Markowitz optimization, a key component of modern portfolio theory, is prone to estimation errors in mean vector and covariance matrix, leading to unrealistic portfolio weights, limited diversification, and weak out-of-sample performance. In response, a heuristic portfolio resampling approach...

General dynamic factor models have demonstrated their capacity to circumvent the curse of dimensionality in the analysis of high-dimensional time series and have been successfully considered in many economic and financial applications. Being second-order models, however, they are sensitive to...

Based on a General Dynamic Factor Model with infinite-dimensional factor space and MGARCH common shocks, we develop new estimation and forecasting procedures for conditional covariance matrices in high-dimensional time series. The finite-sample performance of our approach is evaluated via Monte...

In this paper, we analyse the recent principal volatility components analysis procedure. The procedure overcomes several difficulties in modelling and forecasting the conditional covariance matrix in large dimensions arising from the curse of dimensionality. We show that outliers have a...

Markowitz optimization plays an important role in modern portfolio theory. However, it is well-known that Markowitz optimization is highly affected by the estimation error of the mean vector and covariance matrix, resulting in extreme and/or unrealistic portfolio weights, lacks of...