System Identification in Noisy Data Environments: An Application to Six Asian Stock Markets
AbstractThis 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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Bibliographic InfoPaper provided by EconWPA in its series International Finance with number 0410005.
Length: 62 pages
Date of creation: 21 Oct 2004
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- Los, Cornelis A., 2006. "System identification in noisy data environments: An application to six Asian stock markets," Journal of Banking & Finance, Elsevier, vol. 30(7), pages 1997-2024, July.
- C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
- G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
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