This paper presents empirical evidence on the effectiveness of eight different parametric ARCH models in describing daily stock returns. Twenty-seven years of UK daily data on a broad-based value weighted stock index are investigated for the period 1971-97. Several interesting results are documented. Overall, the results strongly demonstrate the utility of parametric ARCH models in describing time-varying volatility in this market. The parameters proxying for asymmetry in models that recognize the asymmetric behaviour of volatility are highly significant in each and every case. However, the performance of the various parameterizations is often fairly similar with the exception of the multiplicative GARCH model that performs qualitatively differently on several dimensions of performance. The outperformance of any model(s) is not consistent across different sub-periods of the sample, suggesting that the optimal choice of a model is period-specific. The outperformance is also not consistent as we change from in-sample inferences to out-of-sample inferences within the same period.