In this paper we investigate exponential smoothing (ES) predictors for the weights of high-dimensional realized global minimum variance portfolios (GMVP) which only depend on the realized covariance matrix of risky financial assets. We compare direct ES predictions of realized GMVP proportions...

Statistical inferences for weights of the global minimum variance portfolio (GMVP) are of both theoretical and practical relevance for mean-variance portfolio selection. Daily realized GMVP weights depend only on realized covariance matrix computed from intraday highfrequency returns. In this...

Currently, the U.S. Department of Justice is seeking money from world's leading financial institutions to settle a probe tied to mortgage-backed securities. Several banks with agreements already met constitute experienced cases with unprecedented amounts of money to pay. In this paper we aim to...

The real time supervision of inflation expectations is an important issue for monetary policymakers, especially in presence of economic uncertainty. In this paper we propose a novel methodology for sequential monitoring of noisy and heteroscedastic market based daily proxies for short, medium...

In this paper we adapt the empirical similarity (ES) concept for the purpose of combining forecasts originating from different models. Our ES approach is suitable for situations where a decision maker refrains from evaluating success probabilities of forecasting models but prefers to think by...

Using a novel three-phase model based upon a conditional autoregressive Wishart (CAW) framework for the realized (co)variances of the Dow Jones and the German stock index DAX, we analyze intra-daily volatility spillovers between the US and German stock markets. The proposed model explicitly...

We propose a Conditional Autoregressive Wishart (CAW) model for the analysis of realized covariance matrices of asset returns. Our model assumes a generalized linear autoregressive moving average structure for the scale matrix of the Wishart distribution allowing to accommodate for complex...

This paper considers estimation of an unknown distribution parameter in situations where we believe that the parameter belongs to a finite interval. We propose for such situations an interval shrinkage approach which combines in a coherent way an unbiased conventional estimator and non-sample...