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Title: Estimating a time-varying parameter model with shrinkage for the Standard\&Poor's 500 index Authors:  Borys Koval - Vienna University of Economics and Business (Austria) [presenting]
Leopold Soegner - Institute for Advanced Studies (Austria)
Sylvia Fruehwirth-Schnatter - WU Vienna University of Economics and Business (Austria)
Abstract: Time-varying parameter models (TVP) are used to investigate in-sample and out-of-sample predictability for monthly returns of the Standard\&Poor's 500 index (S\&P 500). We consider unrestricted TVP model with a discount factor for the variance process. For the restricted TVP model, we follow the recently developed Bayesian methods that allow for shrinkage for time-varying parameter models. This is attained by applying hierarchical double-Gamma shrinkage priors on the process variances to automatically shrink the time-varying coefficients to static ones. In addition, we differentiate between the significant and insignificant coefficients if the model is overfitted. Both models are tested using simulated data and real market data. Furthermore, we investigate the sensitivity of the estimation approach and the time span used to evaluate the model. To evaluate one-step-ahead predictive densities, Kalman mixture approximations were applied.