{'fraction_non_alphanumeric': 0.02040816326530612, 'fraction_numerical': 0.00322234156820623, 'mean_word_length': 5.5174825174825175, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Multivariate High-Frequency-Based Factor Model

We propose a new predictive model for large-dimensional realized covariance matrices. Using high-frequency data, we construct daily realized covariance matrices for the constituents of the S\&amp;P 500 Index and a set of observable factors. Using a standard decomposition of the joint covariance matrix we express the covariance matrix of the individual assets similar to an approximate factor model. A novel feature of the model, is the use of the hierarchical clustering algorithm to determine the structure of the idiosyncratic covariance matrices. To construct a conditional covariance model, we suggest to model the components of the covariance structure separately using autoregressive time series regressions. In an out-of-sample portfolio selection exercise, we find that the proposed model outperforms other commonly used multivariate volatility models in extant literature.