taking into account the weight and the magnitude of the price change variance of the subindices. Sampling Techniques 4.7 In survey sampling theory, 1 there is a distinction between the parameter and the estimator. In the context of a CPI, the parameter is the target price index number that is based on prices and quantities of the products that belong to the universe. The estimator is the price index that is actually compiled using the sampled data as input. The result of the estimator depends on the price index formula that may or may not use weights, and on
), the SDR, and gold with ARCH(1,1) as well as for the U.S. dollar with ARCH(6,2). Only the BJ test for the average composite index with ARCH(1,1) is not statistically significant. I. Brief Literature Review The uncertainty of speculative prices has been observed to change through time ( Mandelbrot (1963) and Fama (1965) ). The tendency of large (small) price changes in high-frequency financial data to be followed by other large (small) price changes is often called “volatility clustering.” One specification that has emerged for characterizing such changing
) price changes in the high frequency financial data to be followed by other large (small) price changes is often called “volatility clustering.” One of the specifications that has emerged for characterizing such changing variances is the ARCH model ( Engle (1982) ) and its various extensions. In his seminal paper, Engle suggests that one possible parametrization for variances is to express them as a linear function of past-squared values of the errors of the model. With financial data, the ARCH model captures the tendency for volatility clustering, and numerous
exchange rate risk and may thus expose investors to greater risk of loss than they are willing to accept. If estimates of changing variances can be made with reasonable accuracy, appropriate portfolio shifts can be made over time. Such a strategy can help central banks manage their foreign exchange reserves. The tests employ the autoregressive conditional heteroscedasticity (ARCH) methodology. The existence of heteroscedasticity makes it difficult to base inferences and predictions on least-squares estimation. When the conditional variances of returns are not constant
estimates of changing variances can be made with reasonable accuracy, appropriate portfolio shifts can be made over time. Such a strategy can help central banks manage their foreign exchange reserves. The tests employ the autoregressive conditional heteroscedasticity (ARCH) methodology. The existence of heteroscedasticity makes it difficult to base inferences and predictions on least-squares estimation. When the conditional variances of returns are not constant through time, they can be calculated with the estimation procedure used in ARCH models. Estimation is possible