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This paper studies the performance of GARCH model and its modi®cations, using the rate of returns from the daily stock market indices of the Kuala Lumpur Stock Exchange (KLSE) including Composite Index, Tins Index, Plantations Index, Properties Index, and Finance Index. The models are stationary GARCH, unconstrained GARCH, non-negative GARCH, GARCH-M, exponential GARCH and integrated GARCH. The parameters of these models and variance processes are estimated jointly using the maximum likelihood method. The performance of the within-sample estimation is diagnosed using several goodness-of-®t statistics. We observed that, among the models, even though exponential GARCH is not the best model in the goodness-of-®t statistics, it performs best in describing the often-observed skewness in stock market indices and in out-of-sample (onestep-ahead) forecasting. The integrated GARCH, on the other hand, is the poorest model in both respects.

Wet-sieving method using nested sieves is one common method to measure aggregate stability. However, this method cannot be used to measure the stability of individual aggregate size fractions, only of whole soils. Thus, this study was to develop an equation to estimate the aggregate breakdown of individual aggregate size fractions in this particular method. The key to develop the equation was to assume that aggregate breakdown happens sequentially and consistently, and that the aggregate breakdown between any two aggregates in the same aggregate size fractions is equal in percentage. Applying these two assumptions, this equation was developed: xi=(Wai×Di)/(Wai+Di−1), where xi is the weight of aggregate breakdown in aggregate size fraction i, Wai is the weight of the aggregates in aggregate size fraction i before wet-sieving, and Di and Di−1 are the weight of aggregates that have passed through sieve i and i−1, respectively. This equation was tested with five soil series. The soils were separated into six aggregate size fractions

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