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dc.contributor.authorFonseca Torres, John Walter
dc.date.accessioned2019-11-29T16:34:10Z
dc.date.available2019-11-29T16:34:10Z
dc.date.issued2019-10-21
dc.identifier.urihttp://hdl.handle.net/10818/38517
dc.description33 páginases_CO
dc.description.abstractLos administradores de portafolio, enfrentan diariamente entornos volátiles como consecuencia de la interacción de instrumentos financieros. La adecuada gestión del riesgo con herramientas de información y seguimiento permiten señalar cambios en las cotizaciones de diferentes activos. No obstante, estas herramientas tienen en común la presentación de datos de forma individual, desconociendo la alta asociación de los mercados financieros. Los escenarios de tensión en los mercados mundiales incrementan la aversión al riesgo de los inversionistas e impactan la rentabilidad de los portafolios. Existen indicadores que se encargan de monitorear los niveles de volatilidad de diferentes activos. El VIX (Chicago Board Options Exchange SPX Volatility Index), es el más reconocido y refleja un estimado del mercado de la volatilidad futura sobre el índice accionario S&P. Sin embargo, se concentra en el entorno de renta variable, excluyendo las interacciones que se presentan entre los diferentes activos y mercados. Con el objetivo de subsanar las limitaciones descritas, se propone el diseño de un instrumento de medición de volatilidad que incluya un conjunto de diferentes activos de los principales mercados financieros. Este nuevo indicador pretende capturar el efecto de interacción de los mercados y por tanto recogerá de forma más amplia las fluctuaciones de los activos que componen dicho índice.
dc.formatapplication/pdfes_CO
dc.language.isospaes_CO
dc.publisherUniversidad de La Sabanaes_CO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceUniversidad de La Sabana
dc.sourceIntellectum Repositorio Universidad de La Sabana
dc.subjectPortafolio de inversiones
dc.subjectCambio organizacional
dc.subjectInversiones
dc.subjectRentabilidad
dc.subjectMercados financieros
dc.titlePropuesta de construcción de un índice volatilidad diversificadoes_CO
dc.typemasterThesises_CO
dc.publisher.programMaestría en Gerencia de Inversiónes_CO
dc.publisher.departmentEscuela Internacional de Ciencias Económicas y Administrativases_CO
dc.identifier.local275266
dc.identifier.localTE10461
dc.type.hasVersionpublishedVersiones_CO
dc.rights.accessRightsrestrictedAccesses_CO
dc.creator.degreeMagíster en Gerencia de Inversiónes_CO
dcterms.referencesEngle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, Vol 50, pp 987–1007.eng
dcterms.referencesLombardi, M. and Schrimpf, A. (2014) Concepto de volatilidad y la prima de riesgo. Bank for International Settlements BIS Quarterly Review, informe trimestral del PIB, pp 12-13.eng
dcterms.referencesAboura, S. and Chevallier, J. (2015) Geographical diversification with a World Volatility Index. Journal of Multinational Financial Management, Vol 30, issue C, 62-82.eng
dcterms.referencesMacDonald, R. Sogiakas, V. and Tsopanakis, A. (2018) Volatility co-movements and spilloover effects within the Eurozone economies: A multivariate GARCH approach using the Financial Stress Index. Journal of International Financial Markets, Institutions and Money, 52, pp. 17-36.eng
dcterms.referencesPattipeilohy,C. and Schrimpf, A. (2014) Volatility stirs, markets unshaken. Bank for International Settlements BIS Quarterly Revieweng
dcterms.referencesGarleanu, N. Pedersen, L. and Poteshman, A. (2009) Demand-based option pricing. Review of Financial Studies, Vol 22, pp. 4259-99.eng
dcterms.referencesVladyslav, S. and Turner, G. (2018) The equity market turbulence-The role of Exchange traded volatility products, Volatility is back. Bank for International Settlements BIS Quarterly Revieweng
dcterms.referencesBekaert, G. Hoerova, M. and Lo Duca, M. (2013) Risk, uncertainty and monetary policy. Journal of Monetary Economics, Vol 60, pp. 771–88.eng
dcterms.referencesBlack, F. (1976) Studies of stock price volatility changes. Proceedings of the 1976 Meetings of the American Statistical Association, Business and Economic Statistics Section 1976, pp 177-81.eng
dcterms.referencesPindyck, R.(1984) Risk, inflationand the stock market. American Economic Review, Vol 74, pp 335- 51.eng
dcterms.referencesShin, H S. (2010) Risk and liquidity, Oxford University Press, Clarendon Lectures in Finance. Journal of Financial Intermediation 19: 418-437.eng
dcterms.referencesAdrian, T. and Boyarchenko, N. (2012) Intermediary leverage cycles and financial stability; Federal Reserve Bank of New York, Staff Reports, no 576.eng
dcterms.referencesBorio, C. and Drehmann, M. (2009) Towards an operational framework for financial stability: “fuzzy” measurement and its consequences. Bank for International Settlements BIS working paper no 284eng
dcterms.referencesMiyajima, K. and Shim, I. (2014) Asset managers in emerging market economies. Bank for International Settlements BIS Quarterly Review, pp 19-34eng
dcterms.referencesOfficer, R.R. (1973) The variability of the market factor of the New York Stock Exchange. The Journal of Business, Vol 46, pp. 434–53.eng
dcterms.referencesSchwert, G.W. (1989) Why does stock market volatility change over time?. The Journal of Finance, Vol XLIV, No 5eng
dcterms.referencesParkinson, M. (1980) The extreme value method for estimating the variance of the rate of return. Journal of Business, Vol 53, pp 61–65.eng
dcterms.referencesYin, L. (2015) Investigating robust estimation and forecasting of volatilities of futures with interquartile range models. Journal of Finance and Economics, Vol 3, pp 1-10.eng
dcterms.referencesChou, R. Y. (2005) Forecasting financial volatilities with extreme values: The Conditional Autorregresive Range (CARR) model, Vol 37, issue 3, pp 567-82.eng
dcterms.referencesTan, S. Aur, k. Chan, J. Ng, K. and Mohamed, I. (2019) Quantile range-based volatility measure for modelling and forecasting volatility using high frequency data, The North American Journal of Economics and Finance, Vol 47, pp 537-51.eng
dcterms.referencesAndersen, T. Bollerslev, T. and Meddahi, N. (2004) Analytical Evaluation of Volatility Forecasts, International Economic Review , Vol 45, pp 1079-1110.eng
dcterms.referencesBaker, S. Bloom, N. and Davis, S.J. (2015) Measuring economic policy uncertainty, National Bureau of Economic Research, No 21633.eng
dcterms.referencesTobback, E. Naudts, H. Daelemans, W. Junqé de Fortuny, E. and Martens, D. (2018) Belgian economic policy uncertainty index: Improvement through text mining, International Journal of Forecasting, Elsevier, Vol 34, pp 355-65.eng
dcterms.referencesWhaley, R. (2009) Understanding the VIX, The Journal of Portafolio Management. 35, pp 98–1eng
dcterms.referencesCarr, P. and Wu, L. (2006) A tale of two indices. The Journal of Derivatives 13, pp 13–29.eng
dcterms.referencesGonzalez, M. (2015) Model Free Volatility indexes in the financial literature: A Review. International Review of Economics and Finance, Elsevier, Vol 40, pp 141-59.eng
dcterms.referencesPeng, Y. and Lon NG, W. (2012) Analysing financial contagion and asymmetric market dependence with volatily índices via copulas, Annals of Finance, Vol 8, Issue 1, pp 49-74.eng
dcterms.referencesBekaert, G. Hoerova, M. and Lo Duca, M. (2013) Risk, uncertainty and monetary policy. Journal of Monetary Economics, Vol 60, pp 771–88.eng
dcterms.referencesZhang, K. and Chan, L. (2009) Efficient factor GARCH models and factor-DCC models. Quantitative Finance, Vol 9, pp 71–91.eng
dcterms.referencesStock, J. and Watson, M. (2002) Forecasting using principal components from a large number of predictor. Journal of the American Statistical Association, Vol 97, No 460, pp 1167-79.eng
dcterms.referencesJiang, G. Konstantinidi, E. and Skiadopoulus, G. (2012) Volatility spillovers and the effect of news announcements. Journal of Banking and Finance, Elsevier, Vol 36, pp 2260-73.eng
dcterms.referencesKenourgios, D. (2014) On financial contagion and implied market volatility. International Review of Financial Analysis, Vol 34, pp 21-30.eng
dcterms.referencesSensoy, A. and Omole, J. (2018) Implied Volatility Indices: A Review and Extension in the Turkish Case. International Review of Financial Analysis, Vol 60, pp 151-61.eng
dcterms.referencesDe la Fuente, S (2011) Análisis Componentes Principales, Facultad Ciencias Económicas y empresariales, Universidad Autónoma de Madrid 2011.spa
dcterms.referencesPeres-Neto, P. Jackson, D. and Somers, K. (2005) How many principal components? Stopping rules for determining the number of non trivial axes revisited. Computational Statistics and Data analysis, Vol 49, pp 974-97.eng
dcterms.referencesBalacco, H. (1986) Algunas consideraciones sobre la definición de causalidad de Granger en el análisis econométrico, Económica, Vol 32, No 2, pp 207-55.spa
dcterms.referencesMontero, R. (2013) Test de Causalidad. Documentos de Trabajo en Economía Aplicada. Universidad de Granada. Españaspa
dcterms.referencesSchwert, G. (1983), Size and stock returns, and other empirical regularities, Journal of Finance Economics, Vol 12, issue 1,pp 1-158eng
dcterms.referencesEngle, R. F., and Manganelli, S. (2004) CAViaR: Conditional autoregressive value at risk by regression quantiles. Journal of Business & Economic Statistics, V 22, pp367–81.eng
dcterms.referencesGaviria, C. (2016) Regresión por Mínimos Cuadrados Parciales PLS aplicada a datos variedad Valuados. Universidad Nacional de Colombia.spa
dcterms.referencesBollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, V 31, pp 307–27.eng
dcterms.referencesForni, M. Hallin, M. Lippi, M y Reichlin, L. (2000) The Generalized Dynamic-Factor Model: Identification and Estimation. Review of Economics and Statistics, V 82, pp 540–54.eng
dcterms.referencesFernandez, F.J. (1997) A Dinamic Factor Model For Economic Time Series. Kybernetika, V 33, pp 583– 606.eng
dcterms.referencesDoz, C. Giannone, D. y Reichlin, L. (2011) A two steep estimator for large approximate Dynamic Factor Models based on Kalman filtering. Journal of econometrics, V 164, pp 188–205eng
dcterms.referencesBai, Z. Wong, W y Zhang, B. (2010) Multivariate linear and nonlinear causality test. Mathematics and Computers in Simulation, V 81, pp 5–17.eng
dcterms.referencesHiemstra, C. y Jones, JD. (1994) Testing for lienar and nonlinear Granger causality in the stock Price -volume relation. Journal of Finance, V 49, pp 1639–64.eng
dcterms.referencesBaek, E.G. y Brock, W.A. (1992) A general test for nonlinear Granger causality: bivariate model. Korea Development Institute.eng


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