; stochastic volatility; asymmetry; heavy-tailed distribution; quasi-maximum likelihood estimation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:11:y:2023:i:3:p:18-:d:1207445 Access Statistics for this article Econometrics is currently edited by Ms. Jasmine Liu More articles in Econometrics from MDPI |
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