Dynamic modeling and forecasting algorithms for financial data systems

OF THE DISSERTATION Dynamic Modeling and Forecasting Algorithms for Financial Data Systems by Manish Mahajan Dissertation Director: Dr. N. N. Puri It is a valid question that why a Control Systems Engineer would be interested in dealing with financial instruments. Financial instruments involving option theory are very elegant, math oriented and practical. These mathematical tools have created a new industry known as ’Derivative Industry’ or ’Hedge-Fund Industry’ or so called ’Risk-Management Industry’. This thesis is aimed at developing investment strategies involving the decision making needs via control system techniques. The problem, in general, is computationally challenging particularly when investment of many securities is involved resulting in a high dimensional computational framework. Furthermore, complications may arise due to realistic restrictions and non-linearities. The various areas of financial engineering are very fertile for the application of the system methodology and control theory techniques. Modeling, optimization, identification and computational methods used in the Systems Engineering can be successfully applied to the financial instruments. The ideas developed in this thesis are more about the scientific reasoning involving financial instruments rather than specific situations alone. Major contribution of this thesis is the time series optimal prediction filter and the development of the Dynamic Modeling and Forecasting Algorithm (DMFA). The proposed algorithm predicts the next data point of the financial time series while dynamically computing the parameters from existing data. The computation of the parameters is optimized by use of the recursive

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