This paper formulates dynamic density functions, based upon skewed-t and similar representations, to model and forecast electricity price spreads between different hours of the day. This supports an optimal day ahead storage and discharge schedule, and thereby facilitates a bidding strategy for...

We propose a shrinkage estimator for covariance matrices designed to minimize estimation error of the Global Minimum Variance (GMV) portfolio. Implementing the GMV portfolio requires estimating the asset covariance matrix and using this to obtain variance-minimizing portfolio weights. Standard...

The mean squared prediction error of the linear regression model is examined when estimation is performed with instrumental variables. It is shown that increasing the number of instruments in the estimation procedure, can reduce the mean squared prediction error of the model through more...

Background: The increased availability of claims data allows one to build high dimensional datasets, rich in covariates, for accurately estimating treatment effects in medical and epidemiological cohort studies. This paper shows the full potential of machine learning for the estimation of...

In this paper, we propose a new non-parametric density estimator derived from the theory of frames and Riesz bases. In particular, we propose the so-called bi-orthogonal density estimator based on the class of B-splines, and derive its theoretical properties including the asymptotically optimal...

Deriving estimators from historical data is common practice in applied quantitative finance. The availability of ever larger data sets and easier access to statistical algorithms has also led to an increased usage of historical estimators. In this research note, we illustrate how to assess the...

We propose autocorrelation-robust asymptotic variances of the Brier score and Brier skill score, which are generally applicable in circumstances with weak serial correlation. An empirical application in macroeconomics underscores the importance of taking care of serial correlation. We find that...

Forecasting-volatility models typically rely on either daily or high frequency (HF) data and the choice between these two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer of many limitations. HF data feature microstructure problem,...

Forecasting volatility models typically rely on either daily or high frequency (HF) data and the choice between these two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer from many limitations. HF data feature microstructure problem,...

Understanding multi-market interactions and identifying leading markets in the global financial network is of interest to investors, regulators and policymakers. To discover the essential dynamic dependencies of digital currency exchanges, we propose TriSNAR, a three-layer sparse estimator for...