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This research presents an original financial data processing methodology, which effectively neutralizes the effect of microstructure noise in price data, and yields consistent jump detection results across a wide range of detection methods and data sampling frequencies.
The aim of this research is to develop a fast and robust variant of the evolutionary heuristic Bionomic algorithm and assess its contribution to solving complex parametric estimation problems, in conjunction with other traditional optimization techniques. We introduce a modified version of the Bionomic Algorithm (MB), designed to efficiently comput...
The objective of this research is to present a 2-step data processing methodology for return series, to neutralize the potential effects of consecutive large returns and microstructure noise in jump detection test results.