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Estimating large-scale multivariate local level models with application to stochastic volatility
Last modified: 2018-05-10
Abstract
We derive the closed-form solution to the Riccati equation for the steady-state Kalman filter of the multivariate local linear trend model. Based on this result we propose a fast EM algorithm that provides approximated maximum likelihood estimates of the model's parameters and apply it to large-scale stochastic volatility models.
References
- Alizadeh S, Brandt MW, Diebold FX (2002) Range-based estimation of stochasticvolatility models. The Journal of Finance 57(3):1047–1091
- Ansley C, Kohn R (1985) Estimation, filtering and smoothing in state space modelswith incomplitely specified initial conditions. The Annals of Statistics 13:1286–1316
- Durbin J, Koopman S (2001) Time Series Analysis by State Space Methods. OxfordUniversity Press
- Harvey A (1989) Forecasting Structural Time Series and the Kalman Filter. CambridgeUniversity Press
- Harvey A, Ruiz E, Shephard N (1994) Multivariate stochastic variance models. Reviewof Economic Studies 61(2):247–264
- de Jong P (1988) A cross-validation filter for time series models. Biometrika76:594–600
- de Jong P (1989) Smoothing and interpolation with state-space models. Journal ofthe American Statistical Association 75:594–600
- Koopman S (1993) Disturbance smoother for state space model. Biometrika80(1):117–126
- Koopman S (1997) Exact initial kalman filtering and smoothing for nonstationarytime series models. Journal of the American Statistical Association92(400):1630–1638
- Shumway RH, Stoffer DS (2017) Time Series Analysis and Its Applications. WithR Examples. Springer
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