Estrategias de trading con Time Series Momentum

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 2...

Descripción completa

Detalles Bibliográficos
Autor Principal: Acero Ríos, Esstefanía
Otros Autores: Ramirez, Hugo E.
Formato: Tesis de maestría (Master Thesis)
Lenguaje:Español (Spanish)
Publicado: Universidad del Rosario 2019
Materias:
Acceso en línea:http://repository.urosario.edu.co/handle/10336/19986
id ir-10336-19986
recordtype dspace
spelling ir-10336-199862019-09-19T12:37:01Z Estrategias de trading con Time Series Momentum Acero Ríos, Esstefanía Ramirez, Hugo E. Trend-following Time Series Momentum Volatility Estimation Trading Signals Portafolio Turnover Economía financiera Análisis de inversiones Tasa de retorno Volatilidad Modelos matemáticos 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. 2019-06-17 2019-07-23T14:34:19Z info:eu-repo/semantics/masterThesis info:eu-repo/semantics/acceptedVersion http://repository.urosario.edu.co/handle/10336/19986 spa info:eu-repo/semantics/openAccess application/pdf Universidad del Rosario Maestría en Finanzas Cuantitativas Facultad de Economía instname:Universidad del Rosario reponame:Repositorio Institucional EdocUR 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.
institution EdocUR - Universidad del Rosario
collection DSpace
language Español (Spanish)
topic Trend-following
Time Series Momentum
Volatility Estimation
Trading Signals
Portafolio Turnover
Economía financiera
Análisis de inversiones
Tasa de retorno
Volatilidad
Modelos matemáticos
spellingShingle Trend-following
Time Series Momentum
Volatility Estimation
Trading Signals
Portafolio Turnover
Economía financiera
Análisis de inversiones
Tasa de retorno
Volatilidad
Modelos matemáticos
Acero Ríos, Esstefanía
Estrategias de trading con Time Series Momentum
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.
author2 Ramirez, Hugo E.
author_facet Ramirez, Hugo E.
Acero Ríos, Esstefanía
format Tesis de maestría (Master Thesis)
author Acero Ríos, Esstefanía
author_sort Acero Ríos, Esstefanía
title Estrategias de trading con Time Series Momentum
title_short Estrategias de trading con Time Series Momentum
title_full Estrategias de trading con Time Series Momentum
title_fullStr Estrategias de trading con Time Series Momentum
title_full_unstemmed Estrategias de trading con Time Series Momentum
title_sort estrategias de trading con time series momentum
publisher Universidad del Rosario
publishDate 2019
url http://repository.urosario.edu.co/handle/10336/19986
_version_ 1645141028143890432
score 11,828437