{"id":{"repo_id":"rosario","oai_identifier":"oai:repository.urosario.edu.co:10336/19986"},"canonical_url":"https://search.dev.ndltd.org/etd/rosario/oai:repository.urosario.edu.co:10336/19986","repository":{"repo_id":"rosario","name":"Universidad del Rosario","base_url":"https://repository.urosario.edu.co/oai/request"},"display":{"title":"Estrategias de trading con Time Series Momentum","abstract":"Constructing a time-series momentum strategy involves the volatility-adjusted aggregation of univariate strategies and therefore relies heavily on the e ciency of the volatility estimator and on the quality of the momentum trading signal. Using a dataset with intra-day quotes of 18 assets from May 2017 to May 2019, we investigate these dependencies and their relation to time-series momentum pro tability. Momentum trading signals generated by tting a linear trend on the asset price path maximise the out-of-sample performance in small holding periods while minimising the portfolio turnover, hence dominating the ordinary momentum trading signal in literature, the sign of past returns. Regarding the volatility adjusted aggregation of univariate strategies, the Realized Volatility estimator did not present the best results as it was expected, however the Yang-Zhang range estimator and Garman and Klass Modi ed estimator constitute a good choice for volatility estimation in terms of maximising eficiency (Theorically) and minimising the ex-post portfolio turnover, althought the bias is not minimum.","abstract_html":"Constructing a time-series momentum strategy involves the volatility-adjusted aggregation of univariate strategies and therefore relies heavily on the e ciency of the volatility estimator and on the quality of the momentum trading signal. Using a dataset with intra-day quotes of 18 assets from May 2017 to May 2019, we investigate these dependencies and their relation to time-series momentum pro tability. Momentum trading signals generated by tting a linear trend on the asset price path maximise the out-of-sample performance in small holding periods while minimising the portfolio turnover, hence dominating the ordinary momentum trading signal in literature, the sign of past returns. Regarding the volatility adjusted aggregation of univariate strategies, the Realized Volatility estimator did not present the best results as it was expected, however the Yang-Zhang range estimator and Garman and Klass Modi ed estimator constitute a good choice for volatility estimation in terms of maximising eficiency (Theorically) and minimising the ex-post portfolio turnover, althought the bias is not minimum.","abstract_has_math":false,"creators":["Acero Ríos, Esstefanía"],"institution":"Universidad del Rosario","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-06-17","date_published":"2019-06-17","updated_at":"2026-07-27T20:46:36Z","subjects":["Trend-following","Time Series Momentum","Volatility Estimation","Trading Signals","Portafolio Turnover","Economía financiera","Análisis de inversiones","Tasa de retorno","Volatilidad","Modelos matemáticos"],"languages":["spa"],"rights":["info:eu-repo/semantics/openAccess"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["http://repository.urosario.edu.co/handle/10336/19986"],"render_values":[{"text":"http://repository.urosario.edu.co/handle/10336/19986","href":"http://repository.urosario.edu.co/handle/10336/19986","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.48713/10336_19986","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Acero Ríos, Esstefanía"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-06-17","2019-07-23T14:34:19Z"]},{"key":"dc:publisher","label":"Institution","values":["Universidad del Rosario","Facultad de Economía","Maestría en Finanzas Cuantitativas"]},{"key":"dc:type","label":"Dc Type","values":["info:eu-repo/semantics/masterThesis","info:eu-repo/semantics/acceptedVersion"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Trend-following","Time Series Momentum","Volatility Estimation","Trading Signals","Portafolio Turnover","Economía financiera","Análisis de inversiones","Tasa de retorno","Volatilidad","Modelos matemáticos"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["spa"]},{"key":"dc:rights","label":"Dc Rights","values":["info:eu-repo/semantics/openAccess"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://doi.org/10.48713/10336_19986","http://repository.urosario.edu.co/handle/10336/19986"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Constructing a time-series momentum strategy involves the volatility-adjusted aggregation of univariate strategies and therefore relies heavily on the e ciency of the volatility estimator and on the quality of the momentum trading signal. Using a dataset with intra-day quotes of 18 assets from May 2017 to May 2019, we investigate these dependencies and their relation to time-series momentum pro tability. Momentum trading signals generated by tting a linear trend on the asset price path maximise the out-of-sample performance in small holding periods while minimising the portfolio turnover, hence dominating the ordinary momentum trading signal in literature, the sign of past returns. Regarding the volatility adjusted aggregation of univariate strategies, the Realized Volatility estimator did not present the best results as it was expected, however the Yang-Zhang range estimator and Garman and Klass Modi ed estimator constitute a good choice for volatility estimation in terms of maximising eficiency (Theorically) and minimising the ex-post portfolio turnover, althought the bias is not minimum."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:source","label":"Dc Source","values":["T. Andersen and T. Bollerslev. Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review, 39(4):885{905, 1998.","A.-N. Balta and R. Kosowski. Improving time-series momentum strategies: The role of trading signals and volatility estimators. 2012.","O. E. Barndor -Nielsen and N. Shephard. Econometric analysis of realized volatility and its use in estimating stochastic volatility models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(2):253{280, 2002.","A. C. Bryhn and P. H. Dimberg. An operational de nition of a statistically meaningful trend. PLOS ONE, 6:1{9, 04 2011.","M. B. Garman and M. J. Klass. On the estimation of security price volatilities from historical data. The Journal of Business, 53(1):67{78, 1980.","P. Hansen and A. Lunde. Realized variance and market microstructure noise. Journal of Business and Economic Statistics, 24:127{161, 2006.","M. Parkinson. The extreme value method for estimating the variance of the rate of return. The Journal of Business, 53(1):61{65, 1980.","C. Pirrong. Momentum in futures markets. SSRN Electronic Journal, 02 2005. doi: 10.2139/ ssrn.671841.","T. J.Moskowitz, Y. H. Ooi, and L. H. Pedersen. Time series momentum. JournalofFinan- cialEconomics, 104(3):228{250, 2012.","L. C. G. Rogers and S. E. Satchell. Estimating variance from high, low and closing prices. Ann. Appl. Probab., 1(4):504{512, 11 1991. doi: 10.1214/aoap/1177005835.","M. W Brandt and J. Kinlay. Estimating historical volatility. 01 2005.","D. Yang and Q. Zhang. Drift-independent volatility estimation based on high, low, open, and close prices. The Journal of Business, 73(3):477{91, 2000.","instname:Universidad del Rosario","reponame:Repositorio Institucional EdocUR"]},{"key":"dc:title","label":"Title","values":["Estrategias de trading con Time Series Momentum"]}]}],"canonical_facts":{"dc:creator":["Acero Ríos, Esstefanía"],"dc:date":["2019-06-17","2019-07-23T14:34:19Z"],"dc:description":["Constructing a time-series momentum strategy involves the volatility-adjusted aggregation of univariate strategies and therefore relies heavily on the e ciency of the volatility estimator and on the quality of the momentum trading signal. Using a dataset with intra-day quotes of 18 assets from May 2017 to May 2019, we investigate these dependencies and their relation to time-series momentum pro tability. Momentum trading signals generated by tting a linear trend on the asset price path maximise the out-of-sample performance in small holding periods while minimising the portfolio turnover, hence dominating the ordinary momentum trading signal in literature, the sign of past returns. Regarding the volatility adjusted aggregation of univariate strategies, the Realized Volatility estimator did not present the best results as it was expected, however the Yang-Zhang range estimator and Garman and Klass Modi ed estimator constitute a good choice for volatility estimation in terms of maximising eficiency (Theorically) and minimising the ex-post portfolio turnover, althought the bias is not minimum."],"dc:format":["application/pdf"],"dc:identifier":["https://doi.org/10.48713/10336_19986","http://repository.urosario.edu.co/handle/10336/19986"],"dc:language":["spa"],"dc:publisher":["Universidad del Rosario","Facultad de Economía","Maestría en Finanzas Cuantitativas"],"dc:rights":["info:eu-repo/semantics/openAccess"],"dc:source":["T. Andersen and T. Bollerslev. Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review, 39(4):885{905, 1998.","A.-N. Balta and R. Kosowski. Improving time-series momentum strategies: The role of trading signals and volatility estimators. 2012.","O. E. Barndor -Nielsen and N. Shephard. Econometric analysis of realized volatility and its use in estimating stochastic volatility models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(2):253{280, 2002.","A. C. Bryhn and P. H. Dimberg. An operational de nition of a statistically meaningful trend. PLOS ONE, 6:1{9, 04 2011.","M. B. Garman and M. J. Klass. On the estimation of security price volatilities from historical data. The Journal of Business, 53(1):67{78, 1980.","P. Hansen and A. Lunde. Realized variance and market microstructure noise. Journal of Business and Economic Statistics, 24:127{161, 2006.","M. Parkinson. The extreme value method for estimating the variance of the rate of return. The Journal of Business, 53(1):61{65, 1980.","C. Pirrong. Momentum in futures markets. SSRN Electronic Journal, 02 2005. doi: 10.2139/ ssrn.671841.","T. J.Moskowitz, Y. H. Ooi, and L. H. Pedersen. Time series momentum. JournalofFinan- cialEconomics, 104(3):228{250, 2012.","L. C. G. Rogers and S. E. Satchell. Estimating variance from high, low and closing prices. Ann. Appl. Probab., 1(4):504{512, 11 1991. doi: 10.1214/aoap/1177005835.","M. W Brandt and J. Kinlay. Estimating historical volatility. 01 2005.","D. Yang and Q. Zhang. Drift-independent volatility estimation based on high, low, open, and close prices. The Journal of Business, 73(3):477{91, 2000.","instname:Universidad del Rosario","reponame:Repositorio Institucional EdocUR"],"dc:subject":["Trend-following","Time Series Momentum","Volatility Estimation","Trading Signals","Portafolio Turnover","Economía financiera","Análisis de inversiones","Tasa de retorno","Volatilidad","Modelos matemáticos"],"dc:title":["Estrategias de trading con Time Series Momentum"],"dc:type":["info:eu-repo/semantics/masterThesis","info:eu-repo/semantics/acceptedVersion"]},"updated_at":"2026-07-27T20:46:36Z"}