dc.creatorCastillo Paredes, Laura Daniela
dc.creatorRamoni Perazzi, Josefa
dc.creatorUniversidad de los Andes (Venezuela)
dc.date.accessioned2017-06-29T22:13:43Z
dc.date.available2017-06-29T22:13:43Z
dc.date.created2017-06-29T22:13:43Z
dc.date.issued2017-02-08
dc.identifierCastillo Paredes, L. D. y Ramoni Perazzi, J. (2017). La volatilidad del tipo de cambio paralelo en Venezuela 2005-2015. Revista Apuntes del CENES, 36(63), p.95-135. DOI: https://doi.org/10.19053/01203053.v36.n63.2017.5312. http://repositorio.uptc.edu.co/handle/001/1737
dc.identifier0120-3053
dc.identifier2256-5779 En línea
dc.identifierhttps://repositorio.uptc.edu.co/handle/001/1737
dc.identifier10.19053/01203053.v36.n63.2017.5312
dc.description.abstractEl tipo de cambio paralelo constituye una de las principales variables económicas para la toma de decisiones en Venezuela. Para analizar el comportamiento de esta variable tomando en cuenta sus características inherentes, exceso de curtosis, persistencia y asimetría, se hace una síntesis teórica de los principales modelos estocásticos de volatilidad y se estima un conjunto de modelos. El modelo que mejor ajusta el comportamiento de la variable es un EGARCH (1,1), que captura el efecto asimétrico de las perturbaciones estocásticas sobre la serie. Ante choques negativos (depreciación del tipo de cambio paralelo), la volatilidad asociada se incrementa, pero para choques positivos (apreciación del tipo de cambio paralelo), se mantiene constante.
dc.description.abstractABSTRACT: The parallel exchange rate is one of the most important economic variables for decision making in Venezuela. With the purpose of analyzing the exchange rate considering its inherent characteristics, excess kurtosis, persistence and asymmetry, a theoretical synthesis of the main stochastic volatility models is made and a set of models is estimated. The results show that the model that best explains its behavior is an EGARCH (1.1); it captures the asymmetric effect of stochastic perturbations on the series. Negative shocks (depreciation of the parallel exchange rate) increase the volatility while positive shocks (appreciation of the parallel exchange rate) seem not to exert any effect.
dc.languagespa
dc.publisherUniversidad Pedagógica y Tecnológica de Colombia
dc.relationAkaike, H. (1974). A New Look at the Statistical Model Identification. IEEE: Trans. Auto.Control, 19, 719-723.
dc.relationArias, F. (2006). El proyecto de investigación. Introducción a la metodología científica (5 ed.). Caracas: Episteme.
dc.relationBaillie, R. (2006). Modelling Volatility, Handbook of Econometrics (Vol. 1). In E. T. a. Patterson (Ed.) New York: Palgrave Macmillan
dc.relationBanco Central de Venezuela. (2016). Banco Central de Venezuela. Recuperado de http://www.bcv.org.ve/
dc.relationBernanke, B. & Frank, R. (2007). Principios de economía (3 ed.). Mdrid: McGraw Hill.
dc.relationBerndt, E., Hall, B., Hall, R. & Hausman, J. (1974). Estimation lnference in Nonlinear Structural Models. Annals of the Economic and Social Measurement, 4, 653-665.
dc.relationBollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedastic. Journal of Econometrics, (31), 307-327.
dc.relationBollerslev, T. & Wooldrige, J. (1992). Quasi-maximum Likelihood Estimation and Inference in Dynamic Models with Time-Varying Covariances. Econometric Reviews, 11(2), 143-172.
dc.relationBreusch, T. & Pagan, A. (1978). A Simple Test for Heterocedasticity and Random Coefficient Variance. Econometrica, 46, 1287-1294.
dc.relationBrock, W., Dechert, W., Scheinkman J. & LeBaron, B. (1996). A Test for Independence Based on the Borrelation Dimension. Econometric Reviews, 3(15), 197-235.
dc.relationCampbell, A. (1987). Stock Returns and Term Structure. Journal of Financial Economics, 18, 373-399.
dc.relationDing, Z., Granger, C. & Engle, R. (1993). A long memory property of stock market returns and a new model. Journal of Empirical Finance, 83-106.
dc.relationDornbusch, R. (1976). Expectations and Exchange Rate Dynamics. The Journal of Political Economy, 84, 1161-1176.
dc.relationDornbusch, R., Fischer, S. & Startz, R. (2009). Macroeconomía (10.ª ed.). México, D.F.: McGraw Hill.
dc.relationEcoanalítica. (2014, dic.). Entorno y política cambiaria. Caracas. Recuperado de http:// ecoanalitica.com/?wpfb_dl=179
dc.relationEngle, R. (1982). Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987-1007.
dc.relationEngle, R. & Bollerslev, T. (1986). Modelling the Persistence of Conditional Variance. Econometric Reviews, 5, 1-50.
dc.relationFama, M. (1963). Risk Returm and Equilibrium: Empirical Test. Jorunal of Financial Economics, 71, 607-636.
dc.relationFiglewski, S. (1997). Forecasting Volatility. Financial Markets, Institutions and Instruments, 6(1), 2-87.
dc.relationGlosten, L., Jagannathan, R. & Runkle, D. (1993). Relationships between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. Northwestern University: Mimeo.
dc.relationGodfrey, L. (1978). Testing Against General Autoregressive and Moving Average Models when the Regressors include Lagged Dependent Variables. Econometrica, 46, 1294-1302.
dc.relationHansen, B. (1992). Tests for Parameter Instability in Regressions with I(1) Processes. Journal of Business and Economic Statistics, 10, 321-336.
dc.relationHansen, P. & Lunde, A. . (2005). A forecast comparison of volatility models: does anything beat a GARCH (1,1)? Journal of Applied Econometrics, 20, 873-889.
dc.relationHarvey, A. (1981). The Econometric Analysis of Time Series. Oxford: Phillip Alan.
dc.relationHentschel, L. (1995). All in the Family Nesting Symmetric and Asymmetric GARCH Models. Journal of Financial Economics, 39, 71-104.
dc.relationHsieh, D. (1995). Nonlinear Dynamics in Financial Markets Evidence and Implications. Duke: Institute for Quantitative Research in Finance.
dc.relationMandelbrot, B. (1963). The Variation of Certain Speculative Prices. Journal of Business, 36, 394-419.
dc.relationMárquez, M. (2002). Modelo setar aplicado a la volatilidad de la rentabilidad de las acciones: algoritmos para su identificación. Tesis de Maestría en Estadística. Universitat Politècnica de Catalunya, Barcelona.
dc.relationMcLeod, A. & Li, W. (1983). Diagnostic Checking ARMA Time Series Models Using Squared Residual Autocorrelations. Journal of Time Series Analysis, 4, 269-273.
dc.relationMilhoj, A. (1987). A Multiplicative Parametrization of ARCH Models. Research Report 101. Copenhagen: Institute of Statistics, University of Copenhagen.
dc.relationNelson, D. B & Cao, C. Q. (1992). Inequality Constraints in the Univariate GARCH Model. Journal of Business & Economic Statistics, 10, 229-235.
dc.relationNelson, D. B. (1991). Conditional Heterocedasticity in Asset Returns: A New Approach. Econometrica, 59, 347-370.
dc.relationNyblom, J. (1989). Testing for the Constancy of Parameters Over Time. Journal of the American Statistical Association, 84(405), 223-230.
dc.relationPoon, S. & Granger, C. (2003). Forecasting Volatility in Financial Markets: A Review. Journal of Economic Literature, 41, 478-539
dc.relationPoterba, J. & Summers, L. (1986). The Persistence of Volatility and Stock Market Fluctuations. American Economic Review, 76, 1142-1151.
dc.relationRepública Bolivariana (2014, 20 de feb.). Decreto con Rango Fuerza y Valor de Ley del Régimen Cambiario y sus Ílicitos. Gaceta Oficial de la República Bolivariana, (6.126). Recuperado de www.tsj.gob.ve/gaceta-oficial
dc.relationSamuelson, P. & Nordhaus, W. (2010). Economía con aplicaciones a Latinoamérica (19 ed.). México D.F.: McGraw Hill.
dc.relationSánchez, A. & Reyes, M. (2006). Regularidades probabilísticas de las series financieras y la familia de modelos GARCH. Ciencia Ergo Sum, 13(2), 149-156.
dc.relationSchwarz, G. (1978). Estimating the Dimension of a Model. Annals of Statistics, 6, 461-464
dc.relationShibata, R. (2002). Information Criteria for Statistical Model Selection. Electron. Comm. Jpn. Pt. III, 85, 32–38.
dc.relationTaylor, S. (1986). Modelling Financial Time Series. New York: John Wiley
dc.relationThe Comprehensive R Archive Network –CRAN-. (2016, 20 de jul.). The R Project for Statistical Computing. Retrieved from http://cran.r-project.org/manuals.html
dc.relationTsay, R. (1986). Nonlinearity Test for Time Series. Biometrika, 76, 461-466.
dc.relationZakoian, J. (1990). Threshold Heteroskedastic Model. Paris: INSEE Mimeo.
dc.relationRevista Apuntes del CENES;Vol. 36, núm. 63(2017)
dc.rightshttps://creativecommons.org/licenses/by-nc/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAtribución-NoComercial 4.0 Internacional (CC BY-NC 4.0)
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.rightsCopyright (c) 2017 Apuntes del CENES
dc.sourcehttp://revistas.uptc.edu.co/index.php/cenes/article/view/5312/4733
dc.subjectModelos económicos - Venezuela
dc.subjectAnálisis de series de tiempo
dc.subjectCambio exterior - Investigaciones
dc.subjectConvertibilidad de la moneda
dc.subjectProcesos estocásticos
dc.subjectMercado de capitales
dc.subjectMétodo de momentos (Estadística)
dc.subjectModelos econométricos
dc.titleLa volatilidad del tipo de cambio paralelo en Venezuela 2005-2015
dc.typeArtículo de revista


Este ítem pertenece a la siguiente institución