{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/153697"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/153697","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Forecasting Equity Volatility Dynamics with Markov-Switching EGARCH Models","abstract":"Understanding and anticipating stock market volatility enables better portfolio management. We forecast US equity volatility with a Markov-Switching EGARCH model with one high and one low volatility regime. We show that this model contains similar information about future volatility as the VIX Index. It also outperforms single-regime GARCH and EGARCH models. Moreover, the model’s 1-day ahead regime predictions are economically significant: market volatility and kurtosis, equity risk premia, and stock-bond relations shift when the model forecasts a regime change.","abstract_html":"Understanding and anticipating stock market volatility enables better portfolio management. We forecast US equity volatility with a Markov-Switching EGARCH model with one high and one low volatility regime. We show that this model contains similar information about future volatility as the VIX Index. It also outperforms single-regime GARCH and EGARCH models. Moreover, the model’s 1-day ahead regime predictions are economically significant: market volatility and kurtosis, equity risk premia, and stock-bond relations shift when the model forecasts a regime change.","abstract_has_math":false,"creators":["Dennis-Sharma, Tyson"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Sloan School of Management","school":null,"contributors":[],"advisors":["Kogan, Leonid"],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-02","date_published":"2024-02","updated_at":"2026-07-22T22:21:43Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"rights_urls":["https://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/153697","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Kogan, Leonid"]},{"key":"dc:contributor.department","label":"Department","values":["Sloan School of Management"]},{"key":"dc:creator","label":"Author","values":["Dennis-Sharma, Tyson"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-03-13T13:27:34Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-03-13T13:27:34Z"]},{"key":"dc:date.issued","label":"Date","values":["2024-02"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master","Master of Finance"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://rightsstatements.org/page/InC-EDU/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/153697"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Understanding and anticipating stock market volatility enables better portfolio management. We forecast US equity volatility with a Markov-Switching EGARCH model with one high and one low volatility regime. We show that this model contains similar information about future volatility as the VIX Index. It also outperforms single-regime GARCH and EGARCH models. Moreover, the model’s 1-day ahead regime predictions are economically significant: market volatility and kurtosis, equity risk premia, and stock-bond relations shift when the model forecasts a regime change."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Fin."]},{"key":"dc:title","label":"Title","values":["Forecasting Equity Volatility Dynamics with Markov-Switching EGARCH Models"]}]}],"canonical_facts":{"dc:contributor.advisor":["Kogan, Leonid"],"dc:contributor.department":["Sloan School of Management"],"dc:creator":["Dennis-Sharma, Tyson"],"dc:date.accessioned":["2024-03-13T13:27:34Z"],"dc:date.available":["2024-03-13T13:27:34Z"],"dc:date.issued":["2024-02"],"dc:description.abstract":["Understanding and anticipating stock market volatility enables better portfolio management. We forecast US equity volatility with a Markov-Switching EGARCH model with one high and one low volatility regime. We show that this model contains similar information about future volatility as the VIX Index. It also outperforms single-regime GARCH and EGARCH models. Moreover, the model’s 1-day ahead regime predictions are economically significant: market volatility and kurtosis, equity risk premia, and stock-bond relations shift when the model forecasts a regime change."],"dc:description.degree":["M.Fin."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/153697"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"dc:rights.uri":["https://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Forecasting Equity Volatility Dynamics with Markov-Switching EGARCH Models"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Finance"]},"updated_at":"2026-07-22T22:21:43Z"}