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Distance-based nearest neighbour forecasting with application to exchange rate predictability.
- Source :
- IMA Journal of Management Mathematics; Oct2020, Vol. 31 Issue 4, p469-490, 22p
- Publication Year :
- 2020
-
Abstract
- Forecasting non-stationary time series, especially when the data generating processes contains a random walk component, is a difficult and sometimes impossible task. In this paper we suggest an intuitive, computationally fast and expedient way of forecasting time series of the above type using distance-based nearest neighbours (NN). We exploit to advantage the path and scale dependence present in a random walk model and so we provide a number of theoretical results (a) on the distances used for selecting the NN, (b) on a number of new forecasting models that use these distances and (c) on the properties of the resulting forecasts. We illustrate the efficacy of our method via a comprehensive empirical application on time series of exchange rates and commodities, where we present the resulting performance enhancements and discuss the importance of such results in a decision-making context, linking our forecasting approach with management mathematics and predictive analytics problems. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1471678X
- Volume :
- 31
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- IMA Journal of Management Mathematics
- Publication Type :
- Academic Journal
- Accession number :
- 146035387
- Full Text :
- https://doi.org/10.1093/imaman/dpz016