Forecasting Exchange Rate Value at Risk using Deep Belief Network Ensemble based Approach

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

9 Scopus Citations
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Original languageEnglish
Pages (from-to)25-32
Journal / PublicationProcedia Computer Science
Online published18 Oct 2018
Publication statusPublished - 2018


Title6th International Conference on Information Technology and Quantitative Management, ITQM 2018
PlaceUnited States
Period20 - 21 October 2018



In this paper, we propose a new Value at Risk estimate based on the Deep Belief Network ensemble model with Empirical Mode Decomposition (EMD) technique. It attempts to capture the multi-scale data features with the EMD-DBN ensemble model and predict the risk movement more accurately. Individual data components are extracted using EMD model while individual forecasts can be calculated at different scales using ARMA-GARCH model. The DBN model is introduced to search for the optimal nonlinear ensemble weights to combine the individual forecasts at different scales into the ensembled exchange rate VaR forecasts. Empirical studies using major exchange rates confirm that the proposed model demonstrates the superior performance compared to the benchmark models.

Research Area(s)

  • C45, C53, Deep Belief Network, Empirical Mode Decomposition, Exchange rate forecasting, Value at Risk JEL: F31

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