Forecasting exchange rates for Central and Eastern European currencies using country‐specific factors

Jaworski, Krystian

doi:10.1002/for.2749

Cited by 5 publications

(1 citation statement)

References 34 publications

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“…Most of the vast literature related to exchange rate forecasting is centred around the exchange rate models (Jaworski, 2021). Notwithstanding, the results obtained by Meese and Rogoff (1983) has limited the number of studies focused on univariate time series.…”

Section: Introductionmentioning

confidence: 99%

An application of deep learning for exchange rate forecasting

et al. 2022

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Section: Introductionmentioning

confidence: 99%

An application of deep learning for exchange rate forecasting

et al. 2022

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Modelling and Forecasting of Exchange Rate Pairs Using the Kalman Filter

Date,

Maunthrooa

2024

Journal of Forecasting

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Developing and employing practically useful and easy to calibrate models for prediction of exchange rates remains a challenging task, especially for highly volatile emerging market currencies. In this paper, we propose a novel approach for joint prediction of correlated exchange rates for two different currencies with respect to the same base currency. For this purpose, we reformulate a generalized version of a bivariate ARMA model into a state space model and use the Kalman filter for estimation and forecasting of the underlying exchange rates as latent variables. With extensive numerical experiments spanning 18 different exchange rates (across both emerging markets, developing and developed economies), we demonstrate that our approach consistently outperforms univariate ARMA models as well as the random walk model in short term out‐of‐sample prediction for various exchange rate pairs. Our study fills a gap in the empirical finance literature in terms of robust, explainable, accurate, and easy to calibrate models for forecasting correlated exchange rates. The proposed methodology has applications in exchange rate risk management as well as pricing of financial derivatives based on two exchange rates.

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The factor structure of exchange rates volatility: global and intermittent factors

Caporin,

Rodríguez-Caballero,

Ruiz

2024

Empir Econ

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In this paper, we consider a fractionally integrated multi-level dynamic factor model (FI-ML-DFM) to represent commonalities in the hourly evolution of realized volatilities of several international exchange rates. The FI-ML-DFM assumes common global factors active during the 24 h of the day, accompanied by intermittent factors, which are active at mutually exclusive times. We propose determining the number of global factors using a distance among the intermittent loadings. We show that although the bulk of common dynamics of exchange rates realized volatilities can be attributed to global factors, there are non-negligible effects of intermittent factors. The effect of the COVID-19 on the realized volatility comovements is stronger on the first global-in-time factor, which shows a permanent increase in the level. The effects on the second global factor and on the intermittent factors active when the EU, UK and US markets are operating are transitory lasting for approximately a year after the pandemic starts. Finally, there seems to be no effect of the pandemic neither on the third global factor nor on the intermittent factor active when the markets in Asia are operating.

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Forecasting exchange rates for Central and Eastern European currencies using country‐specific factors

Cited by 5 publications

References 34 publications

An application of deep learning for exchange rate forecasting

An application of deep learning for exchange rate forecasting

Modelling and Forecasting of Exchange Rate Pairs Using the Kalman Filter

The factor structure of exchange rates volatility: global and intermittent factors

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