Abstract
This study uses the interval computing approach to forecast the annual and quarterly variability of the stock market. We find that the forecasting accuracy is significantly higher than the OLS lower and upper bound forecasting. The strength of the interval computing comes from its data processing. It uses lower and upper bound information simultaneously, no variability information is lost in parameter estimation. The quarterly interval (variability) forecasts suggest that the interval computing method outperforms the OLS lower and upper bound forecasting in both stable and volatile periods.
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References
Armstrong, J. S. (2001). Suggestions for Further Research. http://www.forecastingprinciples.com/researchers.html
Chatfield C. (1993) Calculating interval forecasts. Journal of Business and Economic Statistics 11: 121–144
Chatfield C. (2001) Prediction intervals for time-series forecasting. In: Armstrong J.S. (eds) Principles of forecasting: Handbook for researchers and practitioners. Kluwer Academic Publisher, Boston
Chen N.-F., Roll R., Ross S. (1986) Economic forces and the stock market. Journal of Business 59: 383–403
De Gooijer J.G., Hyndman R.J. (2006) 25 years of time series forecasting. International Journal of Forecasting 22: 443–473
Fama E. (1981) Stock returns, real activity, inflation, and money. The American Economic Review 71: 545–565
Fama E., French K. (1993) Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 33: 3–56
Fama E., French K. (1997) Industry costs of equity. Journal of Financial Economics 43: 153–193
Gardner E.S. (1988) A simple method of computing prediction intervals for time series forecasts, Management Science 34: 541–546
Gau C.-Y., Stadtherr M. A. (2000) Reliable nonlinear parameter estimation using interval analysis: Error-in-Variable approach. Computers and Chemical Engineering 24: 631–638
Granger C.W.J. (1996) Can we improve the perceived quality of economic forecasts? Journal of Applied Econometrics 11: 455–473
He L.T., Hu C. (2007) Impacts of interval measurement on studies of Economic variability: Evidence from stock market variability forecasting. Journal of Risk Finance 8: 489–507
Hu C., He L.T. (2007) An application of interval methods for stock market forecasting. Journal of Reliable Computing 13: 423–434
Hu C., Xu S., Yang X. (2002) A review on interval computation Software and applications. International Journal of Computational and Numerical Analysis and Applications 1(2): 149–162
Hua Z., Brennecke J.F., Stadtherr M.A. (1996) Reliable prediction of phase stability using an Interval-Newton method. Fluid Phase Equilibria 116: 52–59
Kearfott R.B. (1996) Interval extensions of non-smooth functions for global optimization and nonlinear systems solvers. Computing 57: 149–162
Kearfott R.B., Hongthong S. (2005) Validated linear relaxations and preprocessing: Some experiments. SIAM Journal on Optimization 16: 418–433
Korvin, A., Hu, C., Chen, P. (2002). Association analysis with interval valued fuzzy sets and body of evidence. Proceedings of the 2002 IEEE International Conferences on Fuzzy Systems, 518–523.
Moore R.E. (1966) Interval analysis. Prentice-Hall, Englewood Cliffs, N.J.
Moore R.E. (1979) Methods and applications of interval analysis. SIAM Studies in Applied Mathematics, Philadelphia, SIAM
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He, L.T., Hu, C. Impacts of Interval Computing on Stock Market Variability Forecasting. Comput Econ 33, 263–276 (2009). https://doi.org/10.1007/s10614-008-9159-x
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DOI: https://doi.org/10.1007/s10614-008-9159-x