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System Identification in Noisy Data Environments: An Application to Six Asian Stock Markets

  • Cornelis A Los

This paper analyzes the systematic relationship between the stock market valuations, the nominal GDPs and the interest rates of six Asian countries, using not 'single equation regression,' but an alternative methodology based on complete, multidirectional, least squares projections. We compare the results with the spectral analysis of the information matrices and determine the noise levels. The objective is to extract the multidimensional economic system structures from the noisy empirical observations. This complete methodology sharply contrasts with the incomplete methodology of Fama (1990), Schwert (1990), etc., who presume planal relations, fit them to the multidimensional data by only one prejudiced unidirectional projection, thereby ignoring between 75% - 92% of the available covariance information and not publishing the absolute majority of all possible model projections. The results in this paper show that the analyzed countries are better analyzed using such complete multidirectional LS projections, even though the analysis is combinatorially much more complex. All six Asian financial-economic systems are high data noise environments, in which it is very difficult to separate the systematic signals from the noise. Because of these high noise levels, spectral analysis is very unreliable. We identify Taiwan's stock market, economy and financial market to be rationally coherent. In contrast, Malaysia, Singapore, Philippines and Indonesia show only partially coherent systems, while no coherent system can be identified among Japan's data.

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Paper provided by EconWPA in its series International Finance with number 0410005.

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Length: 62 pages
Date of creation: 21 Oct 2004
Date of revision:
Handle: RePEc:wpa:wuwpif:0410005
Note: Type of Document - pdf; pages: 62
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  1. Gallinger, George W, 1994. "Causality Tests of the Real Stock Return-Real Activity Hypothesis," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 17(2), pages 271-88, Summer.
  2. Fama, Eugene F, 1981. "Stock Returns, Real Activity, Inflation, and Money," American Economic Review, American Economic Association, vol. 71(4), pages 545-65, September.
  3. Los, Cornelis A., 1999. "Galton's Error and the under-representation of systematic risk," Journal of Banking & Finance, Elsevier, vol. 23(12), pages 1793-1829, December.
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  10. Schwert, G. William, 1987. "Effects of model specification on tests for unit roots in macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 20(1), pages 73-103, July.
  11. George W. Gallinger, 1994. "Causality Tests Of The Real Stock Return-Real Activity Hypothesis," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 17(2), pages 271-288, 06.
  12. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-38, July.
  13. Kholdy, Shady & Sohrabian, Ahmad, 1995. "Testing for the relationship between nominal exchange rates and economic fundamentals," Global Finance Journal, Elsevier, vol. 6(2), pages 121-134.
  14. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
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