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原文連結
論文資訊
- 類型：已發表論文
- 日期：2020-01-07
摘要
We develop an iterative and efficient 資訊-theoretic estimator for forecasting interval-valued data, and use our estimator to forecast the SP500 returns up to five days ahead using moving windows. Our forecasts are based on 13 years of data. We show that our estimator is superior to its competitors under all of the common criteria that are used to evaluate forecasts of interval data. Our approach differs from other methods that are used to forecast interval data in two major ways. First, rather than applying the more traditional methods that use only certain moments of the intervals in the estimation process, our estimator uses the complete sample 資訊. Second, our method simultaneously selects the model (or models) and infers the model's parameters. It is an iterative approach that imposes mi
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