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The hybrid information capturing methodology proposed above can thus be supposed to address complex nonlinear dynamics behind the noise of financial data. To this end, a key approach to ascertain membership values for financial sample data is introduced in order to build the FSVM in terms of statistical characteristics of the financial data from the prior wavelet denoising stage. Moreover, the GARCH model is also employed in the final step so that the test errors from the preliminary test based on FSVM are deeply analyzed to capture missed price volatility information which are often neglected by existing approaches involving the traditional SVM models. The methodology proposed is thus enabled to sufficiently tackle the styled facts, such as nonlinearity, instability, strong noise, skewed distribution, and so on, because two factors of influencing price volatility, that is, the market factor and the time series factor, are all accounted for, and its prediction outperformance appears in empirical analysis of S&amp;P 500 index.<\/jats:p>","DOI":"10.3233\/jifs-169598","type":"journal-article","created":{"date-parts":[[2018,7,6]],"date-time":"2018-07-06T12:00:32Z","timestamp":1530878432000},"page":"405-414","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["A hybrid information capturing methodology for price volatility and its application to financial markets"],"prefix":"10.1177","volume":"35","author":[{"given":"Chuanhe","family":"Shen","sequence":"first","affiliation":[{"name":"Institute of Financial Engineering, Shandong Women\u2019s University, Changqing University Science and Technology Park, Jinan, P.R. 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