Typically, point forecasting methods are compared and assessed by means of an error
measure or scoring function, with the absolute error and the squared error being key …

This paper introduces a new class of multivariate volatility models that utilizes high‐
frequency data. We discuss the models' dynamics and highlight their differences from …

This paper addresses the question of the selection of multivariate generalized
autoregressive conditional heteroskedastic (GARCH) models in terms of variance matrix …

We propose a Conditional Autoregressive Wishart (CAW) model for the analysis of realized
covariance matrices of asset returns. Our model assumes an autoregressive moving …

Large one-off events cause large changes in prices, but may not affect the volatility and
correlation dynamics as much as smaller events. In such cases, standard volatility models …

As a new type of electronic currency, bitcoin is more and more recognized and sought after
by people, but its price fluctuation is more intense, the market has certain risks, and the price …

Multivariate GARCH (MGARCH) models need to be restricted so that their estimation is
feasible in large systems and so that the covariance stationarity and positive definiteness of …

We propose new asymmetric multivariate volatility models. The models exploit estimates of
variances and covariances based on the signs of high-frequency returns, measures known …

Motivated by the need for a positive‐semidefinite estimator of multivariate realized
covariance matrices, we model noisy and asynchronous ultra‐high‐frequency asset prices …

Reservoir computing is a recently introduced machine learning paradigm that has already
shown excellent performances in the processing of empirical data. We study a particular …