This paper introduces a large-dimensional covariance estimator that exploits the hierarchical structure in financial returns. Prevailing techniques that filter the noise in a covariance matrix according to hierarchical agglomeration are fragile to data perturbations and inordinately suppress...

In this paper, we examine the herding behaviour of the Chinese renewable energy sector using both static and time-varying coefficient models. Examining daily data from January 05, 2015 to April 29, 2022, we find strong evidence of herding behaviour changing over time in this market. We find that...

This study aims to overcome the problem of dimensionality, accurate estimation, and forecasting Value-at-Risk (VaR) and Expected Shortfall (ES) uncertainty intervals in high frequency data. A Bayesian bootstrapping and backtest density forecasts, which are based on a weighted threshold and...