Carry Trade Returns Research Articles

On the predictability of the distribution of excess returns in currency markets

This paper investigates the predictability of foreign exchange (FX) volatility and liquidity risk factors on returns to the carry trade, an investment strategy that borrows in currencies with low interest rates and invests in currencies with high interest rates. Previous studies have suggested that this predictability could have been spuriously accounted for due to the persistence of the predictors. The analysis uses a predictive quantile regression model developed by Lee (2016) that allows for persistent predictors. We find that predictability changes remarkably across the entire distribution of currency excess returns. Predictability weakens substantially in the left tail once persistence is accounted for, implying a moderate negative predictive relation between FX volatility risk and carry trade returns. By contrast, it becomes stronger in the right tail. Furthermore, we provide evidence that FX volatility risk still dominates liquidity risk after controlling for persistence. These findings suggest that the persistence of the predictors needs to be taken into account when one measures predictability in currency markets. Finally, out-of-sample forecast performance is also presented.

Forward Premium Puzzle and Heterogeneous Beliefs

We propose a two-country model with heterogeneous beliefs to understand the forward premium puzzle. Facing a shock to the domestic money supply, the disagreement between domestic and foreign investors shifts the relative wealth of investors, which moves the exchange rate and interest rate differential in opposite directions. Calibrated to U.S. and U.K. data, our model reproduces the rejection of the uncovered interest rate parity. Using a monthly index of heterogeneous beliefs based on the Consensus Forecast in 15 major economies, the empirical evidence confirms that the dispersion of beliefs helps explain exchange rate movements and carry trade returns.

Time-Varying Risk Aversion and the Profitability of Carry Trades: Evidence from the Cross-Quantilogram

This paper examines the predictive power of time-varying risk aversion over payoffs to the carry trade strategy via the cross-quantilogram methodology. Our analysis yields significant evidence of directional predictability from risk aversion to daily carry trade returns tracked by the Deutsche Bank G10 Currency Future Harvest Total Return Index. The predictive power of risk aversion is found to be stronger during periods of moderate to high risk aversion and largely concentrated on extreme fluctuations in carry trade returns. While large crashes in carry trade returns are associated with significant rises in investors’ risk aversion, we also found that booms in carry trade returns can be predicted at high quantiles of risk aversion. The results highlight the predictive role of extreme investor sentiment in currency markets and regime specific patterns in carry trade returns that can be captured via quantile-based predictive models.

Exploring Currency Risk Factors via Corridor Implied Volatility and Its Term Structures

This paper extends the application of corridor implied volatility by Andersen et al. (2015) to currency market, empirically measuring currency risks at multiple horizons, and finds its term structure contains useful information, both for profitable trading strategies and for common risk factors constructions on the basis of Lustig et al. (2011). I consistently find that for currencies paired by US dollars, the term structure of currency risk is flat at a low level prior to the 2008 crisis, upward-sloping after the crisis and peaks at a high level with a prominently negative slope during the crisis. This work is believed to be new in the currency research field. I then use this information to build trading strategies, earning a profit by longing currencies with the highest level or slope and shorting ones with the lowest level or slope. The profit by sorting slope is significantly high and robust to the 2008 crisis period, with a low correlation to the Carry Trade return, suggesting extra information in risk than the interest rate. Next, I extract global risk factors by level and slope to help understand the currency excess return, a long-lasting puzzle. The global risk factor by level substantially improves the cross-sectional explanatory power in currency excess returns compared to Lustig et al. (2011). Furthermore, I show that there is certain high risk corresponding to a high level and low slope, and high interest rate currency earns returns co-varying negatively to this risk, implying that it is a risky asset and thus requires a high risk premium, which explains the Carry Trade return well.

Is bitcoin a channel of capital inflow? Evidence from carry trade activity

We empirically identify carry trade activities under the disguise of bitcoin transactions on USD and CNY bitcoin exchanges during the post global financial crisis period. Relying on variations of bitcoin exchange pairs and two policy shocks, we find the evidence of inflow capital control evasion. We show that the relative CNY to USD bitcoin price, indicating capital inflow volume, of those with high excessive currency conversion profit respond significantly more negatively to carry trade returns. Moreover, this relationship becomes significantly weaker when China tightens its regulations on bitcoin transactions.

Pricing Risks Across Currency Denominations

We use principal component analysis on 55 bilateral exchange rates of 11 developed currencies to identify two important global risk sources in foreign exchange (FX) markets. The risk sources are related to Carry and Dollar but are not spanned by these factors. We estimate the market prices associated with the two risk sources in the cross-section of FX market returns and construct FX market-implied country-specific stochastic discount factors (SDFs). The SDF volatilities are related to interest rates and expected carry trade returns in the cross-section. The SDFs price international stock returns and are related to important financial stress indicators and macroeconomic fundamentals. The first principal risk is associated with the Treasury-EuroDollar (TED) spread, quantities measuring volatility, tail and contagion risks, and future economic growth. It earns a relatively small implied Sharpe ratio. The second principal risk is associated with the default and term spreads and quantities capturing volatility and illiquidity risks. It further correlates with future changes in the long-term interest rate and earns a large implied Sharpe ratio. This paper was accepted by Lauren Cohen, finance.

Currency Regimes, Volatility Risks, and Carry Trades: The Option Value of Government Currency Intervention in Emerging Markets

This study investigates the relationship between cross-sectional carry trade returns and global foreign exchange volatility risk. During periods of high volatility innovations, the average carry trade returns on emerging markets are higher than that of all countries or developed economies. Furthermore, the average returns on managed-float and fixed-rate carry trades are significantly higher than that of free-float carry trade. Government currency intervention in emerging markets can explain these differences. There is an option value in government currency intervention, which can be calculated using an American currency option model with stochastic strikes. This result has policy implications.

Good Carry, Bad Carry

We distinguish between “good” and “bad” carry trades constructed from Group of Ten (G-10) currencies. The good trades exhibit higher Sharpe ratios and sometimes positive return skewness, in contrast to the bad trades, which have both substantially lower Sharpe ratios and highly negative return skewness. Surprisingly, good trades do not involve the most typical carry currencies like the Australian dollar and Japanese yen. The distinction between good and bad carry trades significantly alters our understanding of currency carry trade returns, and invalidates, for example, explanations invoking return skewness and crash risk.

Long Run and Short Run Risk Premium in Currency Market

In this paper, I investigate risk premium of long run and short run volatility component of exchange rate returns in currency market. I find that high interest rate currencies of carry trade strategy load negatively on long run volatility innovation, while low interest rate currencies load positively. Risk price of long run volatility innovation is negative which implies that high carry trade returns are considered as compensation for time varying long run volatility risk. In contrast, risk price of short run volatility innovation is positive. Low interest currencies deliver low returns and high interest rate currencies yield high returns under times of high short run volatility. In terms of momentum strategy, risk price of short run volatility innovation is negative and statistically significant, while risk price of long run component is insignificant. Therefore, long run volatility does not provide any explanation for high returns of currency momentum strategy. High momentum returns, on the other hand are reward for investors to bear short run volatility risk.

When Do Commodity Prices Matter for the Carry Trade? The Role of FX Liquidity

Carry traders invest in high-yield currencies, which are typically the currencies of commodity exporters. Guided by this stylized fact, we study the impact of commodity prices on carry trade performance. Commodity price shocks contemporaneously explain and predict carry trade returns but only when currency market liquidity is low. Our findings are consistent with the premise that a decline in commodity prices is perceived as negative news for commodity exporters and induces carry traders to unwind their positions, thereby exacerbating the downward price pressure during liquidity dry-outs. This sheds new light on the interconnection between the commodity and currency markets.

Solvency risk premia and the carry trades

This paper shows that currency carry trades can be rationalized by the risk premia originating from the sovereign solvency risk. We find that solvency risk is a key determinant of risk premia in the cross section of carry trade returns, as its covariance with currency returns captures a substantial part of the cross-sectional variation of carry trade returns. Importantly, low interest rate currencies serve as insurance against solvency risk, while high interest rate currencies expose investors to more risk. The results are not attenuated by existing risks and pass a broad range of various robustness checks.

Crash-o-phobia in Currency Carry Trade Returns

Currency carry trade returns are on average large and non-normally distributed. While the literature has found different explanations for the existence of carry trade returns, the higher order moments of their return distribution still pose a puzzle. We propose a new model to explain these non-normal properties of currency carry trade returns, by assuming that agents are loss averse and overweight states with low probabilities. This combination of loss aversion and probability weighting is called crash-o-phobia. Using non-linear least squares and risk-neutral state prices implied by currency options, we estimate this crash-o-phobia model to price developed and emerging market currencies. The parameter estimates reveal crash-o-phobic beliefs and preferences with significant differences across currencies. Compared to a model with rational beliefs and CRRA utility, our crash-o-phobia model performs significantly better at explaining the whole distribution of currency carry trade returns.

The Impact of Jumps on Carry Trade Returns

This paper investigates how jump risks are priced in currency markets. We find that currencies whose changes are more sensitive to negative market jumps provide significantly higher expected returns. The positive risk premium constitutes compensation for the extreme losses during periods of market turmoil. Using the empirical findings, we propose a jump modified carry trade strategy, which has approximately 2-percentage-point (per annum) higher returns than the regular carry trade strategy. These findings result from the fact that negative jump betas are significantly related to the riskiness of currencies and business conditions.

The impact of jumps on carry trade returns

This paper investigates how jump risks are priced in currency markets. We find that currencies whose changes are more sensitive to negative market jumps provide significantly higher expected returns. The positive risk premium constitutes compensation for the extreme losses during periods of market turmoil. Using the empirical findings, we propose a jump modified carry trade strategy, which has approximately two-percentage-point (per annum) higher returns than the regular carry trade strategy. These findings result from the fact that negative jump betas are significantly related to the riskiness of currencies and business conditions.

Where’s the Risk? The Forward Premium Bias, the Carry-Trade Premium, and Risk-Reversals in General Equilibrium

This paper builds a two-country dynamic stochastic general equilibrium macro model to understand three empirical facts about international currency returns. They are the downward forward premium bias, the carry trade return, and the long-run risk reversal. Cross-country heterogeneity in unit-root productivity levels generates the systematic risk priced into currency returns. The risk can be magnified through monetary policy. Both a complete markets and an incomplete markets model are qualitatively consistent with these facts. Quantitatively, the incomplete markets model performs better.

Carry Trade Returns with Support Vector Machines

AbstractThis paper proposes a novel approach to directional forecasts for carry trade strategies based on support vector machines (SVMs), a learning algorithm that delivers extremely promising results. Building on recent findings in the literature on carry trade, we condition the SVM on indicators of uncertainty and risk. We show that this provides a dramatic performance improvement in strategy, particularly during periods of financial distress such as the recent financial crises. Disentangling the measures of risk, we show that conditioning the SVM on measures of liquidity risk rather than on market volatility yields the best performance.

Carry Trade Returns and Segmented Risk Pricing

The returns to carry trades are controversially discussed as there seems to be no unifying risk-based explanation of currency returns and stock returns. This paper addresses carry trade returns from a risk pricing perspective and examines if these returns can be connected to persistent cross-country differences of risk aversion. Therefore, I analyze a data set of individual carry trade currencies. Based on a GMM estimation, I find significantly large and persistent cross-country differences of risk aversion in the interest rate market compared to the implied risk aversion in the stock market. In this context, investment currencies are more sensitive to U.S. consumption risk while funding currencies provide a hedge. However, this also implies that there is no unifying SDF and consequently, both the interest rate market and the stock market appear to be segmented in risk pricing for carry trade countries.

The Effect of Risk Changes on Carry Trade Returns and Speculative Behavior

This paper analyzes the time-series reaction of carry trade returns to changes in various risk factors. Using non-linear methods, I find that implied currency volatility is an informative time-series predictor. Increases (declines) in the implied currency volatility (or, generally, perceptions of future risk) result in lower (higher) subsequent carry trade returns. The reaction to extreme changes in these risk perceptions is even more pronounced. Using futures positioning data, it is shown that speculators tend to sell carry trade positions upon a perception of increased risk and vice versa. A piecewise linear threshold model is proposed to predict short-term carry trade returns; it outperforms a variety of benchmarks (including the random walk) on almost all metrics. Robustness tests suggest that this performance does not depend on certain model settings or the sample period (i.e. data mining); instead, a rollingly updated model would lead to even better results.

From Carry Trades to Curvy Trades

Traditional carry trade strategies are based on differences in short-term interest rates, neglecting any other information embedded in yield curves. We derive return distributions of carry trade portfolios among G10 currencies, where the signals to buy and sell currencies are based on summary measures of the yield curve, the Nelson-Siegel factors. We find that a strategy based on the relative curvature factor, the curvy trade, yields higher Sharpe ratios and a smaller return skewness than traditional carry trade strategies. Curvy trades build less upon the typical carry currencies, like the Japanese yen and the Swiss franc, and are hence less susceptible to crash risk. In line with that, standard pricing factors of traditional carry trade returns, such as exchange rate volatility, fail to explain curvy trade returns in a linear asset pricing framework. Our findings are in line with recent interpretations of the curvature factor. A relatively high curvature signals a relatively higher path of future short-term rates over the medium-term putting upward pressure on the currency. JEL Classification: C23, C53, G11

Solvency Risk Premia and the Carry Trades

The paper shows that currency carry trades can be rationalized by the time-varying risk premia originating from the sovereign solvency risk. We find that solvency risk is a key determinant of risk premia in the cross section of carry trade returns, as its covariance with returns captures a substantial part of the cross-sectional vraiation of carry trade returns. Importantly, low interest rate currencies serve as insurance against solvency risk, while high interest rate currencies expose investors to more risk. The results are not attenuated by existing risks and pass a broad range of various robustness checks.