Analysis of Exchange Rate Volatility in Peru in the Presence of Structural Breaks

Victor Chung, Jessie Bravo

Abstract

The presence of structural breaks in the analysis of volatility in the financial field has been ignored in research done in Latin America. This paper fills this gap by analyzing the behavior of the dollar exchange rate in Peru by evaluating the impact of structural breaks in volatility forecasting. The return behavior was analyzed for the period 05/01/2010 to 09/30/2021. Econometric analysis was used, which consisted of: (1) use of the modified Iterative Cumulative Sum of Squares (ICSS) algorithm to determine the structural breakpoints; (2) estimation of GARCH Models for the subsamples originated by the identified breakpoints; and (3) comparison of alternative models with the GARCH(1,1) expanding window model for horizons of 1, 20, 60 and 120 days. The ICSS algorithm identified 8 breaks in volatility behavior. The models were compared based on out-of-sample forecast performance. It was determined that the GARCH model that considers structural breaks is only effective for a one-day horizon. Finally, the GARCH(1,1) 0.25 rolling window model provides a better strategy for forecasting the volatility of exchange rate returns in Peru for longer horizons.

 


Keywords: volatility, GARCH, structural breaks, exchange rate.

 

https://doi.org/10.55463/issn.1674-2974.49.4.28

 


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References


MUMTAZ A. Impact of exchange rate and its volatility on domestic consumption in India and Pakistan. Journal of Public Affairs, 2020: e2479. https://doi.org/10.1002/pa.2479

ASTERIOU D., MASATCI K., and PIBEAM K. Exchange rate volatility and international trade: International evidence from the MINT countries. Economic Modelling, 2016, 59(133). https://doi.org/10.1016/j.econmod.2016.05.006

NOR M., MASRON T., and ALABDULLAH T. Macroeconomic fundamentals and the exchange rate volatility: Empirical evidence from Somalia. SAGE Open, 2020, 10(1). https://doi.org/10.1177/2158244019898841

SUGIHARTI L., ESQUIVIAS M. A., and SETYORANI B. The impact of exchange rate volatility on Indonesia's top exports to the five main export markets. Heliyon, 2020, 6(1). https://doi.org/10.1016/j.heliyon.2019.e03141

EPAPHRA M. Modeling Exchange Rate Volatility: Application of the GARCH and EGARCH Models. Journal of Mathematical Finance, 2017, 7: 121-143. https://doi.org/10.4236/jmf.2017.71007

TRENCA I., ZAPODEANU D., and COCIUBA M. Testing the Presence of Structural Break in the Euro Exchange Rate Variance. Procedia Economics and Finance, 2015, 32: 1163-1169. https://doi.org/10.1016/S2212-5671(15)01582-8

ŽIVKOV D., NJEGIĆ J., and MOMČILOVIĆ M. Bidirectional spillover effect between Russian stock index and the selected commodities. Zbornik Radova Ekonomskog Fakultet au Rijeci, 2018, 36: 27-51. https://doi.org/10.18045/zbefri.2018.1.29

GÜLOGLU B., KAYA P., and AYDEMIR R. Volatility Transmission among Latin American Stock Markets under Structural Breaks. Physica A, 2016, 462: 330-340. https://doi.org/10.1016/j.physa.2016.06.093

ROJAS E., GUZMAN J., and BALTAZAR J. Volatilidad cambiaria, metas de inflación y crisis financiera global. Evidencia para economías latinoamericanas. Revista Economía y Política, 2019, 15(30). https://doi.org/10.25097/rep.n30.2019.07

SOTOMAYOR R. N., & CASTILLO J. E. Modelamiento de la volatilidad del Índice General de la Bolsa de Valores de Lima, periodo 2009-2011. Anales Científicos, 2016, 77(1): 1-7. https://doi.org/10.21704/ac.v77i1.576

CHUNG V. Modelación de la Volatilidad del Tipo de Cambio del Dólar en el Perú: Aplicación de los Modelos GARCH y EGARCH. Análisis Económico y Financiero, 2021, 4(2): 7-12. https://doi.org/10.24265/raef.2021.v4n2.40

JINLAN M., & BEICHEN W. Structural Breaks in Volatility: The Case of Chinese Stock Returns. The Chinese Economy, 2016, 49(2): 81–93. http://dx.doi.org/10.1080/10971475.2016.1143302

INCLÁN C., & TIAO C. Use of cumulative squares for retrospective detection of changes in variance. Journal of the America Statistic Association, 1994, 89: 913–923. https://doi.org/10.1080/01621459.1994.10476824

BLASQUES F., GORGI P., and SIEM J. Feasible invertibility conditions and maximum likelihood estimation for observation-driven models. Electronic Journal of Statistic, 2018, 12: 1019–1052. https://doi.org/10.1214/18-EJS1416

STARICA C., & GRANGER C. Nonstationarities in

Stock Returns. The Review of Economics and Statistics, 2005, 87(3): 503–522. https://www.jstor.org/stable/40042945

BROOKS C. Introductory Econometrics for Finance. Cambridge University Press, Cambridge, 2019.

BABIKIR A., & GUPTA R. Structural breaks and GARCH models of stock return volatility: The case of South Africa. Economic Modelling, 2012, 29(6): 2435-2443. https://doi.org/10.1016/j.econmod.2012.06.038


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