Forecasting exchange rate volatility using high-frequency data: Is the euro different?

Forecasting the volatility of theAustralian dollar using high‐frequency data:Does estimator accuracy improve forecast evaluation?

“…We find that the regression‐based tests are not enhanced by the use of realized variance for forecasts derived from daily data, which is in contrast to early studies, such as Andersen and Bollerslev () but is in line with more recent studies, such as McMillan and Speight () and Chortareas et al (). This is the case both in regard to the regression R 2 and tests of forecast unbiasedness.…”

Volatility forecasting in the Chinese commodity futures market with intraday data

“…This finding also contributes to the discussion in the literature of whether the FIGARCH or the ARFIMA model is empirically better at capturing the long memory feature in the volatility dynamics (Chortareas et al 2011). Given that the intraday Chinese commodity futures data contain large proportion of zero returns which are directly fed in the FIGARCH model, it is not surprising that the ARFIMA model performs better.…”

“…6 The starting and ending dates of the four commodities are constrained by data availability. 7 Chortareas et al (2011) and Liu et al (2014) adopt similar sample period for the out-of-sample forecasting exercise with foreign exchange and commodity futures data, respectively.…”

Asymmetric effects and long memory in dynamic volatility relationships between stock returns and exchange rates

“…They include the root mean squared error (RMSE), the mean absolute error (MAE) and the logarithmic loss function (LL). Various studies have applied these forecasting techniques to compare volatility forecasts across different GARCH specifications (Andersen et al, 1999;Kang et al, 2009;Chortareas et al, 2011). For the univariate case, these criteria are, respectively defined as…”