Estimación de niveles óptimos de cobertura para portafolios de inversión estáticos, dinámicos y con varianza condicional. Evidencia en países emergentes

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DOI:

https://doi.org/10.29105/vtga5.1-776

Palabras clave:

nivel óptimo de cobertura, volatilidad, GARCH, países emergentes

Resumen

La creación de portafolios es una herramienta versátil que le permite al inversionista evaluar la distribución de diversos activos financieros y al mismo tiempo disminuir la volatilidad implícita en los mercados. En esta investigación se compara la eficiencia en el nivel óptimo de cobertura utilizando el modelo de mínimos cuadrados, mínimos cuadrados con ventanas móviles y el modelo GARCH, para un portafolio compuesto por el precio spot del Índice MSCI Mercados Emergentes y los precios futuros del oro, durante 2010 a 2018. Los resultados de este estudio demuestran que de los tres modelos analizados, el método de mínimos cuadrados con ventanas móviles de 6 meses fue el que generó la mayor eficiencia en la cobertura y la menor volatilidad, inclusive por encima del modelo GARCH que resultó ligeramente menos eficiente.

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Citas

Baillie, R. T., & Myers, R. J. (1991). Bivariate GARCH estimation of the optimal commodity futures hedge. Journal of Applied Econometrics, 6(2), 109-124. DOI: https://doi.org/10.1002/jae.3950060202

Basher, S. A., & Sadorsky, P. (2015). Hedging emerging market stock prices with oil, gold, VIX, and bonds: A comparison between DCC, ADCC and GO-GARCH. Energy Economics, 54, 235247. DOI: https://doi.org/10.1016/j.eneco.2015.11.022

Bhattacharya, S., Singh, H., & Alas, R. M. (2011). Optimal Hedge Ratio with Moving Least SquaresAn Empirical Study Using Indian Single Stock Futures Data. International Research Journal of Finance and Economics, 79, 98-111.

Byström, H. N. (2003). The hedging performance of electricity futures on the Nordic power exchange. Applied Economics, 35(1), 1-11. DOI: https://doi.org/10.1080/00036840210138365

Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327. DOI: https://doi.org/10.1016/0304-4076(86)90063-1

Brooks, C., Henry, O. T., & Persand, G. (2002). The effect of asymmetries on optimal hedge ratios. The Journal of Business, 75(2), 333-352. DOI: https://doi.org/10.1086/338484

Chang, C. L., McAleer, M., & Tansuchat, R. (2013). Conditional correlations and volatility spillovers between crude oil and stock index returns. The North American Journal of Economics and Finance, 25, 116-138. DOI: https://doi.org/10.1016/j.najef.2012.06.002

Creti, A., Joëts, M., & Mignon, V. (2013). On the links between stock and commodity markets' volatility. Energy Economics, 37, 16-28. DOI: https://doi.org/10.1016/j.eneco.2013.01.005

Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427-431. DOI: https://doi.org/10.1080/01621459.1979.10482531

Dinică, M. C., & Balea, E. C. (2014). Natural Gas Price Volatility and Optimal Hedge Ratios. Economic Computation & Economic Cybernetics Studies & Research, 48(3).

Ederington, L. H. (1979). The hedging performance of the new futures markets. The Journal of Finance, 34(1), 157-170. DOI: https://doi.org/10.1111/j.1540-6261.1979.tb02077.x

Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007. DOI: https://doi.org/10.2307/1912773

Engle, R. F., & Granger, C. W. (1987). Co-integration and error correction: representation, estimation, and testing. Econometrica: Journal of the Econometric Society, 251-276. DOI: https://doi.org/10.2307/1913236

Erb, C. B., & Harvey, C. R. (2006). The strategic and tactical value of commodity futures. Financ Anal J.62,69-97. DOI: https://doi.org/10.2469/faj.v62.n2.4084

Franco, C., & Zakoian, J. (2010). GARCH models, structure, statistical inference and financial application. Wiley London. DOI: https://doi.org/10.1002/9780470670057

García, A., Zabeh, B., Hosein, M., & Rositas, J. (2006). Optimal Hedge Ratio estimation: GARCH (1, 1) approach, a new model). Innovaciones de Negocios, 3(6), 227-242. DOI: https://doi.org/10.29105/rinn3.6-5

Gorton, G., & Rouwenhorst, K. G. (2004). Facts and fantasies about commodity futures (No. w10595). National Bureau of Economic Research. DOI: https://doi.org/10.3386/w10595

Hillier, D., Draper, P., & Faff, R. (2006). Do precious metals shine? An investment perspective. Financial Analysts Journal, 98-106. DOI: https://doi.org/10.2469/faj.v62.n2.4085

Ibbotson Associates, (2006) Strategic Asset Allocation and Commodities, Chicago

Kroner, K. F., & Sultan, J. (1993). Time-varying distributions and dynamic hedging with foreign currency futures. Journal of financial and quantitative analysis, 28(4), 535-551. DOI: https://doi.org/10.2307/2331164

Ku, Y. H. H., Chen, H. C., & Chen, K. H. (2007). On the application of the dynamic conditional correlation model in estimating optimal time-varying hedge ratios. Applied Economics Letters, 14(7), 503-509. DOI: https://doi.org/10.1080/13504850500447331

Lintner, J. (1965). Security prices, risk, and maximal gains from diversification. The journal of finance, 20(4), 587-615. DOI: https://doi.org/10.1111/j.1540-6261.1965.tb02930.x

Markowitz, H. (1959). Portfolio selection. Investment under Uncertainty. 59

Mili, M., & Abid, F. (2004). Optimal hedge ratios estimate: Static vs Dynamic hedging. Finance India, 18, 655.

Mossin, J. (1966). Equilibrium in a capital asset market. Econometrica: Journal of the econometric society, 768-783. DOI: https://doi.org/10.2307/1910098

Myers, R. J., & Thompson, S. R. (1989). Generalized optimal hedge ratio estimation. American Journal of Agricultural Economics, 71(4), 858-868. DOI: https://doi.org/10.2307/1242663

Michaud, R. O., Michaud, R., & Pulvermacher, K. (2006). Gold as a strategic asset. World Gold Council, London 10.

Pástor, L. (2000). Portfolio selection and asset pricing models. The Journal of Finance, 55(1), 179223. DOI: https://doi.org/10.1111/0022-1082.00204

Rondinone, G., & Thomasz, E. O. (2018). Financiarización de commodities: la incidencia de la tasa de interés en el precio del frijol de soya durante el periodo 1990-2014. Revista Análisis Económico, 31(77), 53-83.

Sadorsky, P. (2014). Modeling volatility and correlations between emerging market stock prices and the prices of copper, oil and wheat. Energy Economics, 43, 72-81. DOI: https://doi.org/10.1016/j.eneco.2014.02.014

Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The journal of finance, 19(3), 425-442. DOI: https://doi.org/10.1111/j.1540-6261.1964.tb02865.x

Wang, K. M., Lee, Y. M., & Thi, T. B. N. (2011). Time and place where gold acts as an inflation hedge: An application of long-run and short-run threshold model. Economic Modelling, 28(3), 806-819. DOI: https://doi.org/10.1016/j.econmod.2010.10.008

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Publicado

28-06-2019

Cómo citar

Galindo-Manrique, A., Lambretón-Torres, V., & Rodríguez-García, M. (2019). Estimación de niveles óptimos de cobertura para portafolios de inversión estáticos, dinámicos y con varianza condicional. Evidencia en países emergentes. Vinculatégica EFAN, 5(1), 43–59. https://doi.org/10.29105/vtga5.1-776