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Some of the difficulties in analyzing time-varying \nunivariate and multivariate conditional volatility and stochastic \nvolatility include the number of parameters to be estimated and the \ncomputational complexities associated with multivariate conditional \nvolatility models and both univariate and multivariate stochastic \nvolatility models. For these reasons, among others, automated inference \nin its present state requires modifications and extensions for modeling \nin empirical financial econometrics. As a contribution to the \ndevelopment of automated inference in modeling volatility, 20 important \nissues in the specification, estimation, and testing of conditional and \nstochastic volatility models are discussed. A \u201cpotential for \nautomation rating\u201d (PAR) index and recommendations regarding the \npossibilities for automated inference in modeling financial volatility \nare given in each case.<\/jats:p>","DOI":"10.1017\/s0266466605050140","type":"journal-article","created":{"date-parts":[[2005,4,25]],"date-time":"2005-04-25T14:15:35Z","timestamp":1114438535000},"page":"232-261","source":"Crossref","is-referenced-by-count":260,"title":["AUTOMATED INFERENCE AND LEARNING IN MODELING FINANCIAL VOLATILITY"],"prefix":"10.1017","volume":"21","author":[{"given":"Michael","family":"McAleer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2005,2,8]]},"reference":[{"key":"S0266466605050140_ref061","doi-asserted-by":"crossref","unstructured":"Ling, S. & M. 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