{"id":{"repo_id":"east-anglia","oai_identifier":"oai:ueaeprints.uea.ac.uk:48762"},"canonical_url":"https://search.dev.ndltd.org/etd/east-anglia/oai:ueaeprints.uea.ac.uk:48762","repository":{"repo_id":"east-anglia","name":"University of East Anglia","base_url":"https://ueaeprints.uea.ac.uk/cgi/oai2"},"display":{"title":"Modelling skewness in Financial data","abstract":"The first systematic analysis of the skew-normal distribution in a scalar case is done by Azzalini (1985). Unlike most of the skewed distributions, the skew-normal distribution allows continuity of the passage from the normal distribution to the skew-normal distribution and is mathematically tractable. The skew-normal distribution and its extensions have been applied in lots of financial applications. This thesis contributes to the recent development of the skew-normal distribution by, firstly, analyzing the the properties of annualization and time-scaling of the skew-normal distribution under heteroskedasticity which, in turn allows us to model financial time series with the skew-normal distribution at different time scales; and, secondly, extending the Skew-Normal-GARCH(1,1) model of Arellano-Valle and Azzalini (2008) to allow for time-varying skewness. Chapter one analyses the performance of the time scaling rules for computing volatility and skewness under the Skew-Normal-GARCH(1,1) model at multiple horizons by simulation and applies the simulation results to the Skew-Normal-Black-Scholes option pricing model introduced by Corns and Satchell (2007). Chapter two tests the Skew-Normal Black-Scholes model empirically. Chapter three extends the Skew-Normal-GARCH(1,1) model to allow for time-varying skewness. The time-varying-skewness adjusted model is then applied to test the relationship between heterogeneous beliefs, shortsale restrictions and market declines.","abstract_html":"The first systematic analysis of the skew-normal distribution in a scalar case is done by Azzalini (1985). Unlike most of the skewed distributions, the skew-normal distribution allows continuity of the passage from the normal distribution to the skew-normal distribution and is mathematically tractable. The skew-normal distribution and its extensions have been applied in lots of financial applications. This thesis contributes to the recent development of the skew-normal distribution by, firstly, analyzing the the properties of annualization and time-scaling of the skew-normal distribution under heteroskedasticity which, in turn allows us to model financial time series with the skew-normal distribution at different time scales; and, secondly, extending the Skew-Normal-GARCH(1,1) model of Arellano-Valle and Azzalini (2008) to allow for time-varying skewness. Chapter one analyses the performance of the time scaling rules for computing volatility and skewness under the Skew-Normal-GARCH(1,1) model at multiple horizons by simulation and applies the simulation results to the Skew-Normal-Black-Scholes option pricing model introduced by Corns and Satchell (2007). Chapter two tests the Skew-Normal Black-Scholes model empirically. Chapter three extends the Skew-Normal-GARCH(1,1) model to allow for time-varying skewness. The time-varying-skewness adjusted model is then applied to test the relationship between heterogeneous beliefs, shortsale restrictions and market declines.","abstract_has_math":false,"creators":["Shum, Wai Yan"],"institution":"University of East Anglia","degree_name":"phd","degree_level":"doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-03","date_published":"2014-03","updated_at":"2026-07-24T02:12:01Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Shum, Wai Yan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014-03"]},{"key":"dc:date.issued","label":"Date","values":["2014-03"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["School of Economics"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of East Anglia"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://ueaeprints.uea.ac.uk/id/eprint/48762/"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["phd"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://ueaeprints.uea.ac.uk/id/eprint/48762/1/Modelling_skewness_in_Financial_data_-_Ann%2C_Wai_Yan_SHUM.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The first systematic analysis of the skew-normal distribution in a scalar case is done by Azzalini (1985). Unlike most of the skewed distributions, the skew-normal distribution allows continuity of the passage from the normal distribution to the skew-normal distribution and is mathematically tractable. The skew-normal distribution and its extensions have been applied in lots of financial applications. This thesis contributes to the recent development of the skew-normal distribution by, firstly, analyzing the the properties of annualization and time-scaling of the skew-normal distribution under heteroskedasticity which, in turn allows us to model financial time series with the skew-normal distribution at different time scales; and, secondly, extending the Skew-Normal-GARCH(1,1) model of Arellano-Valle and Azzalini (2008) to allow for time-varying skewness. Chapter one analyses the performance of the time scaling rules for computing volatility and skewness under the Skew-Normal-GARCH(1,1) model at multiple horizons by simulation and applies the simulation results to the Skew-Normal-Black-Scholes option pricing model introduced by Corns and Satchell (2007). Chapter two tests the Skew-Normal Black-Scholes model empirically. Chapter three extends the Skew-Normal-GARCH(1,1) model to allow for time-varying skewness. The time-varying-skewness adjusted model is then applied to test the relationship between heterogeneous beliefs, shortsale restrictions and market declines."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Modelling skewness in Financial data"]}]}],"canonical_facts":{"dc:creator":["Shum, Wai Yan"],"dc:date":["2014-03"],"dc:date.issued":["2014-03"],"dc:description.abstract":["The first systematic analysis of the skew-normal distribution in a scalar case is done by Azzalini (1985). Unlike most of the skewed distributions, the skew-normal distribution allows continuity of the passage from the normal distribution to the skew-normal distribution and is mathematically tractable. The skew-normal distribution and its extensions have been applied in lots of financial applications. This thesis contributes to the recent development of the skew-normal distribution by, firstly, analyzing the the properties of annualization and time-scaling of the skew-normal distribution under heteroskedasticity which, in turn allows us to model financial time series with the skew-normal distribution at different time scales; and, secondly, extending the Skew-Normal-GARCH(1,1) model of Arellano-Valle and Azzalini (2008) to allow for time-varying skewness. Chapter one analyses the performance of the time scaling rules for computing volatility and skewness under the Skew-Normal-GARCH(1,1) model at multiple horizons by simulation and applies the simulation results to the Skew-Normal-Black-Scholes option pricing model introduced by Corns and Satchell (2007). Chapter two tests the Skew-Normal Black-Scholes model empirically. Chapter three extends the Skew-Normal-GARCH(1,1) model to allow for time-varying skewness. The time-varying-skewness adjusted model is then applied to test the relationship between heterogeneous beliefs, shortsale restrictions and market declines."],"dc:format":["application/pdf"],"dc:identifier.uri":["https://ueaeprints.uea.ac.uk/id/eprint/48762/1/Modelling_skewness_in_Financial_data_-_Ann%2C_Wai_Yan_SHUM.pdf"],"dc:language":["en"],"dc:publisher.department":["School of Economics"],"dc:publisher.institution":["University of East Anglia"],"dc:relation.isreferencedby":["https://ueaeprints.uea.ac.uk/id/eprint/48762/"],"dc:title":["Modelling skewness in Financial data"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["doctoral"],"dc:type.qualificationname":["phd"]},"updated_at":"2026-07-24T02:12:01Z"}