This article studies multiple structural breaks in large contemporaneous covariance matrices of high-dimensional time series satisfying an approximate factor model. The the second-order moment structure common components are due to sudden changes either loadings or latent factors, requiring appropriate transformation models facilitate estimation (transformed) factors and via classical principal...

This paper considers issues related to estimation, inference and computation with multiple structural changes occurring at unknown dates in a system of equations. Changes can occur in the regression coefficients and/or the covariance matrix of the errors. We also allow arbitrary restrictions on these parameters, which permits the analysis of partial structural change models, common breaks occur...

We provide an approach to forecasting the long-run (unconditional) distribution of equity returns making optimal use of historical data in the presence of structural breaks. Our focus is on learning about breaks in real time and assessing their impact on out-of-sample density forecasts. Forecasts use a probabilityweighted average of submodels, each of which is estimated over a different history...