{"id":{"repo_id":"humboldt-diss","oai_identifier":"oai:edoc.hu-berlin.de:18452/14762"},"canonical_url":"https://search.dev.ndltd.org/etd/humboldt-diss/oai:edoc.hu-berlin.de:18452/14762","repository":{"repo_id":"humboldt-diss","name":"Humboldt Universität zu Berlin","base_url":"https://edoc.hu-berlin.de/server/oai/request"},"display":{"title":"Uniform Confidence Band for Pricing Kernels","abstract":"Pricing kernels implicit in option prices play a key role in assessing the risk aversion over equity returns. We deal with nonparametric estimation of the pricing kernel (Empirical Pricing Kernel) given by the ratio of the risk-neutral density estimator and the subjective density estimator. The former density can be represented as the second derivative w.r.t. the European call option price function, which we estimate by nonparametric regression. The subjective density is estimated nonparametrically too. In this framework, we develop the asymptotic distribution theory of the EPK in the L1 sense. Particularly, to evaluate the overall variation of the pricing kernel, we develop a uniform confidence band of the EPK. Furthermore, as an alternative to the asymptotic approach, we propose a bootstrap confidence band. The developed theory is helpful for testing parametric specifications of pricing kernels and has a direct extension to estimating risk aversion patterns. The established results are assessed and compared in a Monte-Carlo study. As a real application, we test risk aversion over time induced by the EPK.","abstract_html":"Pricing kernels implicit in option prices play a key role in assessing the risk aversion over equity returns. We deal with nonparametric estimation of the pricing kernel (Empirical Pricing Kernel) given by the ratio of the risk-neutral density estimator and the subjective density estimator. The former density can be represented as the second derivative w.r.t. the European call option price function, which we estimate by nonparametric regression. The subjective density is estimated nonparametrically too. In this framework, we develop the asymptotic distribution theory of the EPK in the L1 sense. Particularly, to evaluate the overall variation of the pricing kernel, we develop a uniform confidence band of the EPK. Furthermore, as an alternative to the asymptotic approach, we propose a bootstrap confidence band. The developed theory is helpful for testing parametric specifications of pricing kernels and has a direct extension to estimating risk aversion patterns. The established results are assessed and compared in a Monte-Carlo study. As a real application, we test risk aversion over time induced by the EPK.","abstract_has_math":false,"creators":["Wang, Weining"],"institution":"Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2010,"date_issued":"2010-01-19","date_published":"2010-01-19","updated_at":"2026-08-21T16:45:14Z","subjects":["Bootstrap","Kernel Smoothing","Nonparametric Fitting","Empirical Pricing Kernel","Confidence band"],"languages":["eng"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.18452/14110"],"render_values":[{"text":"https://doi.org/10.18452/14110","href":"https://doi.org/10.18452/14110","code":true}]}]},"links":{"outbound_url":"https://edoc.hu-berlin.de/18452/14762","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"source_record":{"url":"https://edoc.hu-berlin.de/server/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Aedoc.hu-berlin.de%3A18452%2F14762","prefix":"dim"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Wang, Weining"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2017-06-18T02:21:58Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2017-06-18T02:21:58Z"]},{"key":"dc:date.issued","label":"Date","values":["2010-01-19"]},{"key":"dc:publisher","label":"Institution","values":["Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät"]},{"key":"dc:type","label":"Dc Type","values":["masterThesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Bootstrap","Kernel Smoothing","Nonparametric Fitting","Empirical Pricing Kernel","Confidence band"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.18452/14110"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://edoc.hu-berlin.de/18452/14762"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Pricing kernels implicit in option prices play a key role in assessing the risk aversion over equity returns. We deal with nonparametric estimation of the pricing kernel (Empirical Pricing Kernel) given by the ratio of the risk-neutral density estimator and the subjective density estimator. The former density can be represented as the second derivative w.r.t. the European call option price function, which we estimate by nonparametric regression. The subjective density is estimated nonparametrically too. In this framework, we develop the asymptotic distribution theory of the EPK in the L1 sense. Particularly, to evaluate the overall variation of the pricing kernel, we develop a uniform confidence band of the EPK. Furthermore, as an alternative to the asymptotic approach, we propose a bootstrap confidence band. The developed theory is helpful for testing parametric specifications of pricing kernels and has a direct extension to estimating risk aversion patterns. The established results are assessed and compared in a Monte-Carlo study. As a real application, we test risk aversion over time induced by the EPK."]},{"key":"dc:title","label":"Title","values":["Uniform Confidence Band for Pricing Kernels"]}]}],"canonical_facts":{"dc:creator":["Wang, Weining"],"dc:date.accessioned":["2017-06-18T02:21:58Z"],"dc:date.available":["2017-06-18T02:21:58Z"],"dc:date.issued":["2010-01-19"],"dc:description.abstract":["Pricing kernels implicit in option prices play a key role in assessing the risk aversion over equity returns. We deal with nonparametric estimation of the pricing kernel (Empirical Pricing Kernel) given by the ratio of the risk-neutral density estimator and the subjective density estimator. The former density can be represented as the second derivative w.r.t. the European call option price function, which we estimate by nonparametric regression. The subjective density is estimated nonparametrically too. In this framework, we develop the asymptotic distribution theory of the EPK in the L1 sense. Particularly, to evaluate the overall variation of the pricing kernel, we develop a uniform confidence band of the EPK. Furthermore, as an alternative to the asymptotic approach, we propose a bootstrap confidence band. The developed theory is helpful for testing parametric specifications of pricing kernels and has a direct extension to estimating risk aversion patterns. The established results are assessed and compared in a Monte-Carlo study. As a real application, we test risk aversion over time induced by the EPK."],"dc:identifier.doi":["https://doi.org/10.18452/14110"],"dc:identifier.uri":["https://edoc.hu-berlin.de/18452/14762"],"dc:language.iso":["eng"],"dc:publisher":["Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Bootstrap","Kernel Smoothing","Nonparametric Fitting","Empirical Pricing Kernel","Confidence band"],"dc:title":["Uniform Confidence Band for Pricing Kernels"],"dc:type":["masterThesis"]},"updated_at":"2026-08-21T16:45:14Z"}