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The role of the enhanced carry to risk on currency policy: the Mexican Peso

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  • Carlos Fernández-Herraiz
  • Antonio Javier Prado-Domínguez
  • Carlos Pateiro-Rodríguez
  • Jesús M. García-Iglesias

Abstract

One of the most common measures of carry trade attractiveness is the carry-to-risk ratio. On analysing the speculative activity, this ratio presents two issues: First, emerging market currencies could merit a legitimate risk premium in a carry trade strategy due to the sovereign risks involved. In order to correct the measure, we include the credit risk in the measure using credit default swaps. Second, we gather more information about potential volatility asymmetries including a directional speculation indicator known as the risk reversal. We prove that the enhanced measure for the Mexican peso (MXN) is well represented by an ARIMA model with appropriate features since 2009. Due to the output of the analysis, we deduce that the Bank of Mexico might use this measure or a similar indicator, not only to understand the attractiveness of the carry trade strategy but to curb destabilizing carry trade activities. The case is compelling for the Mexican peso due to its dual role as emerging market currency and full convertible currency. We conclude that this institution may effectively manage the enhanced carry-to-risk measures in order to achieve financial stability and proper exchange credibility, and recognize its potential utility for other central banks.

Suggested Citation

  • Carlos Fernández-Herraiz & Antonio Javier Prado-Domínguez & Carlos Pateiro-Rodríguez & Jesús M. García-Iglesias, 2019. "The role of the enhanced carry to risk on currency policy: the Mexican Peso," Applied Economics, Taylor & Francis Journals, vol. 51(17), pages 1808-1816, April.
  • Handle: RePEc:taf:applec:v:51:y:2019:i:17:p:1808-1816
    DOI: 10.1080/00036846.2018.1529395
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    Cited by:

    1. Qian Zhang & Kuo-Jui Wu & Ming-Lang Tseng, 2019. "Exploring Carry Trade and Exchange Rate toward Sustainable Financial Resources: An application of the Artificial Intelligence UKF Method," Sustainability, MDPI, vol. 11(12), pages 1-26, June.

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