When Carry Goes Bad: The When, How, and Why of Currency Carry Unwinds

Transcription

1 When Carry Goes Bad: The When, How, and Why of Currency Carry Unwinds Michael Melvin and Duncan Shand

2 When Carry Goes Bad: the when, how, and why of currency carry unwinds Michael Melvin* and Duncan Shand** The literature on currency carry trades has focused on the interesting question of why excess returns to the strategy exist. While it is known that carry trades possess significant risk of large negative returns, or crashes, this left-tail risk is relatively unexplored in the literature. This paper explores the returns to carry trade drawdowns for portfolios of developed market (DM) currencies, emerging market (EM) currencies, and a portfolio combining both. After documenting the positive excess returns that exist over the long-run, the major episodes of carry crashes are analyzed. It is seen that (DM) currency portfolios have realized a different experience than (EM) portfolios in terms of the magnitude, timing, and duration of drawdowns. Both DM and EM currency carry portfolios have underperformed in the post-crisis period. After the analysis of top-10 drawdowns, carry returns are modeled as a function of financial market stress (FSI). It is seen that the constituent parts of the Global FSI are better at capturing events than the Global index. Finally, equity and currency volatility and equity returns are key factors in identifying carry-relevant stress in financial markets. PRELIMINARY DRAFT ON WORK-IN-PROGRESS *contact author: BlackRock, San Francisco; michael.melvin@blackrock.com **University of Warwick; Duncan.Shand@wbs.ac.uk We thank Justin Peterson for excellent research assistance. 1

3 Currency carry trades have attracted much attention from academic researchers in recent years. A carry trade is a speculative strategy of buying currencies of countries with high interest rates funded with the sale of currencies of countries with low interest rates. The focus of the literature has been on why such trades should produce positive returns. In international finance classes, students learn that interest rate parity has exchange rates changing to offset the interest differentials on different currencies so that there should be a zero expected profit from carry trades. While market inefficiency or investor irrationality is a possible explanation for positive carry trade returns, recent research on the topic has pointed to carry trade returns representing a risk premium. 1 We want to focus on the downside of currency carry trades. While it is well known that such trades have a significant negative skew, the experience of carry drawdowns is less well-known. We seek to fill this hole by cataloging the major carry losses in recent times and identifying the likely factors contributing to each. The analysis proceeds as follows: Section I describes the data and portfolio construction approach and then reports cumulative returns for carry portfolios of developed market (DM) currencies, emerging market (EM) currencies, and a portfolio of combined DM and EM currencies. Section II lists and analyzes the top-10 carry crashes for DM and EM currencies. Both magnitude of loss and duration in terms of number of days of carry drawdown are reported. Then a decomposition of carry drawdowns by currency is analyzed to reveal which currencies have been the major contributors to carry crashes. It is known that carry trades perform poorly in times of market stress, so in Section III, returns to the carry trade portfolios are modeled as a function of financial stress. A Financial Stress Index (FSI) is created to measure the time-varying degree of financial conditions. The FSI is then employed in 1 Studies note the negative skew associated with carry returns. A recent small sample of such papers includes Brunnermeier, Nagel, and Pedersen (2008); Jurek (2009); and Lustig, Roussanov, and Verdelhan (2011). 1

4 regression analysis of carry returns to confirm the importance of macro market conditions. Further analysis reveals which particular subsets of the FSI are most important in understanding carry unwinds. Section IV offers a summary and conclusions. 2

5 I. Data and Portfolio Construction A. Data The basic data are spot and forward exchange rates and are taken as the WMR rates sampled at 4pm in London. We focus on the following set of currencies that are widely traded by active currency investors. The developed market (DM) portfolio consists of 2 : Australia (AUD), Canada (CAD), euro (EUR), Japan (JPY), New Zealand (NZD), Norway (NOK), Sweden (SEK), Switzerland (CHF)and the United Kingdom (GBP). The emerging market (EM) universe consists of Brazil (BRL), Chile (CLP), Colombia (COP), Czech Republic (CZK), Hungary (HUF), India (INR), Indonesia (IDR), Malaysia (MYR), Mexico (MXN), Peru (PEN), Philippines (PHP), Poland (PLN), Romania (RON), Russia (RUB), Singapore (SGD), South Africa (ZAR), South Korea (KRW), Taiwan 9TWD), Thailand (THB) and Turkey (TRY). The analysis below will consider carry returns of the full sample of currencies (All) as well as the performance of portfolios of just DM or just EM. We begin by defining s and f as the logs of the spot and forward exchange rates. We measure all exchange rates in units of foreign currency per U.S. dollar. Daily returns from forward speculation are constructed from a strategy of entering into a one-month forward contract at each month end, where the contract is closed out via a spot market transaction at the next month end. This is a carry trade strategy with monthly rebalancing. We calculate the daily mark-to-market on the positions each day in order to infer daily returns for currency i equal to. Covered interest parity ensures that the forward discount/premium is equal to the interest differential, or approximated by, where denotes the US interest rate. Therefore, the log excess returns are constructed as the deviation between the initial interest differential and the realized change in the spot rate, or: 2 The three letter codes used here are as defined in ISO

6 returns rather than log excess returns.. Where portfolio drawdown returns are calculated, we use excess B. Portfolio Construction Portfolios are constructed by ordering currencies based upon interest rates. At each time t currencies are ranked from high to low based upon interest differentials versus the US dollar. Grouping currencies into portfolios is arbitrary and one can create portfolios by grouping all currencies into high to low portfolios or just trading the top three versus the bottom three, or any other arbitrary grouping scheme. In all cases, the strategy is to take long positions in the high interest rate currencies and fund these with short positions in the low interest rate currencies. Recently, some researchers have used a portfolio construction approach assigning currencies to hierarchical portfolios based upon interest rates (for instance, Lustig, Roussanov, and Verdelhan (2011)), in a so-called HML (high minus low) portfolio construction strategy. Currencies are ranked from high to low interest rates and then the currencies are assigned to six different portfolios, starting with the lowest rate currencies being assigned to portfolio 1, next higher to portfolio 2, and so on until each portfolio has an equal number of currencies. Then any remainder of high-yielding currencies is assigned to the highest portfolio. This is reasonable for analysis or investing with a large number of assets. However, if one has a low-breadth strategy, as exists in currencies, then it may be more reasonable to approach the issue more directly by simply going long the top 3 currencies versus shorts in the bottom 3, equally weighting all. This approach would be more representative of the carry trades actually implemented in the market, in particular a DM carry portfolio with only 9 investible assets does not lend itself to the HML construction 3. We will document that the performance of the carry trade is quite similar when both portfolio 3 DM carry strategies have been available to retail investors through ETFs. 4

7 construction methodologies are compared. As a result, we will focus on the simpler methodology in the remainder of the paper. C. Backtest Experience For DM currencies, we construct returns to the carry trade portfolio over the period of December 1983 to August For EM currencies, the sample is much shorter, starting in February We construct a portfolio of all EM and DM currencies by adding the EM currencies to the DM portfolio as they come on-line in the sample. The first set of results includes all currencies and then the results are recomputed over just the developed market sample and, finally, over just the shorter emerging market sample. Figure 1 plots the returns to the carry trade since 1983 for the full sample of DM and EM currencies. 4 Figure 1.a. employs an investment methodology of assigning currencies to six different portfolios as described in the previous section, and then taking long positions in the high interest rate portfolios funded by short positions in the low interest rate portfolios. Figure 1.b. employs a simple strategy of taking long positions in the three highest interest rate currencies funded by short positions in the three lowest interest rate currencies. While the second strategy outperforms the first, one can see that generally the broad experience of investing in the carry trade is similar in both approaches. The carry drawdowns happen at the same time, and the periods of best performance are similar. Overall, one can see that the carry trade was a good performer, with only a few significant setbacks until the time of the financial crisis when the drawdowns became larger and more frequent. We will present a review of the largest drawdowns in a later section, but one can now see that the drawdowns have been quite heterogeneous in terms of duration and magnitude. Given the general similarity between the two alternative portfolio construction techniques, we will focus on the simpler strategy in the rest of the paper. 4 As mentioned above, the early sample period contains only DM currencies but as data are available for the EM currencies they are added over time. 5

8 Figure 2 presents the cumulative returns to a top-3/bottom-3 portfolio approach for the DM and EM currencies. A comparison of DM carry returns, in Figure 2.a, with EM carry returns, in Figure 2.b, illustrates some interesting differences. Overall, EM carry returns have outperformed DM returns in the period of overlap. In addition, the financial crisis is seen as a DM-oriented event, where there was a severe drawdown for the DM currencies but the EM currencies came through it with only a small drawdown in comparison. One can see the importance of the choice of currency universe as the returns to carry may differ substantially. Over the period since 1997, when the EM return series begins, the correlation between DM and EM carry returns has been just One commonality is that carry performance has been challenging for both DM and EM since the financial crisis. The interest rate convergence that has occurred in that time is certainly one factor that may have temporarily reduced the opportunity set for carry trades. II. Top Carry Crashes While carry trades have offered positive returns over the long-run, it is well known that they are subject to tail risk of large drawdowns. In our daily carry portfolio returns, over the period of overlap between the DM and EM portfolios, the DM returns distribution has a mean of 0.02 percent, or 2 basis points, and a skew of while the EM distribution has a mean of 0.06 percent, or 6 basis points, and a skew of Pooling both DM and EM currencies into the All currency portfolio over the sample period of Dec 1983 to August 2013, yields a mean return of 0.03 percent, or 3 basis points, with a skew of Digging deeper into the left tail of carry returns, in this section we examine the worst drawdowns in recent experience. Drawdowns are calculated using the following methodology: a peak is determined as the highest point up until time t in the cumulative return series. A trough is the lowest point following a peak before a new 6

9 peak is established. The magnitude of the drawdown is the cumulative fall from peak to trough. The duration of the event is measured by the number of days from peak to trough. We calculate the top-10 drawdowns for the three different portfolios: developed markets, emerging markets, and the combined portfolio of all currencies. To ensure comparability of the returns, each portfolio is scaled to achieve an ex-post risk level of 10 percent over the sample period. Table 1 lists the top 10 drawdowns for the different portfolios of currencies. For each episode, the table lists the magnitude, length, and beginning and ending dates of the drawdowns. DM currencies, in Table 1.a., experienced top-10 drawdowns ranging from percent and 399 days to 7.15 percent and 30 days. The worst drawdown began in late-july 2007 and ended in early February The summer of 2007 was the first wave of the sub-prime crisis in the U.S and also the period of the quantitative equity crisis (Khandani and Lo, 2007). Negative impacts were seen first in fixed income returns, and then equity returns, and, finally, currency returns beginning in late-june. The magnitude and duration of the carry unwind associated with the financial crisis was quite unlike any other DM currency event since the early 1990s. Section 1.b. in Table 1, shows that the worst drawdown of percent for EM currencies began mid- February 1998 and lasted 92 days, until mid-june. This drawdown was associated with???. It is interesting to note that the global financial crisis is associated with the second worst drawdown for EM markets, of percent and 36. Finally, Section 1.c. of Table 1 lists the top-10 drawdowns for the combined portfolio of DM and EM currencies. The top drawdown of percent overlaps with the period of the second-worse DM drawdown and incorporates the period of the ERM crisis. The sensitivity of the results to choice of currency universe is clear. One can calculate an agreement index to identify the frequency with which one currency universe is experiencing a drawdown when the other universes are not. Doing so, it is seen that 41 percent of the days, the EM and DM portfolios do not experience drawdowns at the same time 7

10 To better understand the experience of carry drawdowns, a decomposition of each of the top carry crashes was done by currency (all returns are measured versus the US Dollar). This allows a view into which currencies contributed most to the negative performance. The carry portfolios are constructed as long the top 3 interest rate currencies and short the bottom 3. Table 2 lists the currencies in each of the top-10 negative return portfolios and their contribution to the carry drawdown. For each episode, Table 2 reports the magnitude, beginning date,and individual currency information in terms of contribution to the overall carry strategy return, exchange rate return versus the U.S. dollar, and mean holding. While the top-3, bottom-3 strategy will have each currency in the portfolio with a +1 or -1 holding, depending upon whether the portfolio holds a long or short position, over the duration of a carry drawdown the ranking of currencies may change each month so that some currencies enter the carry portfolio while others drop out, so that there may be more than 6 currencies held in each drawdown period. In addition, it is possible to have a mean zero holding when the strategy switches between a short and long position over the sample period. We should expect the high-interest rate currencies to be sold and the low-interest rate currencies to be bought in a carry crash as investors unwind their carry positions. Over the sample period studied, that generally means that positions in relatively high-interest rate currencies like the Australian and New Zealand dollar are sold to close out long positions, while relatively lowinterest rate currencies like Japanese yen and Swiss franc are bought to close out short positions. Table 2.a. indicates that these classic carry trade currencies are typically at the top of the list of contributors to the DM carry drawdowns. The second-largest DM drawdown beginning September 1992 was different in that it was the longest duration and involved quite a mix of currencies. Early in that episode, the ERM crisis emerged with the withdrawal of Italy and the UK from the exchange rate mechanism. We see the Italian lira figuring prominently in the negative returns but the pound recovered its value over the long period of the drawdown. Only 8

11 three of the top-ten DM drawdowns had the minimum six currency portfolio. In all other cases, currencies would exit and be replaced by another. For instance, the third-largest drawdown beginning April 1986 held a long Norway position for about 85 percent of the period, with the NOK being replaced by the Italian lira for about 15 percent of the time. Emerging market currencies are not as well-studied as DM currencies, so the results reported in Table 2.b. may be even more instructive. Of course, the same general dynamic should be observed where high interest rate currencies are sold while low interest rate currencies are bought leading to negative returns for those holding carry portfolios. However we do see a different dynamic in the EM portfolio, with most EM currencies depreciating against the US dollar over drawdown periods. For example, the second-largest EM drawdown occurred in the period around the Lehman crisis. We see one of the worst performing currencies over the period was the Korean Won (depreciating against the USD by about 19% over the drawdown period), which was actually short in the carry portfolio, so delivered a positive contribution to the portfolio. Other short carry currencies tended to depreciate less, e.g. the Malaysian ringgit depreciated by 3.7% and the Singapore Dollar by 4.9%. However, the big losses in the portfolio came on the long side as the long carry currencies of Brazilian real (which depreciated by 34%), Turkish lira (30%), and South African Rand (35%) generated large losses for the carry portfolio. This suggests that investors have sold both high and low interest rate currencies during EM carry crashes. Given the correlation of risk premia at times of high financial market stress, it may be that the EM currencies are being sold for reasons other than pure foreign exchange carry purposes. Some names repeat across carry crashes and are found at the top of the attribution list for EM currencies in Table 2.b. This includes relatively high interest rate currencies like Turkish lira (TRY), Brazilian real (BRL), and South African rand (ZAR) along with relatively low interest rate currencies like Taiwan dollar (TWD), Czech koruna (CZK), and Singapore dollar (SGD). 9

12 Combining DM and EM currencies together into one carry portfolio we see that the EM currencies are often at the top of the list of contributors to the carry crashes in Table 2.c. The Japanese yen (JPY) and Swiss franc (CHF) are the most prominent DM currencies in this table, as investors may take short yen and Swiss positions to fund long positions in EM currencies. It is also interesting to note how many currencies are entering and exiting the top-3/bottom-3 carry portfolio. For instance, the top drawdown occurring during the financial crisis has only one currency, the JPY, in the portfolio the entire period. A total of 12 currencies appear over the duration of the drawdown, with 9 of the 12 being EM currencies. Only the JPY, CHF, and, briefly, the EUR appear from the DM currencies. Looking across the top-10 drawdowns for the combined EM/DM portfolios, it is not surprising that when DM currencies appear, they tend to be funding currencies where short positions are held. There are few exceptions to this, like the long AUD, NZD, and NOK in the fourth largest drawdown from the summer of III. Carry Trade Returns and Financial Market Stress A. Determinants of Carry Returns A.1. The Financial Stress Index Carry trades underperform during periods of financial market stress. To analyze the effects of such stress events on the carry portfolio returns used here, we create a Financial Stress Index (FSI). This index follows the IMF (2008) in the choice of input variables. 5 The variables are: Bankbeta: the correlation of the US banking sector equity returns with the broader US equity market returns; measured as the 12-month rolling covariance of the year-over-year percent change of a country's banking sector equity index and its overall stock market index, divided by the rolling 12-month variance of the year-over-year percent change of the overall stock market index 5 Melvin and Taylor (2009) used the IMF variables to create a tradable version of the FSI. 10

13 TEDspread: the spread between 3-month interbank interest rates and the 3-month government treasury bill yield averaged over the G10 countries Yieldslope: the inverted slope of the yield curve; measured as the difference between the 3-month government bill rate and the 10 year government bond rate averaged over the G10 countries Marketreturn: this is the G10 average of the monthly return on the major equity index for the each country Marketvol: the average volatility across the G10 equity market indexes; measured as the exponentially weighted monthly returns using a 36 month half-life Currencyvol: the average volatility across the G10 exchange rates relative to the US dollar; measured as the exponentially weighted monthly returns using a 36 month halflife While we consider the contributions of the individual measures listed above, we also construct a Global FSI by equally weighting each of the individual measures. The construction methodology derives global scores for each measure by equally weighting the underlying country scores. The scores are standard-normal variables (z-scores) using time-varying means and variances of the underlying series constructed using exponentially-weighted moving averages of the mean and standard deviations using 36 month half-lives. The Global FSI and its components are plotted in Figure 3. One can see that the major market events during the period are reflected in the FSI. The FSI rises abruptly during the early wave of the sub-prime crisis in 2007 and peaks following the Lehman Bros. bankruptcy in One can also note in Figure 3 how much the different sub-indices vary through time. Looking at any one of the components could give, at best, a partial view of the risk environment. By looking across 11

14 the spectrum of risk indices, one has a more reliable view of the risk regime. We will employ these measures to assess the extent to which they can explain returns to the carry trade. B.2. Carry returns and financial market stress We begin the analysis by examining summary data for FSI measures and carry return regimes. Table 3 provides a summary of results using hit rates for FSI measures aligning with carry returns. Table 3 shows carry return regimes for the three different portfolios of All currencies, DM currencies, and EM currencies. FSI values, expressed in z-scores, are identified as falling above or below three different values of standard deviation (σ): 0, 1, or 2 standard deviations from zero. Each column represents a different measure of FSI. Then in each cell, results are summarized as (x, y, z, w) where x is percentage of periods identified where the FSI measure is above the hurdle threshold and carry is in drawdown. Accurate classification. y is percentage of periods identified where the FSI measure is below the hurdle threshold and carry is not in drawdown. Accurate classification. z is percentage of periods identified where the FSI measure is below the hurdle threshold and carry is in drawdown. False Negative. w is percentage of periods identified where the FSI measure is above the hurdle threshold and carry is not in drawdown. False Positive. For instance, the top right cell of the table represents the summary results for values of the Global FSI greater than 2 σ. For the portfolio containing All currencies, when the FSI > 2, 2.0% of the periods contain a carry drawdown; for FSI <2, 62.9% of the periods have no carry drawdown and 2.0% of the periods have carry in drawdown; and for FSI > 2, 33.1% of the periods have no drawdown. The results reported in Table 2 may be summarized as follows: 12

15 1. The FSI and subcomponents show similar classification skill in all three portfolios, but are slightly better at identifying drawdown periods for the EM markets. 2. The lower the hurdle threshold, the better the ability to identify carry unwind regimes and the fewer false signals. 3. Individual subcomponents often perform differently in the different portfolios (e.g. EM versus DM or All for CorpSpread, BankBeta and TedSpread when HURDLE = 2). While the summary data in Table 3 are instructive regarding the link between financial market stress and carry return regimes, we now push the analysis further by estimating models of carry returns as a function of the FSI variables. For each carry portfolio, we estimate three specifications. First, using the Global FSI as explanatory variable and second, using the individual FSI components as explanatory variables. Then a third specification creates an ordered variable for each FSI insight using the following methodology: each series is normalized using its full-sample mean and standard deviation to create z-scores for each. Then the monthly data are classified into an ordered variable ranking from 1 to 6 according to z-scores as follows: 1, z -2; 2, -2<z -1; 3, -1<z 0, 4, 0<z 1; 5, 1<z 2; and 6, z>2. The idea being that FSI indicators might make more sense as a discontinuous indicator rather than a continuous variable. Table 4 contains estimation results for each carry portfolio return series. Preliminary work with these data indicated that there was often a reversal with a one-day lag, so we specify the model with contemporaneous and lagged values of the FSI variables. There are several high-level results that are found in Table 4. First, the overall explanatory power of the FSI variables is similar across portfolios. R-squares of an order of magnitude of are what one typically sees in daily exchange rate models. Second, there is more explanatory power by letting the individual FSI components enter into the equation rather than average them into the Global FSI. We don t report the Global FSI results, but they were uniformly poor. Third, an examination of the 13

16 equations with the individual FSIs reveals that different variables are important for explaining EM carry returns than DM. In the All portfolio carry returns are decreasing in contemporaneous EquityVol, but increasing in lagged EquityVol. So if equity volatility is higher today, carry returns tend to fall, but if equity volatility was higher yesterday, carry returns tend to rise today. These kinds of reversals in returns are often seen in financial markets. The only other statistically significant variable in the All equation is lagged EquityReturns. Poor equity returns yesterday are associated with a carry sell-off today. DM carry returns are falling with greater contemporaneous currency and equity volatility. Lagged values of these two variables are associated with a reversal of the effect. So if the shock to volatility occurred yesterday, the market would have sold carry yesterday but bought it today. Finally, for the EM portfolio we see a different pattern. The only statistically significant variable is EquityReturns. But in this case, a fall in equity returns today is associated with higher EM carry returns, yet if the shock occurs yesterday, carry returns are lower. The lack of more individually significant variables is reflective of multicollinearity among the right-hand-side variables. A stepwise regression approach that selects explanatory variables on the basis of their contribution to explanatory power was estimated to explore the individual variables further. Results are reported in Table 5. The All portfolio has three variables selected: lagged values of EquityReturns (negative sign), EquityVolatility (positive sign), and Yieldslope (negative sign). The DM portfolio has only one variable selected: lagged TedSpread (negative sign). The EM portfolio has three variables: contemporaneous BankBeta and EquityReturns (both with a positive sign), and lagged EquityReturns (negative sign). The third specification estimated for each return series orders the independent and dependent variables from 1 to 6 by z-scores as discussed above to assess the notion that FSIs are more sensible as grouped into different levels of financial market stress rather than continuous variables. Estimation with all variables included yielded no evidence of individual statistical significance, so the stepwise method was employed here as well with a criterion for entry of a p- 14

17 value less than 0.1. The stepwise estimation results are reported in Table 6, along with OLS estimation for the ordered GlobalFSI. No variables were selected for the ordered All carry returns portfolio. It was also the case that the ordered GlobalFSI was unsuccessful in explaining ordered All portfolio returns, so we just report results for the DM and EM portfolios in Table 6. For the DM equation, just the TedSpread was selected as before. At the bottom of the table results for the GlobalFSI equation indicates that these variables were not successful in explaining the ordered DM returns. For the EM portfolio, four variables were selected: the ordered carry returns for EM currencies are increasing in ordered contemporaneous Currencyvol and lagged Equityreturns, and TEDspread. Ordered EM carry returns are decreasing in the lagged Yieldspread. In the lower part of the table, it is seen that the GlobalFSI variable is highly statistically significant in explaining ordered EM carry returns, with the contemporaneous effect negative and the lag positive. The take-away from the ordered variable approach is that it only seems useful for modelling EM carry, and is otherwise of limited value. IV. Summary and Conclusions Currency carry trades have long been a popular currency investment strategy. In recent years, the academic interest in such strategies has grown and there have been several studies focused on analysing the source of excess returns to the carry trade. The literature has always recognized the left tail risk of the carry trade but, there has been little research focused solely upon the analysis of carry drawdowns. This paper begins with a presentation of backtest results of a simple carry strategy of taking long positions in the 3 highest interest rate currencies funded with short positions in the 3 lowest interest rate currencies for 3 different portfolios: DM currencies, EM currencies, and a portfolio combining both DM and EM currencies. It is seen that there is a longrun positive excess return, but there are periodic severe negative return episodes. The EM portfolio has outperformed the DM or combined portfolio. 15

18 Digging deeper into carry returns, we chronicle the top-10 drawdowns for each portfolio. Unsurprisingly, the largest DM drawdown is associated with the financial crisis of The longest duration drawdown for the DM portfolio occurs from 1992 to However, the longest duration drawdown follows the financial crisis for the EM portfolio, occurring over the period. The pattern of carry unwind generally involves selling high interest rate currencies to reduce long positions and buying low-interest currencies to reduce short positions, so that there is a marked depreciation (appreciation) of the high (low) interest rate currencies. This results in certain currencies appearing frequently as major contributors to drawdowns. From the DM list are Australian and New Zealand dollars, Japanese yen, and Swiss franc. From the EM list is Indonesian rupiah, South African rand, Turkish lira, and Brazilian real. In different episodes, several other currencies are seen to play a role when they were important funding or risk currencies at the time. The general result that high-interest rate currencies are sold while low-interest rate currencies are bought does not always apply. An exception is seen in the case of EM currencies. In some episodes, the low interest rate EM currencies that are used as short funding currencies are seen to depreciate against the US dollar as EM currencies are sold across the board in such periods of financial market stress. In these cases, the short side of the carry trade works to mitigate the size of the carry drawdown. The final section of the paper creates a Financial Stress Index (FSI) to use in modelling daily carry returns. The idea being that carry drawdowns are known to be associated with periods of financial market stress. The FSI includes various measures of credit risk, volatility, and market returns. After reviewing the FSI construction methodology, an empirical analysis linking carry returns to the FSI reveals the following tendencies: decomposing the FSI into its constituent parts provides a sharper tool to identify carry-relevant stress events than using a Global FSI index. Turning to a regression analysis of carry returns, it is seen that the Global FSI index is not useful 16

19 in modelling carry-relevant stress in financial markets. However, the explanatory power is enhanced by estimating a model allowing the individual components to enter the regression. Results suggest that equity and currency volatility and equity returns are important variables for modelling carry returns. In addition, the TedSpread measure of credit risk is found to be important for DM carry. 17

20 References Brunnermeir, M., Nagel, S., and Pedersen, L Carry trades and currency crashes. NBER Macroeconomics Annual, International Monetary Fund, Financial stress and economic downturns. World Economic Report: Chapter 4, Jurek, J., Crash-neutral currency carry trades. Journal of Financial Economics. Forthcoming. Khandani, A., and Lo, A What Happened to the Quants in August 2007? Evidence from factors and transactions data. Journal of Financial Markets. 14, Lustig, H., Roussanov, N and Verdelhan A., Common risk factors in currency markets. Review of Financial Studies. 24, Melvin, M. and Taylor, M., The crisis in the foreign exchange market. Journal of International Money and Finance. 28,

21 Table 1: The Top 10 Drawdowns from Currency Carry Trade Portfolios The carry portfolios are implemented as long positions in the top 3 interest rate countries funded by short positions in the lowest 3 interest rate currencies, all equally weighted. 1.a. Developed Market Currencies Magnitude Length Beg Date End Date Jul-07 2-Feb Sep Apr Apr Sep Apr Aug Oct Dec Dec May Aug Oct Aug Oct Feb May Jun Jul-02 1.b. Emerging Market Currencies Magnitude Length Beg Date End Date Feb Jun Sep Oct Feb Feb Apr Aug Apr May Jan Mar Jul Aug May Jul Oct-98 4-Nov Oct Dec-09 19

22 1.c. All Currencies Magnitude Length Beg Date End Date Aug-08 2-Feb Aug Apr May Jun May Aug Apr Aug Feb Feb Aug Oct Oct Mar Apr May Jul Aug-07 20

23 Table 2: Individual Currency Attribution during Top Carry Drawdowns The carry returns are decomposed by currency in order to see which currencies have been important contributors to drawdowns. The carry portfolios are implemented as long positions in the top 3 interest rate countries funded by short positions in the lowest 3 interest rate currencies, all equally weighted. There may be more than 6 currencies listed for a drawdown in cases where the top 3 and bottom 3 currencies changed over the duration of the drawdown so that different currencies enter and exit the carry portfolio. We focus on the following set of currencies that are widely traded by active currency investors (ISO codes in parentheses): Australia (AUD), Brazil (BRL), Canada (CAD), Chile (CLP), Colombia (COP), Czech Republic (CZK), Euro (EUR), Hungary (HUF), India (INR), Indonesia (IDR), Japan (JPY), Malaysia (MYR), Mexico (MXN), New Zealand (NZD), Norway (NOK), Peru (PEN), Philippines (PHP), Poland (PLN), Romania (RON), Russia (RUB), Singapore (SGD), South Africa (ZAR), South Korea (KRW), Sweden (SEK), Switzerland (CHF), Taiwan (TWD), Thailand (TWD), Turkey (TRY), and the United Kingdom (GBP). 2.a. Developed Market Currencies Order Magnitude Beginning Date Currency Return Attribution Jul-07 NZD AUD JPY NOK SEK CHF USD GBP EUR Strategy Return Forward Return Mean hold Sep-92 JPY ITL CHF NOK SEK NLG AUD USD FRF GBP CAD NZD BEF Strategy Return Forward Return Mean hold Apr-86 CHF JPY AUD NLG NZD NOK ITL Strategy Return Forward Return Mean hold Apr-13 AUD NZD NOK JPY EUR CHF Strategy Return Forward Return Mean hold Oct-87 JPY CHF NLG AUD NZD NOK ITL Strategy Return Forward Return Mean hold Dec-05 NZD SEK CHF JPY USD AUD GBP Strategy Return Forward Return Mean hold Aug-98 JPY CHF NLG NZD IEP GBP SEK NOK Strategy Return Forward Return Mean hold Aug-90 JPY AUD NZD CHF NLG USD BEF ITL GBP Strategy Return Forward Return Mean hold Feb-04 NZD AUD GBP USD CHF JPY Strategy Return Forward Return Mean hold Jun-02 JPY CHF AUD NZD USD NOK Strategy Return Forward Return Mean hold

24 2.b. Emerging Market Currencies Order MagnitudeBeginning Date Currency Return Attribution Feb-98 IDR THB PHP MYR HUF SGD MXN TWD TRY INR Strategy Return Forward Return Mean hold Sep-08 ZAR TRY BRL RON SGD MYR CZK KRW Strategy Return Forward Return Mean hold Feb-01 TRY MXN SGD TWD MYR THB Strategy Return E Forward Return Mean hold Apr-11 INR BRL ZAR TRY COP CLP IDR RUB PEN PHP SGD CZK TWD RON Strategy Return Forward Return Mean hold Apr-06 TRY BRL CZK IDR MYR TWD SGD Strategy Return Forward Return Mean hold Jan-08 ZAR TRY TWD PEN SGD INR KRW COP Strategy Return Forward Return Mean hold Jul-07 BRL TRY ZAR CZK TWD SGD Strategy Return Forward Return Mean hold May-02 TRY THB SGD TWD ZAR MYR Strategy Return Forward Return Mean hold Oct-98 IDR THB TWD SGD TRY MXN Strategy Return Forward Return Mean hold Oct-09 ZAR CLP RON BRL PEN THB TWD RUB Strategy Return E Forward Return Mean hold

25 2.c. All Currencies Order Magnitude Beginning Date Currency Return Attribution Aug-08 RON ZAR TRY JPY BRL RUB CHF TWD SGD IDR EUR KRW Strategy Return Forward Return Mean hold Aug-92 JPY ITL CHF SEK NOK SGD NLG MYR FRF ZAR DEM NZD CAD BLF Strategy Return Forward Return Mean hold May-98 IDR THB NLG ATS CHF TRY JPY Strategy Return Forward Return Mean hold May-86 CHF AUD NZD JPY DEM NOK Strategy Return Forward Return Mean hold Apr-11 INR BRL ZAR TRY COP EUR CLP PEN PLP IDR RUB JPY CZK RON TWD CHF Strategy Return Forward Return Mean hold Feb-01 TRY JPY MXN SGD MYR SEK Strategy Return E Forward Return Mean hold Aug-97 IDR PLP CHF MXN FNM NLG JPY TRY Strategy Return Forward Return Mean hold Oct-07 ZAR JPY TRY PEN TWD CHF BRL COP Strategy Return Forward Return Mean hold Apr-06 TRY BRL IDR CHF JPY TWD Strategy Return Forward Return Mean hold Jul-07 BRL TRY ZAR JPY CHF TWD SGD Strategy Return Forward Return Mean hold

26 Table 3: The Financial Stress Index and Carry Drawdowns The table shows hit rates for the FSI and its subcomponents for different number of standard deviation hurdles for FSI z-scores aligning with carry return regimes as measured by three different portfolios for all currencies, DM currencies, and EM currencies. The results will be presented as (x, y, z, w) where x is percentage of periods identified where the FSI or identified subcomponent is above the σ hurdle threshold and carry is in drawdown. Accurate classification. y is percentage of periods identified where the FSI or identified subcomponent is below the σ hurdle threshold and carry is not in drawdown. Accurate classification. z is percentage of periods identified where the FSI or identified subcomponent is below the σ hurdle threshold and carry is in drawdown. False Negative. w is percentage of periods identified where the FSI or identified subcomponent is above the σ hurdle threshold and carry is not in drawdown. False Positive. HURDLE = 2 currencyvol eqret eqvol yieldslope tedspread bankbeta CorpSpread Global_FSI ALL (1.2%, 63.6%, 1.3%, 33.9%) (1.4%, 62.6%, 2.3%, 33.8%) (1.0%, 62.6%, 2.3%, 34.1%) (0.1%, 62.9%, 2.0%, 35.0%) (1.8%, 63.1%, 1.8%, 33.3%) (0.3%, 63.2%, 1.7%, 34.8%) (3.2%, 60.2%, 4.7%, 31.9%) (2.0%, 62.9%, 2.0%, 33.1%) EM (0.2%, 79.3%, 2.3%, 18.3%) (0.9%, 78.7%, 2.8%, 17.6%) (0.0%, 78.3%, 3.3%, 18.4%) (0.0%, 79.4%, 2.1%, 18.5%) (1.0%, 79.0%, 2.5%, 17.4%) (0.8%, 80.3%, 1.2%, 17.7%) (2.1%, 75.7%, 5.8%, 16.4%) (1.1%, 78.6%, 2.9%, 17.4%) DM (1.2%, 65.3%, 1.3%, 32.2%) (2.4%, 65.3%, 1.3%, 31.0%) (1.6%, 64.9%, 1.7%, 31.8%) (0.1%, 64.6%, 2.0%, 33.4%) (2.6%, 65.7%, 0.9%, 30.8%) (0.3%, 64.9%, 1.7%, 33.1%) (4.7%, 63.4%, 3.2%, 28.7%) (2.9%, 65.5%, 1.1%, 30.5%) HURDLE = 1 currencyvol eqret eqvol yieldslope tedspread bankbeta CorpSpread Global_FSI ALL (7.8%, 55.5%, 9.4%, 27.3%) (5.3%, 56.7%, 8.2%, 29.8%) (2.3%, 55.3%, 9.6%, 32.8%) (6.4%, 54.3%, 10.6%, 28.7%) (4.8%, 56.7%, 8.1%, 30.3%) (7.3%, 53.9%, 11.0%, 27.8%) (4.0%, 54.2%, 10.7%, 31.1%) (6.7%, 55.9%, 9.0%, 28.4%) EM (1.5%, 65.9%, 15.6%, 16.9%) (3.0%, 70.9%, 10.6%, 15.5%) (1.0%, 70.7%, 10.9%, 17.5%) (1.1%, 65.7%, 15.8%, 17.3%) (2.8%, 71.4%, 10.1%, 15.7%) (3.8%, 67.0%, 14.6%, 14.7%) (2.8%, 69.5%, 12.0%, 15.7%) (2.4%, 68.3%, 13.2%, 16.1%) DM (9.0%, 58.5%, 8.1%, 24.4%) (7.1%, 60.1%, 6.5%, 26.3%) (2.2%, 56.9%, 9.7%, 31.2%) (5.6%, 55.2%, 11.4%, 27.8%) (6.3%, 60.0%, 6.6%, 27.1%) (6.5%, 54.8%, 11.8%, 26.9%) (6.7%, 58.6%, 8.0%, 26.7%) (8.3%, 59.3%, 7.3%, 25.1%) HURDLE = 0 currencyvol eqret eqvol yieldslope tedspread bankbeta CorpSpread Global_FSI ALL (18.6%, 26.5%, 38.4%, 16.5%) (16.7%, 37.3%, 27.6%, 18.4%) (10.9%, 28.5%, 36.4%, 24.2%) (17.1%, 33.1%, 31.8%, 18.0%) (15.7%, 35.4%, 29.5%, 19.4%) (22.2%, 36.9%, 28.0%, 12.9%) (7.5%, 46.6%, 18.3%, 27.6%) (17.0%, 34.4%, 30.5%, 18.1%) EM (10.3%, 34.8%, 46.7%, 8.2%) (7.8%, 45.1%, 36.5%, 10.7%) (6.9%, 41.1%, 40.4%, 11.6%) (5.7%, 38.3%, 43.2%, 12.8%) (8.9%, 45.2%, 36.3%, 9.6%) (9.5%, 40.8%, 40.7%, 9.0%) (6.5%, 62.2%, 19.3%, 12.0%) (5.1%, 39.1%, 42.4%, 13.4%) DM (19.0%, 28.5%, 38.1%, 14.5%) (16.7%, 39.0%, 27.6%, 16.7%) (11.7%, 31.0%, 35.6%, 21.7%) (16.1%, 33.8%, 32.8%, 17.3%) (17.5%, 38.8%, 27.7%, 15.9%) (17.9%, 34.3%, 32.3%, 15.5%) (8.2%, 48.9%, 17.7%, 25.2%) (19.0%, 38.1%, 28.5%, 14.4%) 24

27 Table 4: Currency Carry Returns and Financial Stress Indicators Model Estimation The table reports estimation results for regressions of daily returns from currency carry portfolios as a function of financial stress indicators (FSI). Carry portfolios include All currencies, DM currencies, and EM currencies. The FSI measures include the beta of US bank stocks with the US market portfolio, realized currency volatility, US equity market returns, US equity market volatility, the TED spread of Eurodollar yields over Treasuries, and the slope of the yield curve (Yieldspread). Higher values of the FSI indicates greater market stress. Dependent Variable: RETURNALL Dependent Variable: RETURNDM Dependent Variable: RETURNEM Variable Coefficient Prob. Variable Coefficient Prob. Variable Coefficient Prob. C C C BANKBETA BANKBETA BANKBETA CURRENCYVOL CURRENCYVOL CURRENCYVOL EQRET EQRET 7.46E EQRET EQVOL EQVOL EQVOL TEDSPREAD TEDSPREAD TEDSPREAD YIELDSLOPE YIELDSLOPE YIELDSLOPE BANKBETA(-1) BANKBETA(-1) BANKBETA(-1) CURRENCYVOL(-1) CURRENCYVOL(-1) CURRENCYVOL(-1) EQRET(-1) EQRET(-1) EQRET(-1) EQVOL(-1) EQVOL(-1) EQVOL(-1) 6.75E TEDSPREAD(-1) -6.48E TEDSPREAD(-1) TEDSPREAD(-1) -3.66E YIELDSLOPE(-1) YIELDSLOPE(-1) YIELDSLOPE(-1) R-squared R-squared R-squared F-statistic F-statistic F-statistic Prob(F-statistic) Prob(F-statistic) Prob(F-statistic)