Características estadísticas del índice general de la Bolsa de Valores de Colombia (IGBC) en sus primeros 10 años
Keywords:
IGBC, Heavy tail, Aggregational Gaussianity, Volatility clustering, Taylor effectAbstract
There are many studies published in the literature on stylized facts in financial time series. However, for the Colombian case there is only one work that documents the stylized facts of the returns. Alonso and Arcos (2006) documented the presence of four stylized facts in the exchange rate series and the principal Colombian Stock Exchange Index (IGBC), using a daily sample for the period from January 21, 1999 to April 31, 2005. The aim of this document is to present five stylized facts on the behavior of the IGBC returns in its first 10 years. Furthermore, a wider range of statistical test is used to support the existence of those stylized facts. Evidence is provided for the following stylized facts: I) no efficiency of the market; II) heavy tails of the distribution; III) aggregational Gaussianity; IV) volatility clustering and V) Taylor effect. In our case, the sample of the daily IGBC will be used for the period between July 3, 2001 and July 5, 2011.
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References
Alonso, J. C. y Arcos, M. A. (2006). Cuatro hechos estilizados de las series de rendimientos: Una ilustración para Colombia. Estudios Gerenciales. Universidad Icesi.
Alonso, J. C. y Berggrun, L. (2010). Introducción al análisis de riesgo financiero. Universidad Icesi.
Andersen, T. G., et al. (2001). The distribution of realized exchange rate volatility. Journal of the American Statistical Association, 96(453), 42–55.
Andersen, T. G. y Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review, 39(4), 885–905.
Andreou, E., Pittis, N. y Spanos, A. (2001). On modeling speculative prices: the empirical literature. Journal of economic surveys, 15, 187–220.
Andrews, D. W. K. y Ploberger, W. (1996). Testing for serial correlation against an ARMA (1,1) process, 91(435), 1331–1342.
Ball, R. y Kothari, S. P. (1989). Nonstationary expected returns: Implications for tests of market efficiency and serial correlation in returns. Journal of Financial Economics, 25(1), 51–74.
Belaire-Franch, G. y Contreras, D. (2004). Ranks and signs-based multiple variance ratio tests. Working paper. University of Valencia.
Bollerslev, T., Engle, R. y Nelson, D. (1993). ARCH models. University of California, San Diego. Department of Economics.
Breitung Jorg. (2002). Nonparametric tests for unit roots and cointegration. Journal of Econometrics, 108(2), 343–363.
Chen, W. W. y Deo, R. S. (2006). The variance ratio statistic at large horizons. Econometric Theory, 22, 206–234.
Choi, I. (1999). Testing the random walk hypothesis for real exchange rates. Econometrics, 14, 293–308.
Chow, K. V. y Denning, K. C. (1993). A simple multiple variance ratio test, 58(3), 385–401.
Cont, R. (2001). Empirical properties of asset returns: stylized facts and statistical issues. Quantitative Finance, 1(2), 223–236.
David, A. (1997). Fluctuating confidence in stock markets: Implications for returns and volatility. The Journal of Financial and Quantitative Analysis, 32(4), 427–462.
Durlauf, S. N. (1991). Spectral based testing of the martingale hypothesis. Journal of Econometrics, 50, 355–376.
Domínguez, M. y Lobato, I. (2003). Testing the martingale difference hypothesis. Econometric Reviews, 22(4), 351–377.
Engle, R. F., Ito, T. y Lin, W. L. (1990). Meteor showers or heat waves? Heteroskedastic intra-daily volatility in the foreign exchange market. Econometrica, 58(3), 525–542 (Mayo, 1990).
Escanciano, J. C. y Lobato, I. N. (2009). An automatic portmanteau test for serial correlation. Journal of Econometrics, 151, 140–149.
Escanciano, J. C. y Velasco, C. (2006). Generalized spectral tests for the martingale difference hypothesis, 134(1), 151–185.
Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417.
Granger, C.W. J. y Ding, Z.(1995). Some properties of absolute returns. An alternative measure of risk. Annales d
′Economie de Statisque, 40, 67–91.
Houthakker, H. S. (1961). Systematic and random elements in short-term price movements. The American Economic Review, 51(2), 164–172.
Jain, P.C. y Joh, G.(1988). Thedependencebetweenhourlyprices andtrading volume. The Journal of Financial and Quantitative Analysis, 23(3), 269–283.
Karemera, D., Ojah, K. y Cole, J. A. (1999). Random walks and market efficiency tests: Evidence from emerging equity markets. Review of Quantitative Finance and Accounting, 13(2), 171–188.
Kendall, M. G. (1953). The analysis of economic time-series—Part I: Prices. Journal of the Royal Statistical Society. Series A (General), 116(1), 11–25.
Kim, J. H. (2006). Wild bootstrapping variance ratio tests, 92(1), 38–43.
Kim, J. H. y Shamsuddin, A. (2008). Are Asian stock markets efficient? Evidence from new multiple variance ratio tests, 15(3), 518–532.
Kwiatkowski, D., Phillips, P., Schmidt, P. y Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics, 54(1-3), 159–178.
Liesenfeld y Jung. (2000). Conditional normality versus heavy-tailed distributions. Journal of Applied Econometrics, 15(2), 137–160.
Lo, A. W. y MacKinlay. (1988). Stock market prices do not follow random walks: Evidence from a simple specification test. The Review of Financial Studies, 41–66.
Lo, A. W. y MacKinlay, A. C. (1990). When are contrarian profits due to stock market overreaction? The Review of Financial Studies, 3(2), 175–205.
Lobato, I. N. y Velasco, C. (2000). Long memory in stock-market trading volume. Journal of Business & Economic Statistics, 18(4), 410–427.
Lux, T. y Marchesi, M. (2000). Volatility clustering in financial markets: A microsimulation of interacting agents. International Journal of Theoretical and Applied Finance, 3(4), 675–702.
Lux, T. y Sornette, D. (2002). On rational bubbles and fat tails, Journal of Money. Credit and Banking, 34(3), 589–610. Part 1.
Mantegna, R. N. y Stanley, H. (2000). Introduction to econophysics: Correlations and complexity in finance. Cambridge University Press.
Mandelbrot, B. (1963). The variation of certain speculative prices. The Journal of Business, 36(4), 394–419.
Mitchell, W. C. (1915). The making and using of index numbers. Bulletin of the US Bureau of Labor Statistics, 173.
Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica.
Osborne, M. F. M. (1962). Periodic structure in the Brownian motion of stock prices. Operations Research, 10(3), 345–379.
Phillips, Peter, C. B. y Pierre Perron. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346.
Richardson, M. y Smith, T. (1991). Tests of financial models in the presence of overlapping observations, 4(2), 227–254.
Sewell Martin. (2011). Characterization of financial time series. Research Note RN/11/01. University College London, London.
Taylor, S. (1986). Modelling financial time series. New York: Wiley
Taylor, S. (2005). Asset price dynamic, volatility and prediction. Princeton, NJ: Princeton University Press.
Urrutia, J. (1995). Tests of random walk and market efficiency for Latin American emerging equity markets, Journal of Financial Research. Southern Finance Association and Southwestern Finance Association, 18(3), 299–309.
Whang, Y. J. y Kim, J. (2003). A multiple variance ratio test using subsampling, 79(2), 225–230.
Worthington, A. C. y Higgs, H. (2003). Tests of random walks and market efficiency in Latin American stock markets: An empirical note. Discussion Paper No. 157, Queensland University of Technology.
Wright, J. H. (2000). Alternative variance-ratio tests using ranks and signs, 18(1), 1–9
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