Abstract
This paper investigates the asymmetric effects of exchange rate volatility in currency markets using high-frequency, intraday data of the most actively traded currencies over 2004–2017. The analysis is conducted by combining the quantile regression model with the heterogeneous autoregressive (HAR) model and its extensions where realized variance is decomposed into positive and negative semivariances. We find that safe haven currencies exhibit behavior different from that of other currencies. For safe haven currencies, negative realized semivariance associated with appreciation plays an important role in explaining the quantile-dependent volatility dynamics. This behavior is more pronounced during high volatility phases. The opposite holds for other currencies, i.e., positive realized semivariance associated with depreciation matters more. The results also reveal that while negative jumps associated with the appreciation of safe haven currencies lead to higher future volatility, positive jumps associated with the depreciation of other currencies lead to higher future volatility, especially during high volatility phases. We formally test whether the volatility dynamics are quantile dependent.
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Notes
The carry trade strategy exploits the empirical failure of uncovered interest rate parity (UIP), which implies that the rate of appreciation or depreciation of the spot exchange rate is negatively correlated with the lagged interest rate differential, or forward premium. The carry trade has been historically profitable, on average. The predictability of carry trades and the coincident failure of UIP have been analyzed by Fama (1984); Bansal (1997), Baillie and Cho (2014a). In addition, the considerable time variation in the UIP regression over the last two decades has been investigated by Baillie and Cho (2014b).
In a related study, Cho et al. (2019) find that the carry-to-risk ratio and jumps can also be strongly associated with the changes in volatility risk premium.
These, including the US dollar, are the most actively traded currencies in terms of their share of the daily turnover in currency markets. In addition, after obtaining the raw data on five-minute bid and ask rates, we define the mid-rate as the midpoint of the logarithmic bid and ask rates.
Baillie et al. (2019) address the importance of modeling long memory in realized volatility in addition to the HAR models.
See Patton and Sheppard (2015) for more details.
The estimation results for the two nested models, (i) and (iii), are available from the authors on request.
The BV series plotted for all currencies are available from the authors on request, but are omitted due to space constraints.
See Koenker and Xiao (2002) for more details.
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The authors gratefully acknowledge the constructive comments made by the Associate Editor. The authors are also grateful to seminar participants at Sungkyunkwan University and the 2017 International Panel Data Conference (IPDC) in Thessaloniki, Greece, for their helpful comments and suggestions. Any remaining errors are solely the authors’ responsibility.
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Cho, D., Rho, S. On asymmetric volatility effects in currency markets. Empir Econ 62, 2149–2177 (2022). https://doi.org/10.1007/s00181-021-02091-7
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DOI: https://doi.org/10.1007/s00181-021-02091-7