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Repositorio de la Red Internacional de Investigadores en Competitividad

COMPETITIVIDAD GLOBAL

Vol. 13 (2019): Los Retos de la Competitividad ante la Industria 4.0 978-607-96203-0-8

Administración activa de portafolios con modelos markovianos de cambio de régimen- GARCH en los principales países de la región andina

Enviado
marzo 23, 2020
Publicado
2020-03-30

Resumen

Resumen


En el presente trabajo se estudia el empleo de modelos markovianos con cambio de régimen (Markov-Switching, MS) de dos regímenes, con varianza constante, ARCH o GARCH y función de verosimiltud gaussiana o t-Student. Esto para administrar activamente portafolios en los mercados accionarios del índice MSCI andino (Chile, Colombia y Perú). Al realizar 996 simulaciones semanales de enero del 2000 a enero del 2019, se ejecutó la siguiente estrategia de inversión para un portafolio denominado en dólares de los EEUU: 1) invertir en el activo libre de riesgo si la probabilidad de estar en el régimen de alta volatilidad en t+1 es mayor a 50% o 2) invertir en el índice accionario en caso contrario. Los resultados sugieren que emplear modelos MS-ARCH gaussianos es lo mejor para la administración activa de los mercados chileno y colombiano, y que ninguno de estos es preferible para la administración pasiva en Perú.

Citas

  1. Ahmed, R. R., Vveinhardt, J., Štreimikiene, D., Ghauri, S. P., & Ashraf, M. (2018). Stock returns, volatility and mean reversion in Emerging and Developed financial markets. Technological and Economic Development of Economy, 24(3), 1149–1177. https://doi.org/10.3846/20294913.2017.1323317
  2. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. https://doi.org/10.1016/J.CUB.2017.09.001
  3. Alexander, C. (2002). Principal component models for generating large GARCH covariance matrices. Economic Notes, 31(2), 337–359. https://doi.org/10.1111/1468-0300.00089
  4. Alexander, C., & Kaeck, A. (2007). Regime dependent determinants of credit default swap spreads. Journal of Banking & Finance, (32), 1008–1021. https://doi.org/10.1011/j.jbankfin.2007.08.002
  5. Alvarez-Plata, P., & Schrooten, M. (2006). The Argentinean currency crisis: A Markov-switching model estimation. Developing Economies, 44(1), 79–91. https://doi.org/10.1111/j.1746-1049.2006.00004.x
  6. Ang, A., & Bekaert, G. (2002a). International Asset Allocation With Regime Shifts. The Review of Financial Studies, 15(4), 1137–1187.
  7. Ang, A., & Bekaert, G. (2002b). Regime Switches in Interest Rates. Journal of Business & Economic Statistics, 20(2), 163–182. https://doi.org/10.1198/073500102317351930
  8. Ang, A., & Bekaert, G. (2002c). Short rate nonlinearities and regime switches. Journal of Economic Dynamics and Control, 26(7–8), 1243–1274. https://doi.org/10.1016/S0165-1889(01)00042-2
  9. Ang, A., & Bekaert, G. (2004). How regimes affect asset allocation. Financial Analysts Journal, 60(2), 86–99. https://doi.org/10.2469/faj.v60.n2.2612
  10. Areal, N., Cortez, M. C., & Silva, F. (2013). The conditional performance of US mutual funds over different market regimes: do different types of ethical screens matter? Financial Markets and Portfolio Management, 27(4), 397–429. https://doi.org/10.1007/s11408-013-0218-5
  11. Bodie, Z., Kane, A., & Marcus, A. (2014). Investments global edition (10th ed.). New York, USA:Mc Graw-Hill.
  12. Bollerslev, T. (1987). A Conditionally Heteroskedastic time series model for speculative prices and rates of return. The Review of Economics and Statistics, 69(3), 542–547.
  13. Brooks, C., & Persand, G. (2001). The trading profitability of forecasts of the gilt–equity yield ratio. International Journal of Forecasting, 17(1), 11–29.
  14. Cabrera, G., Coronado, S., Rojas, O., & Venegas-Mart’inez, F. (2017). Synchronization and Changes in Volatilities in the Latin American’S Stock Exchange Markets. International Journal of Pure and Apllied Mathematics, 114(1). https://doi.org/10.12732/ijpam.v114i1.10
  15. Camacho, M., & Perez-Quiros, G. (2014). Commodity Prices and the Business Cycle in Latin America: Living and Dying by Commodities? Emerging Markets Finance and Trade, 50(2), 110–137. https://doi.org/10.2753/ree1540-496x500207
  16. Castellano, R., & Scaccia, L. (2014). Can CDS indexes signal future turmoils in the stock market? A Markov switching perspective. CEJOR, 22(2), 285–305. https://doi.org/10.1007/s10100-013-0330-7
  17. De la Torre, O., Galeana-figueroa, E., & Álvarez-García, J. (2018). Using Markov-Switching models in Italian , British , U . S . and Mexican equity portfolios : a performance test. Electronic Journal of Applied Statistical Analysis, 11(2), 489–505. https://doi.org/https://doi.rg/10.1285/i20705948v11n2p489
  18. Dubinskas, P., & Stungurienė, S. (2010). Alterations in the financial markets of the baltic countries and Russia in the period of Economic cownturn. Technological and Economic Development of Economy, 16(3), 502–515. https://doi.org/10.3846/tede.2010.31
  19. Dueker, M. (1997). Markov Switching in GARCH Processes and Mean- Reverting Stock-Market Volatility. Journal of Business & Economics Statistics, 15(1), 26–34.
  20. Dufrénot, G., Mignon, V., & Péguin-Feissolle, A. (2011). The effects of the subprime crisis on the Latin American financial markets: An empirical assessment. Economic Modelling, 28(5), 2342–2357. https://doi.org/10.1016/J.ECONMOD.2011.04.012
  21. Engle, R. (1982). Autoregressive Conditional Heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.
  22. Girdzijauskas, S., Štreimikienė, D., Čepinskis, J., Moskaliova, V., Jurkonytė, E., & Mackevičius, R.(2009). Formation of Economic bubles: cuases and possible interventions. Technological and Economic Development of Economy, 15(2), 267–280. https://doi.org/10.3846/1392- 8619.2009.15.267-280
  23. Glosten, L., Jaganathan, R., & Runkle, D. E. (1993). On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks. The Journal of Finance, 48(5),1779–1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x
  24. Haas, M., Mittnik, S., & Paolella, M. S. (2004). A New Approach to Markov-Switching GARCH Models. Journal of Financial Econometrics, 2(4), 493–530.
  25. Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357–384.
  26. Hamilton, J. D. (1990). Analysis of time series subject to changes in regime. Journal of Econometrics, 45(1–2), 39–70. https://doi.org/10.1016/0304-4076(90)90093-9
  27. Hamilton, J. D. (1994). Time Series Analysis. Princeton: Princeton university press.
  28. Hamilton, J. D., & Susmel, R. (1994). Autoregressive conditional heteroskedasticity and changes in regime. Journal of Econometrics, 64(1–2), 307–333. https://doi.org/10.1016/0304-4076(94)90067-1
  29. Hauptmann, J., Hoppenkamps, A., Min, A., Ramsauer, F., & Zagst, R. (2014). Forecasting market turbulence using regime switching models. Financial Markets and Portfolio Management,28(2), 139–164. https://doi.org/10.1007/s11408-014-0226-0
  30. Kanas, A. (2005). Regime linkages between the Mexican currency market and emerging equity markets. Economic Modelling, 22(1), 109–125. https://doi.org/10.1016/j.econmod.2004.05.003
  31. Kim, C.-J. (1994). Dynamic linear models with Markov switching. Journal of Econometrics, 60(1–2), 1–22. https://doi.org/10.1016/0304-4076(94)90036-1
  32. Klaassen, F. (2002). Improving GARCH volatility forecasts with regime-switching GARCH. In Advances in Markov-Switching Models (pp. 223–254). https://doi.org/10.1007/978-3-642- 51182-0_10
  33. Klein, A. C. (2013). Time-variations in herding behavior: Evidence from a Markov switching SUR model. Journal of International Financial Markets, Institutions & Money, 26, 291–304. https://doi.org/10.1016/j.intfin.2013.06.006
  34. Kritzman, M., Page, S., & Turkington, D. (2012). Regime Shifts: Implications for Dynamic Strategies. Financial Analysts Journal, 68(3), 22–39.
  35. Kutty, G. (2010). the Relationship Between Exchange Rates and Stock Prices : the Case of Mexico. North American Journal of Finance and Banking Research, 4(4), 1–12.
  36. Lamoureux, C. G., & Lastrapes, W. D. (1990). Persistence in Variance, Structural Change, and the GARCH Model. Journal of Business & Economic Statistics, 8(2), 225–234. https://doi.org/10.1080/07350015.1990.10509794
  37. Lopes, J. M., & Nunes, L. C. (2012). A Markov regime switching model of crises and contagion: The case of the Iberian countries in the EMS. Journal of Macroeconomics, 34, 1141–1153. https://doi.org/10.1016/j.jmacro.2012.08.007
  38. Ma, J., Deng, X., Ho, K.-C., & Tsai, S.-B. (2018). Regime Switching Determinants for Spreads of Emerging Markets Sovereign Credit Default Swaps. Sustainability, 10(2730), 1–17.https://doi.org/10.3390/su10082730
  39. Maggin, J. L., Tuttle, D., Pinto, J., & McLeavey, D. W. (2007). Managing Investment Portfolios: A Dynamic Process (John Miley and Sons Inc, Ed.). Hoboken, USA.
  40. Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.x
  41. Markowitz, H. (1959). Portfolio selection. Efficient diversification of investments. New Haven: Yale University Press.
  42. Miles, W., & Vijverberg, C.-P. (2011). Formal targets, central bank independence and inflation dynamics in the UK: A Markov-Switching approach. Journal of Macroeconomics, 33, 644–655. https://doi.org/10.1016/j.jmacro.2011.04.003
  43. Mouratidis, K., Kenourgios, D., Samitas, A., & Vougas, D. (2013). Evaluating currency crises: A multivariate markov regime switching approach*. Manchester School, 81(1), 33–57. https://doi.org/10.1111/j.1467-9957.2012.02259.x
  44. MSCI Inc. (2012). MSCI Andean Index (USD). Market cap indexes. Retrieved at April 24, 2019, from https://www.msci.com/documents/10199/a71ce44f-2f53-44eb-b40b-fcba0e5b5547
  45. MSCI Inc. (2018a). MSCI emerging markets index. Indexes. Retrieved at May 12, 2018, from https://www.msci.com/documents/1296102/1362201/MSCI-Emerging-Markets Brochure-April-2018.pdf/9e532b6f-5281-4e36 bdae-045328a2f8ac
  46. MSCI Inc. (2018b). MSCI emerging markets latin america index (USD). Market cap indexes. Retrieved at April 11, 2018, from https://www.msci.com/documents/10199/5b537e9c-ab98-49e4-88b5-bf0aed926b9b
  47. MSCI Inc. (2018c). MSCI Global Investable Market Indexes Methodology. Indexes. Retrieved at May 2, 2018, from http://www.msci.com/eqb/methodology/meth_docs/MSCI_Jan2015_GIMIMethodology_vf.pd
  48. MSCI Inc. (2019). End of day index data search - MSCI. Indexes. Retrieved at April 2, 2019, from https://www.msci.com/end-of-day-data-search
  49. Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347. https://doi.org/10.2307/2938260
  50. Parikakis, G. S., & Merika, A. (2009). Evaluating volatility dynamics and the forecasting ability of Markov switching models. Journal of Forecasting, 28(8), 736–744. https://doi.org/10.1002/for.1135
  51. Rotta, P. N., & Valls Pereira, P. L. (2016). Analysis of contagion from the dynamic conditional correlation model with Markov Regime switching. Applied Economics, 48(25), 2367–2382. https://doi.org/10.1080/00036846.2015.1119794
  52. Sharpe, W. (1963). A simplified model for portfolio analysis. Management Science, 9(2), 277–293.
  53. Sharpe, W. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, XIX(3), 425–442.
  54. Sosa, M., Ortiz, E., & Cabello, A. (2018). Dynamic Linkages between Stock Market and Exchange Rate in mila Countries: A Markov Regime Switching Approach (2003-2016). Análisis Económico, 33(83), 57–74. https://doi.org/10.24275/uam/azc/dcsh/ae/2018v33n83/sosa
  55. Thomson Reuters. (2018). Thomson Reuters Eikon. Thomson Refinitiv Eikon login. Retrieved at December 10, 2018, from https://eikon.thomsonreuters.com/index.html
  56. Viterbi, A. (1967). Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory, 13(2), 260–269. https://doi.org/10.1109/TIT.1967.1054010
  57. Walid, C., Chaker, A., Masood, O., & Fry, J. (2011). Stock market volatility and exchange rates in emerging countries: A Markov-state switching approach. Emerging Markets Review, 12, 272– 292. https://doi.org/10.1016/j.ememar.2011.04.003
  58. Walid, C., & Duc Khuong, D. (2014). Exchange rate movements and stock market returns in a regime-switching environment: Evidence for BRICS countries. Research in International Business and Finance, (31), 46–56. https://doi.org/10.1016/j.ribaf.2013.11.007
  59. Zhao, H. (2010). Dynamic relationship between exchange rate and stock price: Evidence from China. Research in International Business and Finance, 24(2), 103–112. https://doi.org/10.1016/j.ribaf.2009.09.001
  60. Zheng, T., & Zuo, H. (2013). Reexamining the time-varying volatility spillover effects: A Markov switching causality approach. North American Journal of Economics and Finance, 26, 643–662. https://doi.org/10.1016/j.najef.2013.05.001.

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