Abstract
This paper analyzes the cyclical behavior of Dow Jones by testing the existence of long memory through a new class of semiparametric ARFIMA models with HYGARCH errors (SEMIFARMA-HYGARCH); this class includes nonparametric deterministic trend, stochastic trend, short-range and long-range dependence and long memory heteroscedastic errors. We study the daily returns of the Dow Jones from 1896 to 2006. We estimate several models and we find that the coefficients of the SEMIFARMA-HYGARCH model, including long memory coefficients for the equations of the mean and the conditional variance, are highly significant. The forecasting results show that the informational shocks have permanent effects on volatility and the SEMIFARMA-HYGARCH model has better performance over some other models for long and/or short horizons. The predictions from this model are also better than the predictions of the random walk model; accordingly, the weak efficiency assumption of financial markets seems violated for Dow Jones returns studied over a long period.
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References
Akaike H. (1970) Statistical predictor identification. Annals of Institute of Statistical Mathematics 22: 203–217
Aloui C., Mabrouk S. (2010) Value-at-risk estimations of energy commodities via long-memory, asymmetry and fat-tailed GARCH models. Energy Policy 38: 2326–2339
Andrews D. W. K., Guggenberger P. (2003) A bias-reduced log-periodogram regression estimator for the long-memory parameter. Econometrica 71: 675–712
Baillie R. T., Bollerslev T, Mikkelsen H. O. (1996) Fractionally integrated generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 74: 3–30
Barkoulas J. T., Baum C. F. (1997) Long memory and forecasting in Euroyen deposit rates. Financial Engineering and the Japanese Market 4: 189–201
Barkoulas J. T., Baum C. F. (2006) Long-memory forecasting of US monetary indices. Journal of Forecasting 25(4): 291–302
Beran, J. (1999). SEMIFAR models—A semiparametric framework for modelling trends, long-range dependence and nonstationarity. Discussion Paper No. 99/16. Center of Finance and Econometrics, University of Konstanz.
Beran J., Feng Y. (2002a) SEMIFAR models—A semiparametric framework for modelling trends, long-range dependence and nonstationarity. Computational Statististics & Data Analysis 40: 393–419
Beran J., Feng Y. (2002b) Iterative plug-in algorithms for SEMIFAR models -definition, convergence and asymptotic properties. Journal of Computational and Graphical Statistics 11: 690–713
Beran J., Ocker D. (1999a) SEMIFAR forecasts, with applications to foreign exchange rates. Journal of Statistical Planning and Inference 80: 137–153
Beran, J., & Ocker, D. (1999b). Volatility of stock market indices—An analysis based on SEMIFAR models. Working Paper, Department of Economics and Statistics, University of Konstanz, Germany.
Bollerslev T. (1986) Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics 31: 307–327
Brock, W. A., Dechert, W. D., & Scheinkman, J. (1987). A test for independence based on the correlation dimension. Discussion Paper 8702, University of Wisconsin-Madison.
Cardamone, E., & Folkinshteyn, D. (2007). HYGARCH Approach to Estimating Interest Rate and Exchange Rate Sensitivity of a Large Sample of U.S. Banking Institutions. Working Paper, Temple University, Department of Finance, USA.
Christodoulou-Volos C. C., Siokis F. M. (2006) Long range dependence in stock market returns. Applied Financial Economics 16: 1331–1338
Conrad C. (2010) Non-negativity conditions for the hyperbolic GARCH model. Journal of Econometrics 157: 441–457
Davidson J. (2004) Moments and memory properties of linear conditional heteroskedasticity models and a new model. Journal of Business and Economic Statistics 22: 16–29
Dickey D., Fuller W. (1979) Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association 74: 427–431
Diebold F. X., Mariano R. S. (1995) Comparing predictive accuracy. Journal of Business and Economic Statistics 13(3): 253–263
Elliott G., Rothenberg T. J., Stock J. H. (1996) Efficient tests for an autoregressive unit root. Econometrica 64(4): 813–836
Engle R. F. (1982) Autoregressive conditional heteroskedasticity with estimation of U.K. inflation. Econometrica 50: 987–1008
Fama E. F. (1965) Random walks in stock market prices. Financial Analysts Journal 21(5): 55–59
Fama E. F. (1970) Efficient capital markets: A review of theory and empirical work. The Journal of Finance 25(2): 383–417
Fama E. F. (1998) Market efficiency, long-term returns, and behavioral finance. Journal of Financial Economics 49: 283–306
Feng Y., Beran Y., Yu K. (2007) Modelling financial time series with SEMIFAR–GARCH model. IMA Journal of Management Mathematics 18: 395–412
Geweke J., Porter-Hudak S. (1983) The estimation and application of long-memory time series models. Journal of Time Series Analysis 4: 221–238
Granger C. W. J., Joyeux R. (1980) An introduction to long-memory time series models and fractional differencing. Journal of Time Series Analysis 1: 15–30
Grossman S. J. (1976) On the efficiency of competitive stock markets where trades have diverse information. The Journal of Finance 31(2): 573–585
Gursakal S. (2010) Detecting long memory in bulls and bears markets: Evidence from Turkey. Journal of Money, Investment and Banking 18: 95–104
Hall P., Hart J. (1990) Nonparametric regression with long-range dependence. Stochastic Processes and Their Applications 36: 339–351
Härdle, W., & Mungo, J. (2008). Value at risk and expected short fall when there is long range dependence. Discussion Paper 2008-006, Humboldt-universität zu Berlin, Germany.
Hosking J. R. M. (1981) Fractional differencing. Biometrika 68: 165–176
Jensen M. C. (1978) Some anomalous evidence regarding market efficiency. Journal of Financial Economics 6(2–3): 95–101
Kasman A., Kasman S., Torun E. (2009) Dual long memory property in returns and volatility: Evidence from the CEE countries’ stock markets. Emerging Markets Review 10: 122–139
Kwan, W., Li, W. K., & Li, G. (2011). On the estimation and diagnostic checking of the ARFIMA-HYGARCH Model. Computational Statistics and Data Analysis, forthcoming.
Kwiatkowski D., Phillips P., Schmidt P., Shin Y. (1992) Testing the null hypothesis of stationary against the alternative of a unit root: How sure are we that economic time series have a unit root?. Journal of Econometrics 54: 159–178
Lillo F., Farmer J. D. (2004) The long memory of the efficient market. Studies in Nonlinear Dynamics & Econometrics 8: 3
McMillan D. G., Kambouroudis D. (2009) Are risk metrics forecasts good enough? Evidence from 31 stock markets. International Review of Financial Analysis 18: 117–124
Mizrach, B. (1995). A simple nonparametric test for independence. Working Paper 1995-23, Rutgers University, USA.
Nadaraya E. A. (1964) On estimating regression. Theory of Probability and Their Applications 9: 134–137
Phillips P. C. B., Perron P. (1988) Testing for unit roots in time series regression. Biometrika 75: 335–346
Ray B. K., Tsay R. S. (1997) Bandwidth selection for kernel regression with long-range dependence. Biometrika 84: 791–802
Schmidt P., Phillips P. C. B. (1992) LM test for a unit root in the presence of deterministic trends. Oxford Bulletin of Economics and Statistics 54: 257–287
Schwarz G. (1978) Estimating the dimension of a model. Annals of Statistics 6: 461–464
Tang T. L., Shieh S. J. (2006) Long memory in stock index future markets: A value-at-risk approach. Physica A 366: 437–448
Watson G. S. (1964) Smooth regression analysis. Sankhyä A26: 359–372
Wei Y., Wang Y., Huang D. (2010) Forecasting crude oil market volatility: Further evidence using GARCH-class models. Energy Economics 32: 1477–1484
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Chikhi, M., Péguin-Feissolle, A. & Terraza, M. SEMIFARMA-HYGARCH Modeling of Dow Jones Return Persistence. Comput Econ 41, 249–265 (2013). https://doi.org/10.1007/s10614-012-9328-9
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DOI: https://doi.org/10.1007/s10614-012-9328-9