Commodity Price Forecasting Based on Heteroskedasticity Threshold Autoregressive Models for Interval Data
Commodity is an important part of industrial production and financial investment,and accurate commodity price forecasting is of great significance to safe-guard industrial production and help investors avoid risks.However,most of the existing commodity price forecasting models are point-value models based on closing prices,which ignores the volatility information.Therefore we propose a heteroskedas-ticity threshold autoregressive interval model with exogenous variables(HTARIX)and apply it to the commodity markets.We also construct a test statistic based on interval-valued data to test whether there is conditional heteroskedasticity in the model,and propose a generalized minimum DKdistance estimation.The advantage of our model is that it can capture the conditional heteroskedasticity and nonlinear fea-tures of interval-valued time series models.Compared with the point-valued models,our method contains more information of the data.The empirical results imply that HTARIX model performs better than other comparative models in interval-valued commodity price forecasting.
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