Abstract: A method for estimating a covariance with respect to a plurality of bonds is provided. The method includes: receiving historical bond market returns data; using a first algorithm based on an Auto-Regressive-Moving-Average (ARMA) model to calculate ARMA model regression errors based on the historical bond market data; using a second algorithm based on a logarithmic Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model to calculate an estimated volatility vector based on the ARMA model regression errors; using the ARMA model regression errors and the calculated volatility vector to estimate a time-varying covariance matrix of the ARMA model regression errors with respect to the historical bond market data; using the estimated time-varying covariance matrix of the ARMA model regression errors and the calculated volatility vector to estimate a time-varying covariance matrix of the bond returns; and using the estimated time-varying covariance matrix to calculate a set of predicted hedge ratios.

Abstract: Various methods, apparatuses/systems, and media for ultra-high dimensional Hawkes processes are disclosed. A receiver receives event data from a plurality of data sources. The event data includes ultra-high dimensional data. A processor creates a model by modeling the event data as modalities. Each modality contains a number of marks of the event. The processor reduces the dimensionality of the ultra-high dimensional data to a predefined value by implementing processes of factorization; implements a simulation process based on a superposition principle; samples an inter-arrival time for each mark within a modality by applying the simulation process to generate synthetic data; and simulates the synthetic data using the created model thereby increasing processing speed of the processor for processing the ultra-high dimensional data.

Abstract: Various methods, apparatuses/systems, and media for data management are disclosed. A processor integrates internal data, third party data, user-generated content data, and historical data into key data that relates to management of one or more stores and branches in a network of stores and branches. The key data is stored into a single centralized database. The processor generates analytical insights data from analysis of the key data and historical data. The analytical insights data is displayed onto a graphical user interface (GUI) for considering and analyzing the analytical insights data by a user. User's feedback data is received that corresponds to the user's response based on analyzing the analytical insights data. The processor apples machine learning (ML) algorithms for continuously analyzing all available data including the user's feedback data and extracting and classifying contents of available data including the feedback data to provide targeted recommendations data onto the GUI.