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- Title
Estimation of Realized Asymmetric Stochastic Volatility Models Using Kalman Filter.
- Authors
Asai, Manabu
- 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.
- Subjects
KALMAN filtering; STOCHASTIC models; GLOBAL Financial Crisis, 2008-2009; VOLATILITY (Securities)
- Publication
Econometrics (2225-1146), 2023, Vol 11, Issue 3, p18
- ISSN
2225-1146
- Publication type
Article
- DOI
10.3390/econometrics11030018