The Volatility of the Parallel Exchange Rate in Venezuela 2005-2015

Main Article Content

Autores

Laura Daniela Castillo Paredes
Josefa Ramoni Perazzi

Abstract

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.

Keywords:

Article Details

Licence

By submitting articles for evaluation, the author agrees to transfer the publishing rights to Revista Apuntes del CENES for publishing in any format or mean and that the attached partial use license will be signed. To increase their visibility, documents are sent to databases and indexing systems also can be viewed on the website and Redalyc - EBSCO - ProQuest - EconLit - DOAJ -  Scielo - Dialnet - ESCI(WoS) - Latindex  - DOTEC - REPECERIH PLUS - The WZB library -  Actualidad Iberoamericana  -   Publindex  - VCU -  Econpapers - EconBib - Bibilat  -  REDIB  -   Crossref - Worldcat -  CLASE - SHERPA ROMEO - Academia - EconBiz - Socionet - Vlex

The journal is under licence Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

For CC licenses, the principle is the creative freedom. This system complements the copyright without oppose it. The content of the items is the responsibility of each author, and does not compromise in any way, magazine or institution.

Publishing and reproduction of titles, abstracts and full content for academic, scientific, cultural and nonprofit purposes is allowed, when the respective source is acknowledged. This work cannot be used for commercial purposes.

Apuntes del Cenes is an open access journal which means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, without asking prior permission from the publisher or the author. This is in accordance with the BOAI definition of open access.

Apuntes del Cenes  does not charge authors for submission or publication

Form 6 Copyright Transfer Form

When sending an article to submit to the Apuntes del CENES journal, the author(s) certify and accept:

1.That the article has not been accepted for evaluation in another journal, nor has it been published.
2.That, in case a publication of a previous version as a working paper (or 'gray literature') has been reported on a website, and that, in the case of publication being accepted, it will be removed from the Internet site, where
will leave only the title, abstract, keywords and hyperlink to the journal.

3.
That once published in Apuntes del CENES will not be published in another magazine.

References

Arias, F. (2006). El proyecto de investigación. Introducción a la metodología científica (Quinta ed.). Caracas: Episteme.

Baillie, R. (2006). Modelling Volatility, Handbook of Econometrics (Vol. 1). (E. T. a. Patterson, Ed.) New York: Palgrave Macmillan.

Banco Central de Venezuela. (2016). Obtenido de http://www.bcv.org.ve/

Berndt, E., Hall, B., Hall, R. y Hausman, J. . (1974). Estimation lnference in nonlinear Structural Models. Annals of the Economic and Social Measurement, Vol. 4, 653-665.

Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedastic. Journal of Econometrics(Nº 31), 307-327.

Bollerslev, T. y Wooldrige, J. (1992). Quasi-maximum likelihood estimation and inference in Dynamic models with time-varying covariances. Econometric Reviews, Vol. 11(Nº 2), 143-172.

Breusch, T.y Pagan, A. (1978). A Simple Test for Heterocedasticity and Random Coefficient Variance. Econometrica, Vol. 46, 1287-1294.

Brock, W., Dechert,W. , Scheinkman J, y LeBaron, B. (1996). A test for independence based on the correlation dimension. Econometric Reviews, Vol. 3(Nº 15), 197-235.

Campbell, A. (1987). Stock Returns and Term Structure. Journal of Financial Economics, Vol. 18, 373-399.

Decreto con Rango Fuerza y Valor de Ley del Régimen Cambiario y sus Ílicitos. (20 de febrero de 2014). Gaceta Oficial de la República Bolivariana Nº 6.126. Recuperado el 13 de julio de 2016, de www.tsj.gob.ve/gaceta-oficial

Ding, Z., Granger, C. y Engle, R. (1993). A long memory property of stock market returns and a new model. Journal of Empirical Finance, 83-106.

Dornbusch, R., Fischer, S. y Startz, R. (2009). Macroeconomía (Décima ed.). México, D.F.: McGraw Hill.

Ecoanalítica. (Diciembre de 2014). Entorno y Política Cambiaria. Caracas . Obtenido de http://ecoanalitica.com/?wpfb_dl=179

Engle, R. (1982). Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, Vol. 50(Nº 4), 987-1007.

Engle, R. y Bollerslev, T. (1986). Modelling the Persistence of Conditional Variance. Econometric Reviews, Vol. 5, 1-50.

Fama, M. (1963). Risk Returm and Equilibrium: Empirical Test. Jorunal of Financial Economics, Vol. 71, 607-636.

Figlewski, S. (1997). Forecasting Volatility. Financial Markets, Institutions and Instruments, Vol. 6(Nº 1), 2-87.

Glosten, L.; Jagannathan, R. y Runkle, D. (1993). Relationships between the expected value and the volatility of the nominal excess return on stocks. Northwestern University: Mimeo.

Godfrey, L. (1978). Testing Against General Autoregressive and Moving Average Models when the Regressors include Lagged Dependent Variables. Econometrica, Vol. 46, 1294-1302.

Hansen, B. (1992). Tests for Parameter Instability in Regressions with I(1) Processes. Journal of Business and Economic Statistics, Vol. 10, 321-336.

Hansen, P. y Lunde, A. . (2005). A forecast comparison of volatility models: does anything beat a GARCH (1,1)? Journal of Applied Econometrics, Vol. 20, 873-889.

Harvey, A. (1981). The Econometric Analysis of Time Series. Oxford: Phillip Alan.

Hentschel, L. (1995). All in the family Nesting symmetric and asymmetric GARCH models. Journal of Financial Economics, Vol.39, 71-104.

Hsieh, D. (1995). Nonlinear Dynamics in Financial Markets Evidence and Implications. Duke: Institute for Quantitative Research in Finance.

Klien, B. (1977). The demand for quality-adjusted cash balances: price uncertainty in the U.S. demand for money function. Journal of Political Economy, Vol. 85, 692-715.

Mandelbrot, B. (1963). The variation of certain speculative prices. Journal of Business, Vol. 36, 394-419.

Márquez, M. (2002). Modelo setar aplicado a la volatilidad de la rentabilidad de las acciones: algoritmos para su identificación. Tesis de Maestría en Estadística. Barcelona: Universitat Politècnica de Catalunya.

McLeod, A. y Li, W. (1983). Diagnostic checking ARMA time series models using squared residual autocorrelations. Journal of Time Series Analysis, Vol. 4, 269-273.

Milhoj, A. (. (1987). A multiplicative parametrization of ARCH models. Institute of Statistics.: Research Report 101, University of Copenhagen.

Nelson, D. B y Cao, C. Q. (1992). Inequality constraints in the univariate GARCH model. Journal of Business & Economic Statistics, Vol. 10, 229-235.

Nelson, D. B. (1991). Conditional Heterocedasticity in asset returns: a New Approach. Econometrica, Vol. 59, 347-370.

Nyblom, J. (1989). Testing for the Constancy of Parameters Over Time. Journal of the American Statistical Association, Vol. 84(Nº 405), 223-230.

Poon, S. y Granger, C. (2003). Forecasting Volatility in Financial Markets: A review. Journal of Economic Literature, Vol. XLI, 478-539.

Poterba, J. y Summers, L. (1986). The Persistence of Volatility and Stock Market Fluctuations. American Economic Review, Vol. 76, 1142-1151.

Sánchez, A. y Reyes, M. (2006). Regularidades probabilísticas de las series financieras y la familia de modelos GARCH. Ciencia Ergo Sum, Vol. 13(Nº 2), 149-156.

Taylor, S. (1986). Modelling Financial Time Series. New York: John Wiley.

The R Project for Statistical Computing. (20 de julio de 2016). Obtenido de http://cran.r-project.org/manuals.html

Tsay, R. (1986). Nonlinearity test for time series. Biometrika, Vol.76, 461-466.

Zakoian, J. (1990). Threshold heteroskedastic model. Paris: INSEE: Mimeo.

Downloads

Download data is not yet available.