This article aims to study the effectiveness of the multi-step multiplicative component generalized autoregressive conditional heteroscedasticity (MCS-GARCH) model in forecasting intraday value at risk (VaR) and expected shortfall (ES) for both emerging and developed markets. The intraday VaR- and ES-forecasting performance of the MCS-GARCH model is compared with that of three other models. Five-minute returns data of stock market indices from four different countries are used in this study: India, China, the USA and the UK. The VaR and ES quantiles are estimated with the help of conditional extreme value theory (EVT) framework as well as with simple normal distribution assumption. The backtesting of VaR and ES suggest that the conditional EVT models provide superior forecasts compared to their GARCH counterparts. However, the accuracy of these models differs between emerging and developed markets. The multi-step MCS-GARCH or its EVT counterpart fails to outperform other competing models consistently, especially in emerging markets. The inability of the MCS-GARCH model in forecasting intraday risk accurately suggests that the multiple steps in the estimation of this model may lead to the errors-in-variables problem. The results of this study are unique in their suggestion that multi-step models may not be a better option in the estimation of VaR and ES.
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