New Methods for Structural Updating/Learning in High Dimensional Econometric Models via Sequential Monte Carlo with Applications in Economics and Finance(1/2)

We propose a comprehensive modelling approach based on sequential Monte Carlo to adaptively monitor portfolio management and enable deduce information from massive data set yet maintain superior performance with an easy-to-update and manageable portfolio allocation and manage system. Set against the classical first-inference-then-optimize procedure, we begin with a reverse engineering by firstly propose to select only a handful of small number of assets and optimal weights with sparsity from a big sky of assets using newly developed variable selection under a penalized zero-norm constraint. The chosen assets are then modeled and estimated with a general state-space model in a concise way. We introduce and examine the performance of a stochastic genetic differential algorithm in our application. The proposed alternative approaches are then applied in the context of portfolio selection/index tracking/identification of structural breaks. We then build a general state-space model with flexible specifications for the chosen assets for dynamic modelling, and adaptive monitoring and updating. We believe this analyzing framework based on SMC may shed lights on future modelling, and effective updating for applications in Economic and Finance.