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The main objective of this paper is to propose a feasible, model free estimator of the predictive density of integrated volatility. In this sense, we extend recent papers by Andersen et a]. [Andersen, T.G., Bollerslev,T., Diebold, FX, Labys, P., 2003. Modelling and forecasting realized...
Persistent link: https://www.econbiz.de/10009468887
This article makes two contributions. First, we outline a simple simulation-based framework for constructing conditional distributions for multifactor and multidimensional diffusion processes, for the case where the functional form of the conditional density is unknown. The distributions can be...
Persistent link: https://www.econbiz.de/10009468945
Persistent link: https://www.econbiz.de/10005418659
Mild factor loading instability, particularly if sufficiently independent across the different constituent variables, does not affect the estimation of the number of factors, nor subsequent estimation of the factors themselves (see e.g.  Stock and Watson (2009)). This result does not hold in...
Persistent link: https://www.econbiz.de/10011052274
The main objective of this paper is to propose a feasible, model free estimator of the predictive density of integrated volatility. In this sense, we extend recent papers by Andersen et al. [Andersen, T.G., Bollerslev, T., Diebold, F.X., Labys, P., 2003. Modelling and forecasting realized...
Persistent link: https://www.econbiz.de/10005022946
Persistent link: https://www.econbiz.de/10005732835
Persistent link: https://www.econbiz.de/10005122625
Persistent link: https://www.econbiz.de/10005228547
Persistent link: https://www.econbiz.de/10005228700
This chapter discusses estimation, specification testing, and model selection of predictive density models. In particular, predictive density estimation is briefly discussed, and a variety of different specification and model evaluation tests due to various authors including Christoffersen and...
Persistent link: https://www.econbiz.de/10005336511