Exchange rate predictive densities and currency risks: A quantile regression approach
Niango Ange Joseph Yapi ()
Additional contact information
Niango Ange Joseph Yapi: EconomiX - EconomiX - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique
Working Papers from HAL
Abstract:
We investigate the ability of the Fama equation to compute proper conditional densities and currency risks. Based on quantile regressions, we fit a Skewed t-distribution to estimate the conditional densities on the monetary policy of eight currency pairs. We demonstrate that the conditional densities are highly sensitive to the monetary policy stances. Then, we use the estimated conditional densities to measure the currency risks. Our results highlight that the depreciation/appreciation risks are extremely heterogeneous and that the currencies are more exposed to depreciation risks, especially during turmoils. Our findings can be used as a supplementary tool to assess whether a currency behaves as a safe-haven currency. We also investigate the relative and absolute performance of our model in forecasting densities. We find that the predictive densities are perfectly well-calibrated. Moreover, our results also demonstrate that our methodology can outperform the random walk in forecasting densities.
Keywords: Quantile regressions; Predictive densities; Currency risks; Safe-haven currency. (search for similar items in EconPapers)
Date: 2020
Note: View the original document on HAL open archive server: https://hal.science/hal-04159699
References: Add references at CitEc
Citations:
Downloads: (external link)
https://hal.science/hal-04159699/document (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:hal:wpaper:hal-04159699
Access Statistics for this paper
More papers in Working Papers from HAL
Bibliographic data for series maintained by CCSD ().