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Distance-based nearest neighbour forecasting with application to exchange rate predictability
- Source :
- IMA Journal of Management Mathematics. 31:469-490
- Publication Year :
- 2020
- Publisher :
- Oxford University Press (OUP), 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.
- Subjects :
- 050208 finance
Applied Mathematics
Strategy and Management
05 social sciences
Nearest neighbour
Management Science and Operations Research
Management Information Systems
03 medical and health sciences
0302 clinical medicine
Exchange rate
Modeling and Simulation
0502 economics and business
Statistics
030221 ophthalmology & optometry
Predictability
General Economics, Econometrics and Finance
Distance based
Mathematics
Subjects
Details
- ISSN :
- 14716798 and 1471678X
- Volume :
- 31
- Database :
- OpenAIRE
- Journal :
- IMA Journal of Management Mathematics
- Accession number :
- edsair.doi...........089715e7875279d2f1b81b992ab68ed0