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Applied Economics and Finance
Vol. 3, No. 1; February 2016
ISSN 2332-7294 E-ISSN 2332-7308
Published by Redfame Publishing
URL: http://aef.redfame.com
103
The Profitability of Using Pegged Currencies in Carry Trade:
A Case of Saudi Riyal
Musaed S. AlAli1, Lamya S. AlAli2 & Fatema K. AlKhalifa3
1 School of Economics, Finance and Marketing, RMIT University, Melbourne, Australia.
2 College of Business Studies, Public Authority for Applied Education and Training, Kuwait.
3 The Higher Institute of Telecommunication and Navigation, Public Authority for Applied Education and Training,
Kuwait.
Correspondence: Musaed S. AlAli, School of Economics, Finance and Marketing, RMIT University, Melbourne,
Australia.
Received: December 25, 2015 Accepted: January 9, 2016 Available online: January 15, 2016
doi:10.11114/aef.v3i1.1306 URL: http://dx.doi.org/10.11114/aef.v3i1.1306
Abstract
This paper examines the profitability of using pegged currencies in carry trade. Conducting this exercise on Saudi riyal
against six floating currencies has proven to be very rewarding especially when enhanced with forecasting methods.
Carry trade is a very popular currency speculation strategy among traders, where they borrow low-interest currencies
and invest in high-interest currencies. It is a strategy that takes advantage of interest rate differentials between two
currencies. Such strategy should not work under uncovered interest parity (UIP), since according to UIP high interest
rate currencies should depreciate against low interest rate currencies by the interest rate differential itself. But studies
have shown that UIP does not stand and carry traders are profiting from it. As a result of its failure, carry traders are
making returns matching the returns of the S&P 500 and outperforming it in terms of the Sharpe ratio.
Keywords: Carry Trade, Random Walk, Uncovered Interest Parity (UIP), Saudi Riyal (SAR).
1. Introduction
“Profitable and risky, but attractive” this is how La Marca (2007) describes carry trade. The simplicity of such strategy
led many economists and practitioners such as Jorda and Taylor (2009) to consider it as a primitive and naïve trading
strategy. But despite being labelled as a naïve strategy by many, Burnside et al. (2006), Dunis and Miao (2007), Moosa
(2008), Darvas (2009), Menkhoff et al. (2012), and Jurek (2014) all confirm that carry trade does not only produce
similar returns to the of the S&P 500, but it is also less risky, generating better Sharpe ratio (Gyntelberg and Remolona,
2007). Brunnermeier and Pedersen (2009) found that carry trade returns are much less variable than stock returns, with
an annualized standard deviation of about 5 percent (compared to about 15 percent for stocks); as a result, the Sharpe
ratio of the carry trade is double that of stocks. A study conducted by Burnside et al. (2007) for the period 1997 to 2006,
where they used the U.S. dollar as the funding currency in carry trade,- they concluded that carry trade is a profitable
strategy generating an annualized Sharpe ratio of 1.32 compared to 0.23 for the U.S. stock market. Gyntelberg and
Remolona (2007) show that the annualised average daily return on the Australian dollar/yen carry trade was 12.5% per
year during the period September 2001 to September2007, compared to 3.6% for the S&P 500 index. Neely and Weller
(2013) stated that there is a growing body of literature indicating that carry trade have statistically and economically
significant positive excess returns and a Sharpe ratio about double that of equity markets.
UIP is an arbitrage condition indicating that no profit opportunity should arise from the interest rate differential between
two currencies. According to UIP, a low interest rate currency should appreciate against the high interest rate currency
by the interest rate differential itself. However, this is not the case. It has been noticed that currencies with higher
interest rates tend to appreciate against low interest rate currencies, contradicting UIP. The success of carry trade is a
result of the failure of uncovered interest rate parity (UIP). Baillie and Chang (2011) described carry trade as a
speculation against UIP, while Gyntelberg and Remolona (2007) described it as nothing more than a bet against UIP.
Although many researchers, such as Gyntelberg and Remolona (2007), Baillie and Chang (2011), and others, tend to
imply a link between the failures of UIP and profitability, Moosa and Halteh (2012) state that, although the failure of
UIP is a necessary condition for a profitable carry trade, it is not a sufficient enough condition. They argue that big
movements in the currency markets which satisfy the failure of UIP might wipe out the interest rate differential and
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even produce a losing position.
Literature shows that carry trade flourishes during the periods of high interest rate differential and low exchange rate
volatility. Galati et al. (2007) provided evidence indicating that foreign exchange trade volumes are positively correlated
with higher domestic interest rates. Hattori and Shin (2007) found evidence indicating that volumes in carry trades
involving the yen are higher when interest differentials against the yen are high. Jylha and Suominen (2011) found that
the number of speculators increases as the interest rate differential goes higher. But being a leveraged strategy as
described by Sy and Tabarraei (2009), any swing in the exchange rates would result in igniting stop loss orders adding
to the momentum of the exchange rate move resulting in further losses for traders. That’s why predicting currency
crashes is crucial for carry traders. Embedding a forecasting element into the carry trade decision-making process could
reduce the exchange rate risk, enhance profitability and improve risk-adjusted returns, as concluded by Jorda and Taylor
(2009), Moosa (2010), Schmidbauer et al. (2010), Li (2011), Moosa and Halteh (2012), and others. For instance, Della
Corte et al. (2009) documented that there is a significant economic benefit to an investor who exploits deviations from
UIP by forecasting currency returns. Li (2011) found that the profitability and risk-return measures can be improved
when the selection process is enhanced with a forecasting element. Also, Bhatti (2012) found that, other than the interest
rate differential, the expected change in the exchange rate over the investment holding period is an important factor in
determining the return on carry trade. Moosa and Halteh (2012) also stated that the interest rate differential is not a good
indicator for carry trade return.
The magnitude of error was used by Meese and Rogoff (1983) to evaluate the reliability of the forecasting model. With
the inability to beat such criteria, a conclusion was drawn among researchers and practitioners that professional
forecasts would work just as well as a naïve forecasting model if not worse. This pessimistic effect on the field of
exchange rate modelling led many such as Fair (2008) to describe exchange rate equations as “not the pride of open
economy macroeconomics” and argue that the “general view still seems pessimistic”. But, Chow et al. (2007) questioned
the soundness of using such measures to evaluate the forecasting model by stating, “It seems irrational for
profit-maximizing firms to ‘waste’ millions of dollars generating and buying professional forecasts”. As a result of their
findings, they suggested that it might not be suitable to judge the quality forecasting models using forecast error
measures, but rather use profit as a judging criterion. Cheung et al. (2005) noted that using criteria other than the mean
square error (MSE) is not “changing the rules of the game” and minimizing the mean square error may not be important
from an economic standpoint, implying that relying on mean square error may result in overlooking other important
aspects of prediction, such as profitability.
Since the failure of beating Meese and Rogoff’s (1983) magnitude of error criteria, researchers have been exploring new
measures to evaluate the forecasting models. Boothe and Glassman (1987) compared exchange rate forecasting models
using two different criteria: accuracy (measured by the root mean square error) and profitability. They conclude that
there is no obvious relationship between accuracy measures and profitability. Further, a strong relation between
direction accuracy and profitability was found but not between error measures and profitability as concluded by Leitch
and Tanner (1991). They argued that direction accuracy may be more relevant for profitability than error measures and
that measuring the forecasting accuracy based on the magnitude of the error has no predictable relation to profitability.
Engle and Hamilton (1990) supported the use of direction accuracy, by describing it as “not a bad proxy for a
utility-based measure of forecasting performance”. Moosa and Burns (2012) support that view by stating that
profitability is consistently related to direction accuracy while the random walk does not predict the direction. Moosa
(2013) suggested that profitability is the ultimate test of forecasting accuracy.
2. Methodology
Two strategies will be examined in this paper. The first strategy is the conventional carry trade that is based only on the
interest rate differential, and the second is a forecasting-based strategy that takes both the interest rate differential and
the forecasted exchange rate into consideration.
Let and be the interest rates for currencies x and y, respectively. In addition, let S be the spot rate between the
two currencies measured as one unit of y against x, so appreciation of y against x would result in a higher S, and vice
versa.
Under carry trade, when then:
1. At time t, the carry trader would borrow x at for the period t to t+1.
2. That amount borrowed is converted to y at , obtaining 1/ units of y. This amount is invested at for the period
t to t+1.
3. At t+1, the y value of the investment will be (1/)(1+.
4. The x currency value of the investment, converted at the spot rate prevailing at t+1, is
Applied Economics and Finance Vol. 3, No. 1; 2015
105
(.
5. At t+1, the loan on x matures, and the amount (1+ has to be repaid.
In this case the return on carry trade is given by;
, (1)
which can be rewritten as
, (2)
where is the percentage change in the exchange rate between t and t+1. The carry trade operation is implicitly
based on the assumption of a random walk without drift (Moosa, 2004), which means that = 0. Thus, carry trade is
profitable as long as . (That is, as long as the interest rate differential is larger than the depreciation
of currency y against currency x.)
Because of the changes in interest rates differential, it is necessary to switch the role of the currencies, so the general
formula for calculating the rate of return on the carry trade will be as follows:
(3)
The forecasting-based strategy involves calculating the expected rate of return and conducting the position accordingly.
The expected return is calculated as follows:
, (4)
Where
is the calculated percentage change in exchange rate based on the forecasting model. Thus, we go long y
and short x if > 0 and vice versa. In that case, the profitability of the forecasting-based strategy is as follows:
(5)
Since the Saudi riyal is pegged to the U.S. dollar at 3.75 riyals per dollar, with a very narrow fluctuation band, the
exchange rate of the riyal will reflect the economic conditions of the U.S. dollar. Thus, when calculating for
using
Frenkel-Bilson flexible price monetary model of exchange rates, we will be doing so for the U.S. dollar.
, (6)
where is the natural log of the exchange rate, is the natural log of the money supply, is the natural log of the
industrial production, is the nominal interest rate, is the error factor, and a and b refer to the countries whose
currencies are involved. Here, country b will have its currency as the base currency in the exchange rate pair. The
forecasted exchange rate will be as follows:
, (7)
Where is the estimated value of and so on. To convert the natural log forecasted exchange rate to the estimated
exchange rate, the following is applied:
(8)
is calculated from
and , which can be used to calculate the expected return in equation (4).
3. Data and Empirical Results
The empirical results presented in this paper are based on six currency combinations involving the Saudi riyal (SAR)
against the Japanese yen (JPY), the British pound (GBP), the Korean won (KRW), the Singaporean dollar (SGD), the
Canadian dollar (CAD), and the Swiss franc (CHF). Monthly data were used for the period of January 2001 to
December 2011. Data were obtained from the International Financial Statistics (CD-ROM) and the DataStream
terminal.
Following Meese and Rogoff (1983) the magnitude of error measures were used to evaluate the forecasting model
against the random walk. Table 1 show that the monetary model was unable to beat the random walk in any pair. The
error measuring criteria for the average of the six pairs is shown in table 2. By looking at the mean absolute error (MAE)
for both strategies, it is 1.78 for the carry trade compared to 7.27 for the forecasting-based model. The mean square
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106
error (MSE) is 6.87 for the carry trade while, for the forecasting-based model, it is 92.4. In terms of RMSE, the results
were 2.52 and 9.42 for the random walk and forecasting-based models, respectively. The Theil inequality coefficient (U)
concludes the findings in one number, confirming the supremacy of the random walk over forecasting models by
showing no result below 1. The same results can be seen at the individual pairs, where none of the individual pairs
out-performed the random walk in all error measures.
Table 1. Results for Individual Pairs
SGD/SAR
SAR/GBP
JPY/SAR
SAR/KRW
CAD/SAR
CHF/SAR
CT
FC
CT
FC
CT
FC
CT
FC
CT
FC
CT
FC
MAE
1.1
4.5
1.6
6.3
2.0
7.7
2.1
8.4
1.4
6.9
2.5
9.8
MSE
2.2
40.2
4.6
68.4
6.4
86.3
14.1
111.9
3.6
90.7
10.3
156.9
RMSE
1.5
6.3
2.2
8.3
2.5
9.3
3.8
10.6
1.9
9.5
3.2
12.5
U
4.2
3.77
3.72
2.79
5.00
3.91
Ave Interest Diff
3.48
1.56
4.68
3.60
3.00
3.84
Direction Accuracy %
55.31
50.28
50.84
60.89
48.60
55.87
Confusion Rate %
44.69
49.72
49.16
39.11
51.40
44.13
Mean Return
2.01
2.14
2.24
-0.61
0.90
3.76
-0.02
10.80
0.42
1.47
0.84
1.39
Cumulative Return
32.17
34.89
33.94
-12.45
7.78
65.35
-13.37
345.58
3.13
20.66
3.36
12.17
Standard Deviation
18.1
18
26.4
26.04
30.72
30.48
45.24
43.92
22.92
22.80
38.76
38.64
Sharpe Ratio
0.11
0.12
0.08
-0.02
0.03
0.12
0.00
0.25
0.02
0.06
0.02
0.04
VaR 99%
3.53
3.93
5.64
5.62
6.09
4.72
11.56
5.31
4.76
4.20
6.33
6.32
VaR 95%
1.77
2.07
3.39
3.93
4.26
3.66
4.37
3.10
2.87
2.57
4.97
4.62
CT is carry trade and FC is the forecasting based strategy.
When it comes to measures of return, it can be seen that carry trade generated positive mean returns in five out of the
six pairs; while, the forecasting-based strategy also produced five pairs with positive mean returns. SAR/GBP generated
the highest mean return of 2.24% in carry trade; while, SAR/KRW showed the biggest return of 10.80% in the
forecasting-based strategy. The only pair that showed negative return was SAR/KRW in carry trade and SAR/GBP for
the forecasting-based strategy. Looking at carry trade average mean return for the six pairs it was 1.065%, compared to
3.16% for the forecasting-based strategy. When embedding the forecasting model into the selection process, five of the
six pairs showed improvements in mean return, except for SAR/GBP where the mean return for carry trade surpassed
the mean return of the forecasting-based strategy. In regard to the relation between interest rate differential and mean
return. The results were consistent with Moosa (2008) and Moosa and Halteh (2012) that higher interest rate differential
do not necessarily mean a higher return. It can be seen from the results that while JPY/SAR had the largest interest rate
differential, it didn’t produce the highest mean return. Adding to it, while SAR/KRW had an interest rate differential of
3.60%, it generated a negative mean return. In terms of cumulative return, the forecasting-based strategy outperformed
the carry trade in five out of the six pairs, except for SAR/GBP, where carry trade produced a better cumulative return.
The pair that produced the highest cumulative return under the carry trade was SAR/GBP, with 33.94%, while
SAR/KRW produced an astounding cumulative return of 345.58%, up from -13.37% under the carry trade. This huge
gain in SAR/KRW was a result of the high volatility in the exchange rate of the Korean won during the 1997 Asian
financial crisis. The forecasting model was able to capture most of these movements resulting in the huge improvement
in cumulative return.
Looking at the volatility, carry trade had a mean standard deviation for the six pairs of 30.36 compared to 29.98 for the
forecasting-based strategy. SAR/KRW showed the highest standard deviation in both the carry trade and the
forecasting-based strategy, with 45.24 and 43.92, respectively. By looking at the Sharpe ratio, it can be seen that the
Sharpe ratio has improved under the forecasting-based strategy in five out of the six pairs, with SAR/KRW being the
most improved among all pairs. Under carry trade, the average Sharpe ratio for the six pairs was 0.043 while that
average improved to 0.095 under the forecasting-based strategy.
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107
Table 2. Cumulative Results
CT
FC
MAE
1.78
7.27
MSE
6.87
92.4
RMSE
2.52
9.42
U
3.90
Ave Interest Diff
3.36
Direction Accuracy
53.63%
Confusion Rate
46.37%
Mean Return
1.065
3.16
Cumulative Return
11.17
77.70
Standard Deviation
30.36
29.98
Sharpe Ratio
0.043
0.095
VaR 99%
6.32
5.01
VaR 95%
3.605
3.325
Regarding direction accuracy, it can be seen from the results that the forecasting-based strategy produced five pairs with
an accuracy level of more than 50% and only one pair, CAD/SAR, with an accuracy level of less than 50%. When
associating direction accuracy to mean returns it can be seen that four out of the five pairs with accuracy rates above 50%
showed an improvement to their mean returns; the only pair that did not was SAR/GBP. On the other side, despite
having direction accuracy that is less than 50%, CAD/SAR showed an improvement in mean return. This result tends to
contradict Leitch and Tanner’s (1991) finding that there is a strong relation between direction accuracy and profitability.
When it comes to value-at-risk (VaR) at the 99% confidence level, five out of the six pairs showed improvement when
carry trade was enhanced with a forecasting element. SAR/KRW had the most improvement, from 11.56% to 5.31%,
while SGD/SAR was better off in carry trade. At the 95% confidence level, four out of the six pairs showed an
improvement in VaR, while SGD/SAR and SAR/GBP were the only two pairs with a higher expected loss in the
forecasting-based model than in carry trade model. When comparing the two strategies, the forecasting-based strategy
improved the average VaR at both the 99% and the 95% confidence level. At the 99% confidence level the average for
the six pairs declined from 6.32% to 5.01%, while at the 95% confidence level, the average went down from 3.605% to
3.325%.
When comparing carry trade to the S&P 500 as shown in table 3, the results show that the mean return on carry trade
was lower than the return on the S&P 500. For the sample period carry trade produced an average mean return for the
six pairs of 1.065% compared to 1.875% for the S&P 500. On the other hand, carry trade demonstrated lower volatility
than the stock market which is consistent with Brunnermeier and Pedersen (2009) findings. When it comes to the
Sharpe ratio, it can be seen that carry trade produced a better Sharpe ratio of 0.043 compared to 0.034 for the S&P 500.
This finding is consistent with Burnside et al (2007) findings that carry trade produces better Sharpe ratio than the S&P
500.
Table 3. Comparative Results between Carry Trade and S&P 500
CT
S&P 500
Mean Return
1.065
1.872
Standard Deviation
30.36
54.72
Sharpe Ratio
0.043
0.034
VaR 99%
6.32
10.1
VaR 95%
3.605
6.995
4. Conclusion
The results presented in this paper in using pegged currencies in carry trade were consistent with AlAli and AlKulaib
(2015) findings that pegged currencies can be used in carry trade with encouraging results. This paper demonstrated that
the use of the Saudi riyal in a carry trade, in its simplest form, can generate positive returns with a better Sharpe ratio
than the S&P 500 confirming the findings of Gyntelberg and Remolona (2007), Brunnermeier and Pedersen (2009) and
others. Adding forecasting techniques into the selection process led to an improvement in both profitability and the
risk-adjusted return. Using six currency combinations, with the Saudi riyal in each one of the pairs, a profit was made in
five of the six pairs when interest rate differential was the sole selection criterion. The number of profitable pairs stayed
the same when a forecasting technique was introduced, but the profitability and risk-adjusted return were improved. We
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also conclude that the interest rate differential is not associated with profit, confirming the findings of Moosa (2008)
and Moosa and Halteh (2012). On the other hand, we were unable to establish a relation between direction accuracy and
profitability.
The findings of this paper also support Meese-Rogoff (1983) who found that no forecasting model can outperforms the
naïve random walk in terms of the magnitude of error measurements such as root mean square error. But, in terms of
profitability and risk-adjusted returns, the monetary model showed that it performs well in generating better returns than
random walk. These results answer the question of Chow et al. (2007) regarding the reason behind the huge amount of
money paid by profit-maximizing firms to purchase forecasting models.
References
AlAli, M., & AlKulaib, Y. (2015). Carry Trade Profitability Using Pegged Currency: A Case of the Qatari Riyal.
International Journal of Economics and Finance, 7(10), 215-221. http://dx.doi.org/10.5539/ijef.v7n10p215
Baillie, R., & Chang, S. (2011). Carry Trades, Momentum Trading and the Forward Premium Anomaly. Journal of
Financial Markets, 14(3), 441-464. http://dx.doi.org/10.1016/j.finmar.2011.01.001.
Bhatti, R. (2012). On Return and Risk in Carry Trades: A Case of the Pak Rupee. International Journal of Economic and
Finance Studies, 4(2), 201-214.
Boothe, P., & Glassman, D. (1987) Comparing Exchange Rate Forecasting Models: Accuracy versus Profitability,
International Journal of Forecasting, 3, 65-79. http://dx.doi.org/10.1016/0169-2070(87)90079-3
Brunnermeier, M., & Pedersen, L. (2009). Market Liquidity and Funding Liquidity. Review of Financial studies, 22 (6),
2201-2238. http://dx.doi.org/10.1093/rfs/hhn098.
Burnside, C., Eichenbaum, M., Kleshchelski, I., & Rebelo, S. (2006). The Returns to Currency Speculation. NBER
Working Paper, 12489. Cambridge, MA: National Bureau of Economic Research.
Burnside, C., Eichenbaum, M., Kleshchelski, I., & Rebelo, S. (2007). The Returns to Currency Speculation in Emerging
Markets, NBER Working Paper, 12916. Cambridge, MA: National Bureau of Economic Research.
http://dx.doi.org/10.1257/aer.97.2.333.
Cheung, Y., Chinn, M., & Garcia, A. (2005). Empirical Exchange Rate Models of the Nineties: Are Any Fit to Survive?.
Journal of International Money and Finance, 24, 1150-1175. http://dx.doi.org/10.1016/j.jimonfin.2005.08.002.
Chow, K., & Tan, K. L. Y. (2007). The Use of Profits as Opposed to Conventional Forecast Evaluation Criteria to
Determine the Quality of Economic Forecasts. Singapore: Nanyang Business School, Nanyang Technological
University.
Darvas, Z. (2009). Leveraged Carry Trade Portfolios. Journal of Banking and Finance, 33(5), 944-957.
http://dx.doi.org/10.1016/j.jbankfin.2008.10.007.
DellaCorte, P., Sarno, L., & Tsiakas, I. (2009). An Economic Evaluation of Empirical Exchange Rate Models. Review of
Financial Studies, 22, 3491-3530. http://dx.doi.org/10.1093/rfs/hhn058.
Duni, C., & Miao, J. (2007). Trading Foreign Exchange Portfolios with Volatility Filters: The Carry Trade Model
Revisited. Applied Financial Economics, 17(3), 249-255. http://dx.doi.org/10.1080/09603100500447578.
Fair, R. (2008). Estimating Exchange Rate Equations Using Estimated Expectations, Yale University ICF Working Paper,
07-18.
Galati, G., Heath, A., & McGuire, P. (2007). Evidence of Carry Trade Activity, BIS Quarterly Review, 27-41.
Gyntelberg, J., & Remolona, E. (2007). Risk in Carry Trade: A Look at Target Currencies in Asia and the Pacific. BIS
Quarterly Review, 4, 73-82.
Hattori, M., & Shin, H. (2007). The Broad Yen Carry Trade, Institute for Monetary and Economic Studies Paper, No.
07-E-19, Bank of Japan.
Jorda, O., & Taylor, A. (2009). The Carry Trade and Fundamentals: Nothing to Fear but FEER Itself , NBER Working
Paper, 15518, Cambridge, MA: National Bureau of Economic Research.
http://dx.doi.org/10.1016/j.jinteco.2012.03.001.
Jurek, J. (2014). Crash-neutral Currency Carry Trades. Journal of Financial Economics, 113(3), 325-347.
http://dx.doi.org/10.1016/j.jfineco.2014.05.004.
Jylhä, P., & Suominen, M. (2011). Speculative Capital and Currency Carry Trades. Journal of Financial Economics, 99(1),
60-75. http://dx.doi.org/10.1016/j.jfineco.2010.07.006.
LaMarca, M. (2007). Carry trade and financial fragility. Coping with globalized finance: recent challenges and long
Applied Economics and Finance Vol. 3, No. 1; 2015
109
term perspectives. UNCTAD/GDS/2007/2, United Nations, Geneva and New York.
Leitch, G., & Tanner, J. (1991). Economic Forecast Evaluation: Profit Versus the Conventional Error Measures.
American Economic Review, 81, 580-590.
Li, M. (2011). An Evaluation of Exchange Rate Models by Carry Trade. Journal of Economics and International Finance,
3, 72-87.
Meese, R., & Rogoff, K. (1983). Empirical Exchange Rates Models of the Seventies: Do They Fit Out of Sample?.
Journal of International Economics, 14, 3-24. http://dx.doi.org/10.1016/0022-1996(83)90017-X.
Menkhoff, L., Sarno, L., Schmeling, M., & Schrimpf, A. (2012). Carry Trades and Global Foreign Exchange Volatility.
Journal of Finance, 67(2), 681-718. http://dx.doi.org/10.1111/j.1540-6261.2012.01728.x.
Moosa, I. (2004). What Is Wrong with Market-Based Forecasting of Exchange Rates?. International Journal of
Business and Economics, 3(2), 107-121.
Moosa, I. (2008). Risk and Return in Carry Trade. Journal of Financial Transformation, 22(3), 8-13.
Moosa, I. (2010). The Profitability of Carry Trade. Economia Internazionale, 63, 261-380.
Moosa, I. (2013). Why Is It so Difficult to Outperform the Random Walk in Exchange Rate Forecasting?. Journal of
Applied Economics, 45, 3340-3346. http://dx.doi.org/10.1080/00036846.2012.709605.
Moosa, I., & Burns, K. (2012). Can Exchange Rate Models Outperform the Random Walk? Magnitude, Direction and
Profitability as Criteria. Journal of International Economics (Economia Internazionale), 65 (3), 473-490.
Moosa, I., & Halteh, P. (2012). The Profitability of Carry Trade Relative to a Forecasting-Based Strategy. Journal of
International Economics, 65(4), 605-621.
Neely, C., & Weller, P. (2013). Lessons from the evolution of foreign exchange trading strategies. Journal of Banking and
Finance, 37(10), 3783-3798. http://dx.doi.org/10.1016/j.jbankfin.2013.05.029
Schmidbauer, H., Rösch, A., Sezer, T., & Tunalıoglu, V. (2010). Currency Carry Trading with MGARCH Based
Carry-to-Risk Portfolio Optimization. Proceedings of the 30th International Symposium on Forecasting ISF2010,
San Diego, June 20-23.
Sy, M., & Tabarraei, H. (2009). Carry Trade and Return Crash Risk. Paris School of Economics, Working Paper.
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