We develop a systematic framework for the joint modelling of returns and multiple daily realised measures. We assume a linear state space representation for the log realised measures, which are noisy and biased estimates of the log integrated variance, at least due to Jensen's inequality. We incorporate filtering methods for the estimation of the latent log volatility process. The endogeneity between daily returns and realised measures leads us to develop a consistent two-step estimation method for all parameters in our specification. This method is computationally straightforward even when the stochastic volatility model contains non-Gaussian return innovations and leverage effects. The empirical results reveal that measurement errors become significantly smaller after filtering and that the forecasts from our model outperforms those from a set of recently developed alternatives.
Kalman filter leverage realised volatility simulated maximum likelihood