In this paper, we employ 99% intraday value-at-risk (VaR) and intraday expected shortfall (ES) as risk metrics to assess the competency of the Multiplicative Component Generalised Autoregressive Heteroskedasticity (MC-GARCH) models based on the 1-min EUR/USD exchange rate returns. Five...

Risk models developed on one dataset are often applied to new data and, in such cases, it is prudent to check that the model is suitable for the new data. An important application is in the banking industry, where statistical models are applied to loans to determine provisions and capital...

Machine learning is increasingly applied to time series data, as it constitutes an attractive alternative to forecasts based on traditional time series models. For independent and identically distributed observations, cross-validation is the prevalent scheme for estimating out-of-sample...

In this paper, we employ 99% intraday value-at-risk (VaR) and intraday expected shortfall (ES) as risk metrics to assess the competency of the Multiplicative Component Generalised Autoregressive Heteroskedasticity (MC-GARCH) models based on the 1-min EUR/USD exchange rate returns. Five...

Risk models developed on one dataset are often applied to new data and, in such cases, it is prudent to check that the model is suitable for the new data. An important application is in the banking industry, where statistical models are applied to loans to determine provisions and capital...