Gauging the Safehavenness of Currencies
This study assesses the 'safehavenness' of a number of currencies with a view to providing a better understanding of how capital flows tend to react to sharp increases in global risk aversion during periods of financial crisis. It focuses on how currencies are perceived by dollar-based international investors or, more specifically, whether they are seen as safe-haven or risky currencies. To assess the 'safehavenness' of a currency, we use a measure of risk reversal, which is the price difference between a call and put option of a currency. This measures how disproportionately market participants are willing to pay to hedge against appreciation or depreciation of the currency. The relationship between the risk reversal of a currency and global risk aversion is estimated by means of both parametric and non-parametric regressions which allow us to capture the relationship in times of extreme adversity, i.e., tail risk. Our empirical results suggest that the Japanese yen and, to a lesser extent, the Hong Kong dollar are the only safe haven currencies under stressful conditions out of 34 currencies vis-a-vis the US dollar.
|Date of creation:||Sep 2013|
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- Habib, Maurizio M. & Stracca, Livio, 2012.
"Getting beyond carry trade: What makes a safe haven currency?,"
Journal of International Economics,
Elsevier, vol. 87(1), pages 50-64.
- Habib, Maurizio Michael & Stracca, Livio, 2011. "Getting beyond carry trade: what makes a safe haven currency?," Working Paper Series 1288, European Central Bank.
- Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
- Tom Pak-wing Fong & Chun-shan Wong, 2008. "Stress Testing Banks' Credit Risk Using Mixture Vector Autoregressive Models," Working Papers 0813, Hong Kong Monetary Authority.
- Marion Kohler, 2010. "Exchange rates during financial crises," BIS Quarterly Review, Bank for International Settlements, March.
- Guarda, Paolo & Rouabah, Abdelaziz & Theal, John, 2012. "An MVAR framework to capture extreme events in macro-prudential stress tests," Working Paper Series 1464, European Central Bank.
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