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数量经济与金融系列讲座410期:Testing for Neglected Nonlinearity in the Conditional Quantile Using Neural Networks

  发布日期:2019-12-24  浏览次数:

主题:Testing for Neglected Nonlinearity in the Conditional Quantile Using Neural Networks

主讲:Georg Keilbar,School of Business and Economics,Humboldt-Universität zu Berlin

时间:2020年1月9日 10:00-11:30

地点:半岛·体育bob官方网站714会议室

摘要:We propose a specifification test for the conditional quantile by comparing a linearmodel to an alternative model represented by a neural network. The test is formulatedas a testing problem with nuisance parameters under the alternative. We considerthree test statistics, theaverage, theexponential-averageand thesupremum Waldstatistic as functions of a Wald process and derive their asymptotic distribution. Tosimulate critical values, we propose a consistent bootstrap procedure. Further, weshow the asymptotic optimality of theaverageandexponential-average Waldtestagainst local alternatives in a correctly specifified maximum likelihood setting. In anempirical application, we examine the nonlinearity of systemic risk in the frameworkof conditional value-at-risk (CoVaR).

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