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Recurrence Quantification Analysis for Detecting Financial Volatility

Student: Archa Aleksandr

Supervisor: Andrey Dmitriev

Faculty: Graduate School of Business

Educational Programme: Big Data Systems (Master)

Year of Graduation: 2019

The main goal of the thesis is analysis of financial time series' volatility with theory of nonlinear dynamical systems. In particular, recurrence quantification analysis (RQA). Was shown the methods for detecting high-volatility periods in Russian stock market. In the paper were described existing methods of volatility measurement and new were proposed with application of developed algorithms.

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