---
title: Drivers of Exchange Rates in EMDEs: Implications for Foreign Exchange Intervention
url: https://www.ml-quant.com/papers/repec/imf-imfsdn-2026-003/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-10-09
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:imf:imfsdn:2026/003
source_url: https://econpapers.repec.org/RePEc:imf:imfsdn:2026/003
featured: 2026-10-09
citations: unknown
topic: Econometrics & Forecasting
---


# Drivers of Exchange Rates in EMDEs: Implications for Foreign Exchange Intervention

A framework using deviations from interest-rate parity identifies when exchange-rate movements stem from financial shocks rather than fundamentals, aiding intervention decisions.

- Source: https://econpapers.repec.org/RePEc:imf:imfsdn:2026/003
- Identifier: RePEc:imf:imfsdn:2026/003
- Released: 2026-10-05
- First featured: Quant Letter No. 134 (2026-10-09): https://www.ml-quant.com/issues/2026-10-09/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting
- Authors: Ece Ozge Emeksiz, Andres Fernandez, Nikhil Patel, Ivan Petrella, Tatjana Schulze

## Related

- [Forecasting the Risk-Free Rates: Practical Forecasting Model for the Future of 10-Year US Treasury Yield through Exogenous Variables](https://www.ml-quant.com/papers/ssrn/4780234/): The study introduces a forecasting model for predicting the 10-Year US Treasury Yield based on variables like exchange rates and crude oil prices.
- [TKAN: Temporal Kolmogorov-Arnold Networks](https://www.ml-quant.com/papers/ssrn/4825654/): The article presents Temporal Kolomogorov-Arnold Networks (TKANs), a new neural network design that merges the benefits of Recurrent Neural Networks and Long Short-Term Memory for improved multistep time series forecasting.
- [Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022](https://www.ml-quant.com/papers/arxiv/2305.04811/): The article explores deep learning models for financial forecasting, discussing their structures, uses, pros and cons, and potential future research areas.
- [Robust agents learn causal world models](https://www.ml-quant.com/papers/arxiv/2402.10877/): The research suggests that intelligent agents must learn an approximate causal model to generalize to new domains, impacting fields like transfer learning and causal inference.
- [Directional-shift Dirichlet ARMA models for compositional time series with structural break intervention](https://www.ml-quant.com/papers/arxiv/2601.16821/): The article introduces a new Bayesian model that analyzes compositional time series data, effectively handling structural breaks and enhancing forecasting accuracy during these changes.
- [FinCARE: Financial Causal Analysis with Reasoning and Evidence](https://www.ml-quant.com/papers/arxiv/2510.20221/): KG+LLM for Financial Causal Discovery: Combines SEC knowledge graphs, LLM reasoning, and causal discovery to build more accurate finance‑grounded causal models.