Fx Strategies

Global FX Strategy JPMorgan Securities Ltd. London, August 8, 2008

Alternatives to standard carry and momentum in FX

7 4 .

• Carry and momentum are the most commonly followed trading strategies in currency markets, and probably in any asset class.

O

N :

• While standard approaches are profitable, they suffer several shortcomings. Carry incurs high drawdown, particularly when volatility rises. The drawdown on momentum strategies is also high, and returns are poor in range-trading markets. Minor modifications to each strategy can improve performance by better exploiting the interest rate/FX relationship.

S E I G E T A R T

• This paper proposes three such alternatives. (1) Forward Carry positions on changes in expected cash rate spreads rather than current cash rate differentials. (2) Forward Carry Overlay uses these spread movements to time entry into and exit from traditional carry car ry trades. (3) Forward Momentum Overlay uses Forward Carry to time standard spot momentum trades.

S T N E M T S E V N I

• As a stand-alone strategy, Forward Carry delivers similar risk-adjusted returns to standard carry strategies (IR of approximately 0.8) but offers two advantages. Returns are positively correlated with changes in volatility and the drawdown is 1/4th to 1/2 that of traditional approaches.

• Using Forward Carry as an overlay on standard carry and momentum baskets also reduces drawdown by half, hal f, and improves the performance of momentum models in range-trading markets.

• Recommendations from these models and performance statistics are reported regularly in the Investable the Investable Indices & Alpha Strategies section of JPMorgan’s FX Markets Weekly.

Simple enhancements to conventional approaches Carry and momentum are the most widely-followed widely-followed trading strategies in currency markets, and probably in any asset class. Indeed, these approaches inform the majority of rule-based investment styles outlined in the Investment the Investment Strategies series launched by JPMorgan in 2001 (see page 24). In currencies, the standard carry model positions on libor differentials (buying high vs low yielders), while the standard momentum model positions on recent spot trends (buying the currency which has appreciated). While still profitable, these strategies come with pitfalls. Carry suffers high drawdown, particularly when volatility rises. Momentum strategies perform better in high-volatility environment but still incur sizable drawdown. In addition, trend-following systems systems perform poorly when the dollar range trades.

The certifying analyst is indicated by an AC. See page 23 for analyst analyst certification and important legal and regulatory disclosures.

John NormandAC (44-20) 7325-5222 [email protected] J.P. Morgan Securities Ltd.

Kartikeya Ghia (44-20) 7325-9865 [email protected] J.P. Morgan Securities Ltd.

Contents Simple Simp le enhan enhanceme cements nts to conv conventi entional onal appro approache achess

1

Forward Carry

2

Forward Carry Overlay

5

Forward Momentum Overlay

7

Con clu si on s A p p e n d i x ta b l e s

www.morganmarkets.com

10 15

J.P. Morgan Securities Ltd.

Global FX Strategy

John Normand (44-20) 7325-5222 [email protected]

Alternatives to standard carry and momentum in FX August 8, 2008

This paper outlines three alternatives which improve performance without much additional additio nal complexity.

Chart 1: Correlaton between currency manager returns and carry strategies in G-10 and emerging markets Manager returns based on Barclay Currency Trader Index, monthly data, rolling 12-mo window

• Forward Carry trades currencies based on changes in

1.0

expected cash rates rather than on current cash rate differentials;

0.8 0.5

• Forward Carry Overlay uses Forward Carry to time the

0.3

entry into and exit from standard carry strategies; and

0.0

• Forward Momentum Overlay uses Forward Carry to time standard spot momentum trades.

-0.3

correlation with G10 carry

-0.5

correlation with EM carry

-0.8

As suggested by the initial descriptions, all strategies modify traditional approaches by incorporating rate expectations. Some of these strategies such as Forward Carry and Forward Carry Overlay were introduced in earlier JPMorgan research1 and are refined in this paper. Forward Momentum Overlay draws on previous Investment previous Investment Strate gies papers on momentum trading in FX, fixed income and commodities (see page 24). Backtesting for all models follows common guidelines:

• Results are calculated over the longest available sample period, which is 1992 based on daily datasets captured in JPMorgan’s Dataquery and on Bloomberg. We divide the dataset into two parts – parameter estimation is carried out during the in-sample period (1992 - 2006), with 2007- Q2 2008 used to test out-of-sample performance. Figures are also reported for the first and second half of the in-sample period (1992 - 98 and 1998 - 2006) as an addition al robustness check.

• Strategy returns are carry-adjusted based on 1-mo libor rates, such that the investor pays or earns the funding rate on all trades.

• Returns include transaction costs, which are calculated as the average bid-ask spread each year of the sample period. These adjustments range from 0.02% per trade for USD/ JPY to 0.05% on NZD/USD. Given the lack of daily bidoffer data during the early 1990s, we backfill those years using 1995 spreads as a proxy.

• Since signals are based on end-of-day data, trades are assumed to be executed the following day. 1 . See JPMorgan’s FX Barometer , J. Normand, Sep 2004 and Currencies in 2008: Is there anything but carry?,, J. Normand, Nov 2007, both available on www.morganmarkets.com. carry?

2

98

00

02

04

06

08

Source: JPMorgan

• All models include robustness checks around alternative parameters to the baseline model. These results are included in the main text and in the Appendix tables on page 15-19.

• Returns are reported for three currency blocs: (1) USD pairs, (2) Most-liquid G-10 pairs and (3) Major G-10 USD/CHF, pairs. The USD pairs are EUR/USD, GBP/USD, USD/CHF, USD/NOK, USD/SEK, USD/CAD, AUD/USD, NZD/USD & USD/JPY U SD/JPY.. Most-liquid G-10 pairs are the previous nine USD pairs plus the th e five euro crosses cro sses of EUR/GBP, EUR/ CHF,, EUR/SEK, EUR/NOK and EUR/JPY CHF EUR/JPY.. Major G-10 pairs are the 14 Most-liquid G-10 pairs plus other commonly-traded monly-trad ed crosses such as AUD/CAD, CAD/JPY, CAD/JPY, NOK/SEK, NOK/JPY NOK/JPY,, GBP/JPY, AUD/NZD, AUD/JPY and NZD/JPY (22 pairs in total).

More detailed results by individual currency are available on request.

1. Forward Carry Judging from its correlation with currency manager returns, the carry trade has been the industry’s mainstay for the past five years, first in G-10 pairs and more recently rec ently in emerging markets (chart 1). Carry’s popularity stems from three sources: the trading rule is simple (buy high vs lowyielders); the inefficiency is persistent (the forward rate bias); and the returns have been high over the past decade (return-to-risk of 0.8 to 1.2 on G-10 and emerging mar kets baskets). Yet Yet the negative returns on G-10 carry since mid2007 and on emerging markets baskets in early 2008 also


Global FX Strategy

Kartikeya Ghia (44-20) 7325-9865 [email protected]


highlight the strategy’s chief pitfall: returns tend to move inversely with volatility (chart 2), so are vulnerable to cyclical and policy shocks such as recession and central bank surprises. Drawdown is often significant, at some 20% for a typical carry basket.

Chart 2. Returns on standard carry strategy vs FX volatility, 2006 - 08

Intuition: FX responds to rate changes as much as rate levels Regardless of its long-term profitability, trading currencies only on carry ignores the other link between interest rates and FX: currencies respond as much to changes in rates as they do to current rate differentials. Rising rates relative to the rest of the world usually signals cyclical strength which draws capital inflows and sponsors currency appreciation, even for low-yielders. When rate spreads are stable, the traditional carry dynamic tends to dominate, leading high yielders to appreciate versus low-yielders.

based on (1) annual returns for carry basket using Most-liquid G-10 pairs and (2) annual changes in G-10 implied volatility as measured by JPMorgan VXY t 10% e k s a 8% b y r r 6% a C d r 4% a d n a 2% t S n o 0% n r u t -2% e r l a u -4% n n A

-6% -4

-2

0

2

4

6

Annual change in FX vol (VXY) Source: JPMorgan

There are numerous examples of this interplay in practice throughout the G-102.USD/JPY’s appreciation from mid-2006 to mid-2007, even as US - Japan libor spreads were stable (chart 3), illustrated the dominance of carry. The dollar’s subsequent fall from mid-2007 to Q1 2008 as spreads narrowed, even as the US remained the high-yielder, highlighted the dominance of spread changes. EUR/USD has responded exclusively to spread changes over the past few years; rising versus the dollar since 2006 as spreads narrowed, even though the US retained a rate advantage until early 2008 (chart 4).

Chart 3: USD/JPY vs US - Japanese libor differentials

Strategy: trade FX in the direction of spread changes A simple strategy to capture this spread dynamic would be to buy the currency in whose favor rates had moved over some defined period, and to sell the currency when that rate momentum reversed. This rule would apply regardless of whether the focus currency were a high or a low-yielder, since funding currencies can rally as their yield deficit diminishes. Since the strategy is based on the expected cash rate differential, we call this approach Forward Carry, a term introduced in the 2004 publication JPMorgan’s FX Barometer (J. Normand, Sep 2004).

-700

0

80

-100

90

-200

100

-300

110

-400

120

-500 USD/JPY -600

Japan - US libor rates, bp

130 140 150

98

99

01

03

05

07

Source: JPMorgan

Chart 4: EUR/USD vs Euro - US libor differentials 200

1.8

Euro - US libor rates, bp

1.6

90

1.4 -20 1.2 -130 1 -240

0.8

EUR/USD 2

By contrast in emerging markets, rising rates relative to core markets typically represents a risk premium for macroeconomic instability. Hence this trading rule generates decent returns (IR of 0.5 to 1) over long sample periods for currencies where the policy framework has converged towards G-10 (Taiwan, Poland, Mexico), but poor results (negative IR) for countries exhibiting less convergence (South Africa, Hungary, Turkey).

-350

0.6 98

99

01

03

05

07

Source: JPMorgan

3


Global FX Strategy



The model involves three parameters: • The reference interest rate is used to capture monetary policy expectations over the near term. We test three tenors: 1-month rates 1 month forward, 1-month rates 3 months forward and 3-month rates 3 months forward.

Table 1. Performance of Forward Carry, 1992 - 2008 Sensitivity to FX volatility measured as correlation of strategy return with change in JPMorgan’s VXY index of G-10 3-mo implied vol. Drawdown defined as deviation of index from its historic level high. Forward carry Standard carry basket: carry-torisk (USD pairs)

• The lookback period is the interval over which the change in spread is measured. We test four possibilities: the change over the past 1, 3, 6 and 12 months. In general, shorter lookback periods generate stronger results.

• The rebalancing frequency indicates how often the change in spreads is calculated and trades executed – daily, weekly or monthly. Given transaction costs, the choice of rebalancing frequency involves a tradeoff between greater nimbleness/high turnover costs (daily) and less flexibility/lower transaction costs (monthly model).

For comparison, Table 1 also presents returns for the standard carry strategy of simply buying high-yielders versus low yielders. JPMorgan’s carry methodology differs slightly from the conventional approach in that we rank currencies by their carry-to-risk ratio (libor differential divided by annualised spot FX vol) and hold a basket of the top four pairs, rebalancing monthly (see JPMorgan’s FX Barometer , Sep 2004). As is the case with carry models in other asset markets, returns on the FX carry strategy have tended to move inversely with volatility. This is illustrated by the negative correlation between the standard carry basket and aggregate FX volatility as measured by JPMorgan’s VXY (see Introducing the JPMorgan VXY & EM-VXY , J. Normand and A. Sandilya, Dec 2006). 4

Most-liquid G-10 pairs

Major G-10 pairs

1992 - 2006 (full sample) Annualised return

5.6%

4.0%

3.7%

4.3%

Volatility

6.9%

5.1%

3.3%

3.8%

IR

0.81

0.80

1.10

1.13

Correla tion with trade-weighted USD

-0.19

-0 .1 3

-0.21

-0.27

Correlation with VXY

-0.17

0.16

0.14

0.19

1992 - 1998 (1st half) Annualised return

2.2%

4.6%

4.3%

5.7%

Volatility

6.9%

5.5%

3.6%

3.9%

IR

0.31

0.84

1.20

1.47

Correlation with trade-weighted USD

0.25

0.24

0.23

0.18


-0.41

0.40

0.35

0.30

1999 - 2006 (2nd half) Annualised return

Performance: comparable returns, less drawdown As a baseline case, consider a daily model using changes in 1-month rates 3 months forward over a 1-month lookback period. Applied to a basket of nine USD pairs, the strategy has generated a return-to-risk near 0.80 net of transaction costs since 1992, with good consistency across sub-periods (1992 - 98, 1999 - 2006) and larger currency blocs (Mostliquid G-10 pairs and Major G-10 pairs). For all three blocs , performance has been particularly strong since 2007, highlighting the yield-centric nature of most currency movements over the past two years. Drawdown is only a quarter the size of the traditional carry strategy (-7% on Forward Carry) owing to the model’s ability to be long or short high-yield currencies depending on the direction of spread momentum.

USD pairs

8.7%

3.6%

3.2%

3.1%

Volatility

6.7%

5.1%

3.2%

3.5%

IR

1.28

0.70

0.97

0.88

Correla tion with trade-weighted USD

-0.18

-0 .1 1

-0.23

-0.41


0.14

0.03

0.05

0.19

2007 - Q2 2008 (out of sample) Annualised return

-0.1%

13.2%

9.0%

7.9%

Volatility

5.2%

6.0%

4.2%

4.2%

IR

-0.01

2.21

2.17

1.89


0.06

-0.74

-0.92

-0.69


-0.13

0.60

0.84

0.50

# transactions per month

0.5

15

25

39

5.3%

4.1%

3.0%

3.3%

Max loss (monthly)

-6.5%

-4.1%

-2.8%

-2.0%

Max drawdown

-17.6%

-7.0%

-4.4%

-5.8%

Max gain (monthly)

Source: JPMorgan

Chart 5. Foward Carry vs Standard Carry: excess returns index index level, calculated for basket of Most-liquid G-10 pairs 200 Standard Carry basket (carry-to-risk)

180 160

Forward Carry

140 120 100 80 92 Source: JPMorgan

94

97

00

02

05

08


Global FX Strategy



By contrast, all of the Forward Carry baskets tend to be positively correlated with implied volatility (Table 1 and chart 6), a characteristic which makes the approach an attractive complement to traditional carry baskets. This patterns stems from two sources. First, Forward Carry can be long or short the high-yielders, depending on recent movements in rates spreads. Second, rate spreads have more momentum when central bank uncertainty is high, a backdrop which is also bullish for FX vol. Consequently, maximum monthly losses and maximum drawdown are much lower with Forward Carry than with standard carry.

weekly or monthly rebalancing results in weaker performance (0.50 for USD pairs). Using longer lookback period s (3, 6 or 12 months also worsens performance (IR of 0.19 - 0.42 for USD pairs).

One constraint with Forward Carry is that it generates more transactions per month than a standard carry basket, which has very little turnover. For a basket of nine USD pairs, Forward Carry generates roughly 15 signals/trades per month, compared to an average of 1 trade per month with a standard carry model. (Turnover is low with standard models since pair selection is based on the rank-order of libor rates, which changes little from month to month). But even net of transaction costs, the strategy still generates high absolute and risk-adjusted returns. As a robustness check, Table 2 provides a heatmap indicating risk-adjusted performance as the three key parameters – reference interest rate, lookback period and rebalancing frequency – change. The baseline model is daily and trades off changes in 1-month rates 3 months forward over the past month. This strategy generates an IR of 0.97 (outlined in table 2). Using alternative interest rates with a daily model and 1-month lookback does not alter performance much for any of the three currency blocs. Moving from daily to Chart 6. Forward Carry vs Standard Carry: Correlation of annual returns with FX volatility based on (1) annual returns for on each strategy using Most-liquid G-10 pairs and (2) annual changes in G-10 vol as measured by level changes in JPMorgan VXY index. 1.0

Standard Carry

0.8

Forward Carry

2. Forward Carry Overlay Trying to time the entry into and exit from carry trades is nothing new. The traditional approach employs so-called risk appetite measures, which are composites – and often Table 2. Forward Carry performance using other reference interest rates, lookback periods and rebalancing frequencies Information ratios based on 1992 - 2008 sample period for the three currency blocs of USD pairs, Most-liquid G-10 pairs and Major G-10 pairs Reference interest rate

Rebalancing fre qu enc y

Look bac k period

IR

1 mo in 1 mo

Daily

1 mo

0.77

1 mo in 3 mos

Daily

1 mo

0.97

3 mos in 3 mos

Daily

1 mo

0.87

1 mo in 3 mos

Daily

1 mo

0.97

1 mo in 3 mos

Weekly

1 mo

0.50

1 mo in 3 mos

Monthly

1 mo

0.48

1 mo in 3 mos

Daily

1mo

0.97

1 mo in 3 mos

Daily

3mos

0.42

1 mo in 3 mos

Daily

6mos

0.19

1 mo in 3 mos

Daily

12mos

0.30

1 mo in 1 mo

Daily

1 mo

1.04

1 mo in 3 mos

Daily

1 mo

1.29

3 mos in 3 mos

Daily

1 mo

1.13

1 mo in 3 mos

Daily

1 mo

1.29

USD pairs


1 mo in 3 mos

Weekly

1 mo

0.43

1 mo in 3 mos

Monthly

1 mo

0.41

1 mo in 3 mos

Daily

1mo

1.29

1 mo in 3 mos

Daily

3mos

0.43

1 mo in 3 mos

Daily

6mos

0.28

1 mo in 3 mos

Daily

12mos

0.42

Major G-10 pairs

0.6

1 mo in 1 mo

Daily

1 mo

1.07

0.4

1 mo in 3 mos

Daily

1 mo

1.23

0.2

3 mos in 3 mos

Daily

1 mo

1.21

0.0

1 mo in 3 mos

Daily

1 mo

1.23

-0.2

1 mo in 3 mos

Weekly

1 mo

0.53

-0.4

1 mo in 3 mos

Monthly

1 mo

0.51

-0.6

1 mo in 3 mos

Daily

1mo

1.23

-0.8

1 mo in 3 mos

Daily

3mos

0.40

1 mo in 3 mos

Daily

6mos

0.28

1 mo in 3 mos

Daily

12mos

0.58

-1.0 95 Source: JPMorgan

98

01

04

07

Source: JPMorgan

5


Global FX Strategy



jumbles – of volatility, credit spreads and sometimes commodity prices. While good at characterizing current market sentiment — bullish (bearish) on carry when spreads and volatility are below (above) average — these indicators have two shortcomings. Firstly, they are often overfitted, including a factor associated with every previous carry trade unwind over the past decade (e.g. corporate distress, equity market collapse, an emerging markets default). Secondly, they can reverse frequently during market turbulence – imposing high turnover.

Chart 7. Constructing carry baskets with Forward Carry Overlay

Intuition: rate cycles are easier to model than crises Rather than model the last crisis to anticipate the next carry unwind, an alternative would be to focus on more regular cyclical shifts which undermine carry trades too. That is, if currencies show an empirical tendency to rise and fall with changes in spread momentum – the conclusion from the previous Forward Carry analysis – the riskiest carry trades are those which hold high-yielders where cyclical conditions are softening, the central bank easing and interest rates falling. The safest trades would be holding the high-yielders where the economy is accelerating and/or rates rising. Risks would be balanced in a high-yielder where rates are stable. If this intuition is correct, Forward Carry could provide a useful overlay to the standard carry framework. Strategy: condition carry trades on spread movements A simple application of this principle is to hold high yield currencies only where rates are rising, and to close exposure when rate spreads move against the investment currency. Consider the example outlined in chart 7. The standard approach to constructing carry baskets is to rank currency pairs each month by rate differential and to buy the topyielding currencies versus the lowest-yielding ones. Our carry-to-risk framework ranks currencies by risk-adjusted carry and invests only in those pairs offering a yield-to-vol ratio of 0.2 or higher, as we find that imposing some modest threshold for inclusion in the carry basket tends to generate stronger performance than imposing no threshold. Baskets typically hold four pairs for diversification and are rebalanced monthly, since the rank order of currency pairs does not change frequently. This process is illustrated in steps 1 - 4a of the diagram. Forward Carry Overlay adds an additional filter which only holds those currencies where spreads are widening in the high-yielder’s favour. If the pair lacks spread momentum, it is 6

Step 1 Rank all currency pairs in descending order of risk-adjusted carry (carry-to-risk ratio) Step 2 Eliminate pairs with carry-to-risk ratio < 0.2

Standard Carry

Forward Carry Overlay

Step 3a

Step 3b

Select top 4 pairs for inclusion in carry basket

For eligible pairs, calculate the direction of spread momentum on the day prior to rebalancing.

Step 4a Rebalance monthly

Step 4b If spread momentum moving against high-yielder, eliminate. Step 5b

Repeat until 4 eligible pairs identified. Invest equally in each. If < 4 pairs qualify, invest equally in those. Source: JPMorgan

dropped and the process continues until four pairs are identified. If four pairs do not qualify, the model equally allocates capital amongst those pairs which fulfill the criteria. This process is described in steps 1 - 5b. In its simplest form, the model applies the same forward carry rule developed in the previous section – the change in 1-month rates 3 months forward over the past month. This overlay could be applied daily or monthly, but given that Forward Carry performs better as a daily than a monthly model – even adjusted for higher transaction costs – the performance of the overlay model should follow a similar pattern.

Performance: higher returns, less drawdown Table 3 compares the performance of daily and monthly overlay models and that of the standard carry basket. During the in-sample period (1992 - 2006) the daily overla y model applied to the nine USD pairs generates comparable absolute returns (5.9% p.a.) but has a higher volatility and slightly lower risk-adjusted return (0.73 vs 0.81 on the standard basket). Higher volatility results from greater concentration risk with the overlay model, which sometimes invests in only one or two pairs if only that subset meets the inclusion criteria. However, since these pairs have more fundamental strength – they are high-yielders where rates are rising – the


Global FX Strategy



strategy’s return distribution avoids the negative skew of a standard carry basket’s returns. Thus, drawdown on the daily overlay model is half that of the standard carry basket (-8.1% vs -17.6%). Note that we can mitigate the concentration issue by expanding the universe of currencies from the nine USD pairs to the 14 Most-liquid G-10 pairs. On this broader basket, volatility is comparable to the standard carry strategy (5.7%), but absolute and risk-adjusted returns are higher (IR of 0.98). Drawdown is half that of the standard basket (-9.6%). Applying the overlay only once per month has mixed results. The overlay does not improve absolute or risk-adjusted performance for a basket of USD pairs, but it does lower drawdown meaningfully for the broader universe of Most-

liquid G-10 pairs (10.4% vs 20.8%). Daily and monthly overlay strategies also raise the return correlation with volatility, which makes them more conservative carry strategies. This characteristic also accounts for the underperformance of the overlay vs standard carry strategies during the second half of the sample period (1999 2006) when volatility was on a trend decline.

3. Forward Momentum Overlay Momentum is the empirical tendency of outperforming assets to outperform again in the future. In FX, the traditional approach trades in the direction of previous spot movements – buy the currency pair which has rallied – as determined by some filter or moving average rule. Despite the tendency to dismiss these frameworks as overly simplis-

Table 3. Performance of Forward Carry Overlay vs Standard Carry basket, 1992 - 2008 Sensitivity to FX volatility is correlation of strategy return with change in JPMorgan’s VXY index. Drawdown is deviation of index from its historic level high. USD pairs Standard Carry (carry-to-risk)


Standard Carry with Standard Carry with daily overlay monthly overlay

Standard Carry (carry-to-risk)

Standard Carry with Standard Carry with daily overlay monthly overlay

1992 - 2006 (full sample) Annualised return Volatility

5.6% 6.9%

5.9% 8.1%

5.0% 8.7%

4.4% 5.6%

5.6% 5.7%

5.5% 7.1%

IR

0.81

0.73

0.58

0.77

0.98

0.77


-0.19

-0.22

-0.07

-0.17

-0.11

-0.15


-0.17

0.13

0.13

-0.08

0.12

0.17


2.2%

6.6%

6.3%

2.1%

6.7%

6.2%

Volatility

6.9%

5.6%

6.4%

6.5%

3.2%

5.9%

IR

0.31

1.17

0.99

0.33

2.09

1.04


0.25

0.38

0.34

0.25

0.68

0.52


-0.41

0.00

-0.05

-0.13

-0.02

0.04

1999 - 2006 (2nd half) Annualised return Volatility

8.7% 6.7%

5.3% 10.2%

3.8% 10.6%

6.3% 4.7%

4.6% 7.4%

4.8% 8.3%

IR

1.28

0.52

0.36

1.33

0.63

0.58


-0.18

-0.55

-0.40

-0.41

-0.66

-0.65


0.14

0.29

0.32

0.14

0.21

0.30


-0.1%

9.2%

8.6%

-1.8%

4.9%

6.6%

Volatility

5.2%

9.7%

10.5%

5.3%

7.1%

8.1%

IR

-0.01

0.95

0.81

-0.34

0.69

0.81


0.06

-0.70

-0.59

0.86

0.22

0.09


-0.13

0.60

0.51

-0.93

-0.41

-0.27

0.5

2.9

0.9

1.0

6.0

2.0

5.3%

6.5%

6.5%

4.2%

5.3%

6.0%

Max loss (monthly)

-6.5%

-3.9%

-4.9%

-9.1%

-4.0%

-5.7%

Max drawdown

-17.6%

-8.1%

-17.1%

-20.8%

-9.6%

-10.4%

Transactions per month Max gain (monthly)

Source: JPMorgan

7


Global FX Strategy



tic, the profits from such a strategy have been decent. The simple moving average crossover rules proposed in our FX Barometer four years ago have generated a return-to-risk of 0.6 net of transaction costs over the past decade, with no deterioration in performance in the four years since the model was launched in 2004.3 This section proposes alternatives based on subsequent JPMorgan research on momentum strategies (see Investment Strategies list, page 24) and on principles from the Forward Carry model just outlined.

Chart 8. Foward Overlay vs standard carry: excess returns index

Intuition: investors consistently underreact, creating trends In principle, no trend-following strategy should generate consistent profitability if markets are efficient. However, even the strongest proponents of market efficiency acknowledge its limitations due to market segmentation (which impedes capital flows into mispriced markets) or behavioural biases (which impede instantaneous responses to new information). Momentum strategies benefit from two, welldocumented behavioural biases of underreaction and overreaction. Underreaction reflects investors inability or unwillingness to adjust views and positions quickly; either because they await fuller information to make a decision, or because they are reluctant to appear non-consensus. Accordingly, prices adjust slowly towards a market’s fundamental value, in the process producing short-term trends. Overreaction is also based on cognitive biases. Although most investors adjust their expectations fully, some extrapolate this positive news into the future, thus leading prices to overshoot fundamental value. Though both behavioural biases are well-known, they persist. As an example of underreaction, consider the manner in which economists adjust macro forecasts. Each month the Blue Chip survey publishes Wall Street economists’ consensus estimate on key macro variables such as growth, inflation and the Fed funds rate. Economists’ forecasts are reasonable proxies for investors’ views since both camps employ similar frameworks in their decision-making. If economists/investors indeed reacted instantaneously to new information (such as the impact of a credit crunch), their projections would move in a stepwise fashion, perhaps from a forecast of 3% growth this year to a forecast of 1% growth. 3. Following standard approaches, the crossover rule calculated a short-term moving average for each currency pair (between 5 and 60 days) and a long-term moving average (10 and 200 days). It also imposed a four-week filter rule which compared the daily close (London 5PM price) to spot’s four-week high and low. The trading rule was to open a position when two conditions are met: buy (sell) when 1) the shorter-term moving average is above (below) the longer-term moving average; and 2) the 4-week high (low) is exceeded.

8

index level, calculated for basket of Most-liquid G-10 pairs Standard Carry with daily Forward Carry Overlay

260

Standard Carry with monthly Forward Carry Overlay 200

Standard Carry

140

80 92

95

98

01

04

07

Source: JPMorgan

Chart 9. Forward Overlay vs standard carry: Correlation of annual returns with FX volatility based on (1) annual returns for each strategy using Most-liquid G-10 pairs and (2) annual changes in G-10 vol as measured byJPMorgan VXY 1.0

Standard Carry with daily Forward Carry Overlay

0.8

Standard Carry

0.6 0.4 0.2 0.0 -0.2 -0.4 -0.6 -0.8 -1.0 95

98

01

04

07

Source: JPMorgan

In practice, forecasts move incrementally – by roughly a quarter point per month over several months – before fully discounting the new cyclical scenario (chart 10). Serial correlation in forecast changes is so strong that a downgrade to growth forecasts this month has a 70% likelihood of being followed by another downgrade the following month (Table 4). An upgrade to growth forecasts have a 67% likelihood of being followed by a similar move the next month. Since incremental changes in views typically prompt incremental changes in positions, this tendency to underreact creates trends in asset markets. (For a fuller discussion of the relationship between expectational shifts on macro variables and asset markets, see Which Trade? Choosing tactical positions across asset classes, J. Normand, Jan 2004).


Global FX Strategy



The simple strategy Momentum in expectations provides the theoretical under pinning for trend-following strategies; the issue is how best to model it. Typical frameworks involve two parameters: the momentum measure (simple price momentum, exponentiallyweighted moving average) and the rebalancing frequency.

Chart 10: Monthly changes in consensus estimate for US growth in the year ahead, 1990 - 2007 0.5% 0.3% 0.1% -0.1%

• The momentum measure can be based on simple momentum, which calculates performance over a previous lookback period, or an exponentially-weighted moving average, which places more emphasis on recent observations. We test four lookback periods for simple price momentum (the past 1, 3, 6 and 12 months) and their equivalent decay factor for exponentially-weighted moving averages.

• The rebalancing frequency can be of any length – intraday, daily, weekly, monthly. For simplicity, and to fit the approach of most investment managers, we focus on daily, weekly and monthly models rather than on intra-day ones more common to algorithmic accounts. The underlying return series can be based on spot or total returns. Spot momentum is easiest to observe, so is mostwidely referenced in technical models. Total return momentum – spot return plus accrued carry over the same horizon – is not used by FX technicians but is commonly referenced in momentum models in other asset markets. The rationale is that high-performing assets often attract investor flows, thus extending trends. An FX momentum model which only tracks spot returns thus ignores a key driver of realised return – the accrued carry – which may motivate flows and additional spot gains. Applying these parameters to a basket of nine USD pairs suggests the following:

-0.3% -0.5% -0.7% 89

91

93

95

97

99

01

03

05

07

Source: JPMorgan, Blue Chip Economic Indicators monthly survey

Table 4: Conditional probabilities on consensus forecast revisions for US growth and inflation, 1990 - 2007 probability of forecast revision in period t+1 given change in period t Growth

Period t+1 Period t

Up

Down

Up

0.67

0.33

Down

0.31

0.69

Inflation

Period t+1 Period t

Up

Down

Up

0.65

0.35

Down

0.17

0.83

Source: JPMorgan, Blue Chip Economic Indicators monthly survey

most consistent performance to be based on medium-term trends (see Momentum in Commodities, Ribeiro, Loeys and Normand, September 2006).

• Momentum in total returns barely outperforms momen• Simple price momentum still generates a decent returnto-risk over various sample periods but mainly if based on medium-term trends (longer lookbacks). For example with spot momentum, IRs tend to rise with the lookback horizon for daily and monthly models, with the highest IR (0.60) realised for models based on price trends measured over the past year (table 5 and charts 11). There are notable exceptions such as the performance trend of the weekly model, but the generalisation still holds. This finding is consistent with previous JPMorgan research on momentum in commodities which found the strongest and

tum in spot returns for most combinations of rebalancing frequencies and lookback periods (chart 12 and table 6). The degree of outperformance is small but is consistent with the view that trends in total return are a better predictor of future performance, as they capture the attraction of carry for some investors.

• We find no evidence that exponentially-weighted moving averages outperform simple price momentum measures, whether applied to spot or total returns (performance tables available on request).

9


Global FX Strategy



Refinements on the simple strategy: Condition price momentum on rate momentum As refinements on the simple strategy, we examine three additional parameters: pair selectivity, weighting scheme and overlay.

by more due to lack of diversification, leading to a decline in IR to 0.5 (charts 13 and 14, Appendix tables 1 and 2). Performance improves, however, if we choose the top three or four pairs (IR of 0.7), suggesting some benefit from selectivity.

• Pair selectivity would trade only those currencies which

Results are similar whether we rank currencies by absolute or risk-adjust returns, and whether we use spot or total returns as the underlying series. Choosing top performers from even broader baskets of the 14 most-liquid G-10 pairs or the 22 major G-10 pairs also generates higher riskadjusted returns than trading the entire basket (Appendix tables 3 -6).

exhibit the most momentum, rather than all pairs within a give universe (nine USD pairs or 14 most-liquid G-10 pairs, for example). A simple rule would buy only the top one, two or three currencies based on their performance over the lookback period. Currencies could be ranked by absolute or risk-adjusted performance, the latter to avoid systematically choosing high-vol pairs.

• The is little evidence that optimised weights within a • Weighting scheme refers to capital allocation within the basket of chosen currencies. Equal capital allocation is the simplest approach, but previous JPMorgan research has found that using Markowitz (optimised) weights improves performance in commodities markets (see Optimizing Commodities Momentum, Ribeiro and di Pietro, April 2008).

• Overlay is a conditioning variable used to confirm an initial price momentum signal. In technical analysis, the overlay typically comes from either a filter rule (br eaking a new high or low) or shorter-term moving average (confirming a long-term trend), such as those used in the FX Barometer price momentum model. In this paper we explore a more fundamental overlay of Forward Carry. This trading rule would buy a currency only when it exhibits positive price momentum and when forward interest rate spreads are moving in the direction of the appreciating currency. The objective is to counterbalance the noise of price signals with fundamental information from the rates market. Applying these refinements to the nine USD pairs leads to several initial conclusions.

• Selectivity improves performance. As noted in the previous section, a decent baseline model trades currencies based on momentum over the past 12 months. Such a model applied to spot returns for the nine USD pairs and rebalanced daily or monthly generates a return-to-risk of close to 0.6 (tables 4 and 5). If we rank each pair from highest to lowest momentum and choose only the top one or two pairs, absolute performance rises but volatility rises

10

basket of top performers improves risk-adjusted returns. IRs are roughly comparable with both methods (figures available on request).

• Overlaying price momentum with rate spread momentum improves performance materially. The simple strategy of buying a pair only when it exhibits both positive price momentum and rate spread momentum – the Forward Carry principle – raises absolute returns, lowers volatility and raises risk-adjusted returns for almost all spot and total return momentum models (Appendix tables 7 and 8). As a summary, consider charts 15 and 16, which trace out the IR for various momentum models with and without the overlay. For a daily spot model, IRs rise fro m a range of 0 0.60 to a range of 0.50 to 0.96 (chart 15). For a daily total return model, IRs rise from a range of 0.12 to 0.61 to a range of 0.54 to 1.06 (chart 16).

• The overlay strategy also outperforms traditional momentum strategies in range-trading markets . During periods when the dollar is in a range – defined by a monthly move of +/-1% in the trade-weighted USD – a basic spot momentum strategy generates profits in only 54% of those months (chart 17). Using the overlay, which allows for no position-taking unless rate momentum confirms spot momentum, the percentage of profitable months during range-trading environments rises to 60% (chart 18).

Conclusions The enhancements discussed are hardly revolutionary. They are simple modifications to conventional approaches which leverage the other key relationship between FX and interest rates: that currencies respond as much to spread changes as


Global FX Strategy



Chart 11. Price momentum: Spot returns for USD pairs, 1990 - 2006

Chart 12. Price momentum: Total returns for USD pairs, 1990 - 2006 0.8

0.8

0.6

0.6 R I

0.4

R I

0.4

0.2 0.2

0.0

0.0

-0.2 1mo

3mos 6mos lookback period Spot, daily rebalance Spot, w eekly rebalance Spot, monthly rebalance

1mo

3mos

6mos

12mos

lookback period

12mos

Total return, daily rebalance Total return w eekly rebalance Total return, monthly rebalance

Chart 13. Best-of momentum baskets, USD pairs 1990 - 2006

Chart 14. Best-of momentum baskets, Most-liquid G-10 pairs 1990 - 2006

1.0

1.0

0.8

0.8 R I

0.6

0.6

R I

0.4

0.4

0.2

0.2 All

1

2

3

4

5

All

number of pairs Spot momentum (absolute) Spot momentum (v ol-adjusted) Total return momentum (absolute) Total return mom entum (v ol-adjusted)

1

2 3 number of pairs Spot momentum (abs olute) Spot momentum (v ol-adjusted)

4

5

Total return momentum (absolute) Total return momentum (v ol-adjusted)

Chart 15. Forward Momentum Overlay on spot returns, USD pairs, 1990 - 2006

Chart 16. Forward Momentum Overlay on total returns, USD pairs, 1990 - 2006

1.0

1.0

0.8

0.8 0.6

0.6

R I

R I

0.4

0.4

0.2

0.2

0.0

0.0 1mo

3mos 6mos lookback period Spot momentum (daily ) Spot momentum w ith ov erlay (daily) Spot momentum (w eekly) Spot momentum w ith overlay (w eekly)

12mos

-0.2

1mo

3mos

6mos

12mos

lookback period Total return momentum (daily) Total return momentum w ith overlay (daily ) Total return momentum (w eekly ) Total return momentum w ith overlay (weekly )

Source: JPMorgan

11


Global FX Strategy



) o m 2 1 , o m 6 , o m 3 , o m 1 ( s d o i r e p k c a b k o o l d n a ) y l h t n o m , y l k e e w , y l i a d ( s e i c n e u q e r f g n i c n a l a b e r s u o i r a v h t i w s r i a p D S U r o f s n r u t e r t o p s n o d e s a B : m u t n e m o m e c i r P . 5 e l b a T

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U ) e d l e p t h m g a i s e f w o e t d Y u a X o r n ( r t V 8 t u h t t h i i 0 e 0 r w w 2 d n n 2 e o o i s y t i t Q i t i a l a - l l a l i t e u 7 n l r e r a r r 0 n o o o 0 I C C 2 A V R

d o h t e e g a ) r m r e r e v v e a o t e g s n s m i o o r r v a o c B ( m X F

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h t n o ) m l y ) y r h l e t h t p n n n s o o w o n ( m m d ( o i t n s w c i a s a a o r s g l d n x x x a a a a r T M M M

n a g r o M P J : e c r u o S


Global FX Strategy



s o m 2 1

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n a g r o M P J : e c r u o S

13


Global FX Strategy



to spread levels, given the cyclical factors captured by spread moves. As a stand-alone strategy, Forward Carry’s chief advantages over carry is its lower drawdown and positive correlation with changes in volatility. These same advantages extend to its use as an overlay to basic carry and momentum models, both of which have traditionally relied on complicated risk-appetite filters or additional (and endogenous) price filters to time entry and exit.

Chart 17. Return on spot momentum vs moves in trade-weighted USD

Some of these strategies have been previewed in previous JPMorgan research, and this paper provides more comprehensive backtesting across a range of parameters and currency blocs. Recommendations from these models and performance statistics are reported regularly in the Investable Indices & Alpha Strategies section of JPMorgan’s FX Markets Weekly.

monthly data 8%

) s n r u t e r y l h t n o m ( l e d o m m u t n e m o m

6% 4% 2% 0% -2% -4% -6% -4%

-2%

0%

2%

4%

6%

Trade-w eighted USD (monthly returns) Source: JPMorgan

Chart 18. Returns on spot momentum with overlay vs moves in trade-weighted USD monthly data

8% ) s n r u t e r y l h t n o m ( l e d o m m u t n e m o m

6% 4% 2% 0% -2% -4% -6% -4%

-2%

0%

2%

4%

Trade-w eighted USD (monthly returns) Source: JPMorgan

Related publications on www.morganmarkets.com

JPMorgan Tradeable Currency Indices (TCIs), J. Normand July 2, 2007. Bloomberg ticker ALLX JPMQ Introducing the JPMorgan VXY™ & EM-VXY™ , J. Normand and A. Sandilya, Dec 11, 2006. Bloomberg tickers JPMVXYG7 and JPMVXYEM

14

6%


Global FX Strategy



Appendix tables Appendix Table 1. Baskets of top performers based on spot momentum for USD pairs 12-mo lookback, monthly rebalancing, equal weights on each pair Aggregate basket (9 USD pairs)

Absolute price momentum baskets (number of pairs shown in column) 1

2

3

4

5

Vol-adjusted price momentum baskets (number of pairs shown in column) 1

2

3

4

5

1992 - 2006 (full sample) Annualised return Volatility IR Correlation with trade-weighted USD Correlation with VXY

3.6% 6.4% 0.56 -0.09 0.41

5.3% 11.7% 0.46 -0.28 0.19

3.7% 7.5% 0.50 -0.35 0.34

4.2% 6.0% 0.69 -0.15 0.37

4.5% 6.3% 0.71 -0.05 0.43

4.5% 6.6% 0.67 -0.09 0.47

5.9% 10.4% 0.56 -0.20 0.19

5.3% 7.6% 0.70 -0.27 0.37

4.8% 6.6% 0.73 -0.13 0.41

4.0% 6.5% 0.61 -0.09 0.44

4.3% 7.0% 0.63 -0.07 0.46

1992 - 1998 (1st half) Annualised return Volatility IR Correlation with trade-weighted USD Correlation with VXY

3.7% 3.4% 1.10 0.25 0.60

4.7% 6.5% 0.71 -0.09 0.20

2.2% 4.2% 0.53 -0.07 0.39

3.3% 2.9% 1.16 0.06 0.48

5.0% 2.6% 1.94 0.09 0.56

4.8% 3.1% 1.57 0.12 0.69

6.6% 7.7% 0.86 -0.21 0.09

5.0% 4.9% 1.01 -0.07 0.42

4.9% 3.8% 1.29 0.12 0.58

4.4% 3.4% 1.29 0.11 0.64

4.7% 3.3% 1.45 0.09 0.72

1999 - 2006 (2nd half) Annualised return Volatility IR Correlation with trade-weighted USD Correlation with VXY

3.5% 8.5% 0.42 -0.20 0.45

5.9% 15.3% 0.39 -0.32 0.29

5.1% 9.6% 0.53 -0.34 0.49

4.9% 8.0% 0.62 -0.19 0.41

4.0% 8.6% 0.46 -0.18 0.43

4.1% 9.0% 0.46 -0.23 0.45

5.3% 12.9% 0.41 -0.28 0.34

5.5% 9.6% 0.57 -0.29 0.49

4.7% 8.6% 0.55 -0.16 0.44

3.6% 8.6% 0.42 -0.21 0.44

4.0% 9.4% 0.43 -0.23 0.43

2007 - Q2 2008 (out of s ample) Annualised return Volatility

8.3% 5.5%

-1.1% 12.1%

0.6% 11.1%

4.0% 9.9%

5.3% 8.3%

7.6% 7.5%

4.4% 12.1%

2.0% 9.5%

6.3% 7.2%

8.7% 7.3%

8.5% 7.3%

IR Correlation with trade-weighted USD Correlation with VXY

1.51 -0.95 0.90

-0.09 -0.44 0.28

0.07 -0.32 0.12

0.55 -0.53 0.33

0.73 -0.70 0.53

1.05 -0.80 0.66

0.37 -0.58 0.43

0.21 -0.35 0.15

0.87 -0.77 0.64

1.19 -0.87 0.76

1.17 -0.87 0.76

# transactions per month Max gain (monthly)

0.9 6.5%

0.4 7.2%

0.7 8.2%

0.8 7.3%

0.9 6.5%

1.0 7.4%

0.4 7.2%

0.7 6.9%

0.9 6.4%

1.0 6.4%

1.0 7.4%

-5.7% -10.9%

-9.9% -25.4%

-8.9% -16.7%

-8.9% -14.8%

-8.1% -15.0%

-6.2% -13.2%

-8.5% -20.5%

-6.5% -14.5%

-5.9% -14.1%

-6.2% -13.5%

-5.8% -14.9%

Max loss (monthly) Max drawdown

Appendix Table 2. Baskets of top performers based on total return momentum, USD pairs 12-mo lookback, monthly rebalancing, equal weights on each pair Aggregate basket (9 USD pairs)


2

3

4

5


2

3

4

5


3.7% 6.6% 0.56 -0.08 0.33

3.6% 10.2% 0.36 -0.31 0.10

4.4% 7.4% 0.60 -0.28 0.37

4.4% 5.7% 0.77 -0.12 0.31

4.8% 6.2% 0.76 -0.20 0.30

4.3% 6.6% 0.65 -0.15 0.35

6.3% 10.3% 0.61 -0.20 0.16

6.3% 7.0% 0.90 -0.19 0.20

5.3% 5.4% 0.99 -0.12 0.32

4.4% 6.8% 0.66 -0.17 0.35

4.6% 6.5% 0.71 -0.15 0.42


3.8% 3.3% 1.15 0.15 0.42

0.1% 5.7% 0.02 -0.13 0.03

2.4% 4.1% 0.58 0.00 0.40

3.7% 3.3% 1.13 0.09 0.37

4.0% 1.4% 2.94 -0.06 0.37

4.1% 2.2% 1.88 0.00 0.43

4.7% 8.7% 0.54 -0.31 0.02

4.6% 2.7% 1.69 -0.19 0.08

4.7% 2.5% 1.91 0.06 0.30

3.8% 3.2% 1.18 0.04 0.41

4.9% 2.0% 2.49 0.04 0.56


3.6% 8.9% 0.41 -0.22 0.41

6.8% 12.3% 0.55 -0.20 0.30

6.2% 9.2% 0.67 -0.28 0.53

5.1% 7.4% 0.68 -0.26 0.35

5.4% 8.7% 0.62 -0.24 0.40

4.5% 9.1% 0.49 -0.24 0.42

7.7% 11.9% 0.65 -0.01 0.43

7.8% 9.2% 0.86 -0.03 0.44

5.9% 7.2% 0.81 -0.12 0.47

5.0% 9.0% 0.55 -0.30 0.42

4.4% 9.0% 0.48 -0.30 0.44

2007 - Q2 2008 (out of s ample) Annualised return Volatility

8.2% 5.4%

0.5% 12.0%

4.6% 10.3%

4.9% 9.7%

8.0% 8.1%

8.6% 7.2%

7.8% 11.8%

2.6% 8.7%

5.8% 7.1%

10.3% 7.1%

9.4% 7.2%


1.51 -0.94 0.89

0.05 -0.23 0.07

0.53 -0.44 0.25

0.69 -0.42 0.22

1.14 -0.63 0.45

1.20 -0.80 0.66

0.66 -0.68 0.55

0.30 -0.06 -0.13

0.81 -0.59 0.42

1.46 -0.73 0.59

1.30 -0.82 0.69

0.9 6.5% -5.7% -12.2%

0.4 7.2% -15.3% -22.8%

0.7 8.2% -9.1% -17.3%

0.8 7.3% -8.9% -16.0%

0.9 6.5% -8.1% -13.8%

1.0 6.4% -6.2% -13.8%

0.4 7.1% -8.5% -19.2%

0.7 6.9% -7.9% -13.7%

0.8 6.4% -5.6% -12.6%

0.9 6.4% -6.6% -13.6%

1.0 6.4% -6.7% -11.8%

# transactions per months Max gain (monthly) Max loss (monthly) Max drawdown Source: JPMorgan

15


Global FX Strategy



Appendix tables Appendix Table 3. Baskets of top performers based on spot momentum, 14 Most-liquid G-10 pairs 12-mo lookback, monthly rebalancing, equal weights on each pair Aggregate basket (14 mostliquid G-10 pairs)


2

3

4

Vol-adjusted price momentum baskets (number of pairs shown in column)

5

1

2

3

4

5


2.6% 4.2% 0.61 -0.06 0.37

5.5% 11.7% 0.47 -0.15 0.33

4.4% 7.9% 0.56 -0.28 0.44

3.9% 6.0% 0.65 -0.15 0.35

4.1% 6.1% 0.67 -0.08 0.36

4.6% 6.1% 0.75 -0.05 0.39

4.9% 10.7% 0.46 -0.08 0.50

4.7% 7.9% 0.59 -0.26 0.39

4.6% 6.5% 0.70 -0.14 0.35

3.8% 5.7% 0.67 -0.06 0.33

4.0% 5.7% 0.70 -0.08 0.39


3.1% 1.8% 1.72 0.06 0.43

3.6% 7.4% 0.48 0.17 0.43

3.3% 3.5% 0.95 0.05 0.54

2.9% 3.3% 0.86 0.07 0.41

3.8% 3.6% 1.07 0.14 0.48

4.7% 3.8% 1.23 0.09 0.52

4.5% 6.3% 0.72 0.11 0.78

4.1% 4.0% 1.04 -0.15 0.45

4.4% 2.8% 1.55 -0.07 0.49

4.4% 1.5% 2.90 -0.10 0.47

5.0% 2.4% 2.11 0.03 0.56


2.1% 5.7% 0.36 -0.20 0.48

7.2% 14.7% 0.49 -0.15 0.37

5.4% 10.6% 0.51 -0.38 0.48

4.7% 7.7% 0.61 -0.22 0.42

4.4% 8.0% 0.55 -0.25 0.37

4.5% 7 .9% 0.57 -0.20 0.41

5.3% 14.0% 0.38 -0.01 0.48

5.2% 10.5% 0.50 -0.27 0.50

4.7% 8.8% 0.54 -0.17 0.38

3.3% 7 .9% 0.42 -0.14 0.32

3.1% 7.6% 0.40 -0.23 0.37

2007 - Q2 2008 (out of sample) Annualised return Volatility IR Correlation with trade-weighted USD Correlation with VXY

5.1% 4.0% 1.26 -0.93 0.87

-1.1% 12.1% -0.10 -0.49 0.34

1.4% 10.9% 0.16 -0.35 0.16

3.6% 9.9% 0.52 -0.34 0.12

4.8% 8.4% 0.72 -0.51 0.31

7.4% 7.6% 1.13 -0.77 0.63

2.2% 11.2% 0.20 -0.45 0.28

3.5% 8.9% 0.39 -0.53 0.36

5.9% 6.9% 0.87 -0.71 0.55

7.6% 6.7% 1.13 -0.77 0.65

9.1% 6.6% 1.39 -0.84 0.73

# transactions per months Max gain (monthly) Max loss (monthly) Max drawdown

1.7 4.1% -3.9% -8.5%

0.4 7.9% -9.9% -25.3%

0.7 8.2% -8.9% -17.8%

0.9 7.3% -8.6% -14.8%

1.0 6.5% -10.4% -17.2%

1.1 7.4% -8.2% -15.8%

0.5 7.9% -7.7% -27.3%

0.8 6.4% -6.5% -17.2%

1.0 5.4% -5.9% -13.9%

1.2 6.4% -5.9% -14.4%

1.3 5.8% -5.7% -13.8%

Appendix Table 4. Baskets of top performers based on total return momentum, 14 Most-liquid G-10 pairs 14 Most-liquid G-10 pairs, 12-mo lookback, monthly rebalancing, equal weights on each pair Aggregate basket (14 mostliquid G-10 pairs)


2

3

4

5


2

3

4

5


2.7% 4.3% 0.62 -0.03 0.28

3.9% 10.7% 0.36 -0.19 0.27

5.0% 8.1% 0.61 -0.22 0.28

4.9% 6.2% 0.79 -0.11 0.29

4.5% 6.2% 0.72 -0.10 0.33

4.3% 6.0% 0.72 -0.12 0.30

4.7% 10.7% 0.44 -0.21 0.52

4.8% 6.9% 0.70 -0.25 0.31

5.2% 5.9% 0.87 -0.08 0.25

4.6% 5.7% 0.80 -0.08 0.25

4.4% 5.4% 0.82 -0.06 0.30


2.9% 2.4% 1.18 0.05 0.22

0.9% 7.2% 0.12 0.17 0.38

2.7% 4.8% 0.57 0.11 0.27

3.4% 5.1% 0.66 0.09 0.30

3.3% 4.5% 0.73 0.18 0.36

4.2% 3.1% 1.36 0.04 0.31

3.9% 5.9% 0.66 0.04 0.76

3.7% 1.6% 2.41 -0.12 0.38

5.1% 1.5% 3.49 -0.08 0.38

4.1% 3.4% 1.23 -0.05 0.25

4.0% 3.0% 1.33 0.05 0.33


2.5% 5.7% 0.44 -0.18 0.42

6.6% 12.8% 0.52 -0.13 0.33

7.0% 10.0% 0.70 -0.24 0.44

6.3% 7.1% 0.89 -0.19 0.35

5.5% 7.5% 0.73 -0.23 0.39

4.4% 8.0% 0.55 -0.29 0.38

5.4% 14.0% 0.39 -0.26 0.49

5.8% 9.5% 0.61 -0.22 0.44

5.3% 8.3% 0.64 -0.07 0.31

4.9% 7.4% 0.66 -0.06 0.36

4.8% 7.0% 0.68 -0.14 0.37


5.5% 3.7% 1.46 -0.93 0.87

0.9% 12.0% 0.08 -0.35 0.19

3.3% 10.1% 0.40 -0.24 0.04

5.3% 9.9% 0.84 -0.31 0.10

7.5% 8.7% 1.18 -0.58 0.38

8.8% 7.2% 1.34 -0.66 0.48

5.6% 10.9% 0.51 0.15 -0.37

1.4% 8.3% 0.17 0.08 -0.28

4.7% 6.3% 0.74 -0.33 0.15

6.4% 6.4% 1.00 -0.57 0.41

7.9% 6.6% 1.20 -0.72 0.57

0.4 7.9% -8.4% -22.2%

0.7 8.2% -7.9% -16.1%

# transactions per months Max gain (monthly) Max loss (monthly) Max drawdown Source: JPMorgan

16

1.6 4.6% -3.9% -10.7%

0.8 7.3% -8.6% -15.5%

0.9 6.5% -10.4% -16.7%

1.1 6.4% -7.9% -15.9%

0.4 7.1% -7.9% -22.1%

0.7 5.9% -7.9% -14.0%

1.0 5.4% -5.6% -11.8%

1.2 6.4% -6.6% -13.5%

1.1 5.8% -6.7% -12.5%


Global FX Strategy



Appendix tables Appendix Table 5. Baskets of top performers based on spot momentum, 22 Major G-10 pairs 12-mo lookback, monthly rebalancing, equal weights on each pair Aggregate basket (22 major G-10 pairs)


2

3

4

5


2

3

4

5


2.5% 3.3% 0.75 -0.04 0.17

5.5% 9.8% 0.56 -0.14 0.21

4.9% 9.1% 0.54 -0.06 0.22

3.7% 8.5% 0.44 -0.05 0.19

4.5% 7.4% 0.60 0.01 0.23

4.9% 7.1% 0.68 0.01 0.22

6.5% 9.4% 0.69 0.12 0.39

4.7% 8.1% 0.58 0.15 0.40

5.3% 7.5% 0.70 -0.01 0.35

5.5% 5.9% 0.93 0.04 0.28

4.9% 5.6% 0.87 0.03 0.26


2.6% 3.1% 0.84 -0.07 0.04

4.9% 11.0% 0.45 -0.05 0.11

6.4% 9.1% 0.70 -0.25 0.07

3.5% 8.7% 0.40 -0.13 0.08

5.0% 6.8% 0.73 -0.10 0.14

5.5% 6.0% 0.91 -0.10 0.13

7.8% 7.5% 1.03 0.01 0.35

7.0% 4.1% 1.70 -0.12 0.40

6.0% 5.7% 1.06 -0.15 0.30

6.1% 4.8% 1.26 -0.20 0.23

4.8% 3.9% 1.21 -0.12 0.29


2.4% 3.7% 0.64 -0.09 0.35

6.0% 9.5% 0.63 -0.15 0.43

3.6% 9.5% 0.38 -0.19 0.43

4.0% 9.0% 0.44 -0.12 0.35

4.1% 8.4% 0.49 -0.06 0.37

4.3% 8.4% 0.52 -0.02 0.35

5.4% 11.2% 0.48 0.17 0.51

2.7% 10.4% 0.26 0.01 0.47

4.6% 9.2% 0.50 -0.05 0.46

5.0% 7.1% 0.71 0.05 0.38

5.0% 7.1% 0.71 0.11 0.31


2.3% 5.0% 0.46 -0.65 0.47

-0.4% 14.1% -0.03 0.21 -0.39

0.7% 13.2% 0.07 -0.36 0.20

2.2% 12.3% 0.24 -0.27 0.07

3.9% 11.0% 0.45 -0.62 0.45

4.5% 10.4% 0.56 -0.55 0.35

4.6% 13.3% 0.35 -0.65 0.53

1.1% 10.9% 0.10 -0.29 0.13

3.5% 9.0% 0.39 -0.59 0.44

4.4% 8.7% 0.50 -0.35 0.18

7.4% 8.1% 0.92 -0.73 0.59


2.7 -4.3% 3.3% -8.5%

0.5 -11.5% 9.8% -27.7%

0.8 -8.7% 8.3% -19.2%

1.0 -8.8% 7.3% -23.4%

1.2 -10.7% 8.5% -19.5%

1.4 -7.4% 7.5% -16.2%

0.4 -7.7% 9.8% -22.1%

0.8 -8.7% 6.3% -26.9%

1.1 -7.1% 6.2% -18.6%

1.3 -7.0% 6.4% -15.8%

1.5 -6.6% 5.8% -15.5%

Appendix Table 6. Baskets of top performers based on total return momentum, 22 Major G-10 pairs 22 Major G-10 pairs, 12-mo lookback, monthly rebalancing, equal weights on each pair Aggregate basket (22 major G-10 pairs)


2

3

4

5


2

3

4

5


2.8% 3.6% 0.79 -0.04 0.08

5.7% 8.0% 0.72 -0.10 0.10

5.1% 8.1% 0.62 -0.05 0.13

4.6% 7.5% 0.62 -0.01 0.10

5.0% 7.4% 0.68 0.08 0.08

5.3% 7.4% 0.72 0.08 0.05

5.9% 9.3% 0.63 -0.01 0.36

5.3% 7.5% 0.71 0.11 0.32

5.1% 6.5% 0.79 -0.03 0.28

6.0% 5.2% 1.16 0.00 0.21

4.5% 5.7% 0.80 0.00 0.15


2.5% 3.5% 0.70 -0.02 -0.15

4.9% 7.9% 0.62 -0.03 -0.06

4.8% 9.1% 0.52 -0.10 -0.06

4.0% 8.0% 0.50 -0.06 -0.06

4.1% 7.7% 0.54 0.15 -0.09

4.4% 7.6% 0.57 0.11 -0.10

5.2% 8.1% 0.63 0.05 0.37

6.9% 5.7% 1.21 -0.19 0.28

5.2% 4.3% 1.21 -0.13 0.32

6.1% 4.5% 1.35 -0.19 0.18

4.6% 4.4% 1.03 -0.14 0.07


3.2% 3.8% 0.83 -0.11 0.30

6.5% 8.6% 0.75 -0.13 0.34

5.3% 7.8% 0.68 -0.11 0.37

5.2% 7.5% 0.70 -0.05 0.28

5.8% 7.7% 0.76 0.04 0.24

6.1% 7.6% 0.81 0.05 0.20

6.5% 10.8% 0.61 0.05 0.45

3.9% 9.0% 0.44 -0.05 0.38

5.0% 8.2% 0.61 -0.15 0.31

5.9% 6.0% 0.99 -0.01 0.29

4.5% 6.9% 0.65 -0.06 0.27


3.5% 4.8% 0.73 -0.58 0.40

0.1% 14.2% 0.00 0.61 -0.76

3.5% 12.6% 0.31 -0.12 -0.09

3.5% 12.5% 0.40 -0.04 -0.17

6.0% 11.5% 0.68 -0.16 -0.07

6.4% 10.5% 0.80 -0.16 -0.08

7.9% 14.3% 0.55 -0.45 0.29

1.8% 11.4% 0.16 0.20 -0.39

2.6% 8.7% 0.30 0.38 -0.57

4.1% 8.8% 0.47 0.40 -0.59

6.7% 8.0% 0.84 -0.32 0.12

1.6 -9.7% 9.8% -18.2%

3.0 -8.7% 8.2% -21.8%

4.1 -9.3% 7.3% - 25.5%

4.8 -10.7% 6.5% -24.2%

5.4 -11.8% 6.3% -22.2%

0.4 -9.7% 7.5% -17.9%

0.8 -8.7% 6.3% - 17.7%


2.5 -4.2% 3.3% -11.0%

1.1 -7.1% 6.2% -14.4%

1.3 -7.0% 5.9% - 13.4%

1.6 -6.6% 5.9% -15.4%

Source: JPMorgan

17


Global FX Strategy



Appendix charts Appendix Chart 1. Annual returns on Standard Carry, USD pairs and Most-liquid G-10 pairs 30%

Appendix Chart 2. Annual returns on Forward Carry, USD pairs and Most-liquid G-10 pairs 20%

25%

USD pairs

20%


USD pairs Most-liquid G-10 pairs

15%

15%

10%

10%

5%

5% 0%

0%

-5%

-5%

-10% -15%

-10% 92

94

96

98

00

02

04

06

08

Appendix Chart 3. Annual returns on Forward Carry Overlay (daily model), USD pairs and Most-liquid G-10 pairs 30%

30%

20%

20%

15%

15%

10%

10%

5%

5%

0%

0%

-5%

-5%

-10%

-10% 94

96

98

00

02

04

06

98

00

02

04

06

08


92

08

Appendix Chart 5. Annual returns on spot momentum, 12-mo lookback, daily model, USD pairs and Most-liquid G-10 pairs 20%

96

USD pairs

25%


92

94

Appendix Chart 4. Annual returns on Forward Carry Overlay (monthly model), USD pairs and Most-liquid G-10 pairs

USD pairs

25%

92

94

96

98

00

02

04

06

08

Appendix Chart 6. Annual returns on spot momentum with overlay, 12-mo lookback, daily model, USD pairs and Most-liquid G-10 pairs

US pairs

12%

US pairs


10%


15%

8% 10%

6% 4%

5%

2%

0%

0% -5%

-2% -4%

-10% 92

94

96

98

00

02

04

06

Appendix Chart 7. Annual returns on total return momentum, 12-mo lookback, daily model, USD pairs and Most-liquid G-10 pairs US pairs

20%

92

08

Most-liquid G-10 pairs 15%

94

96

98

00

02

04

06

08

Appendix Chart 8. Annual returns on total return momentum with overlay, 12-mo lookback, daily model, USD pairs and Most-liquid G-10 pairs 12%

US pairs

10%


8% 10%

6%

5%

4% 2%

0%

0% -5%

-2%

-10%

-4% 92

20

94

96

98

00

02

04

06

08

92

94

96

98

00

02

04

06

08

Source: JPMorgan


Global FX Strategy



Appendix Table 9. Model summary for USD pairs Forward Carry models

Carry model

Standard carry

Forward Carry

Forward Carry Overlay, daily

Momentum models Forward Carry Overlay, monthly

Spot momentum (12-mo Spot momentum (12-mo lookback, daily lookback, monthly rebalance) rebalance)

Best-of basket (top 3 Forward Momentum Forward Momentum pairs, 12-mo lookback, Overlay (12-mo lookback, Overlay (12-mo lookback, monthly rebalance) daily rebalance) monthly rebalance)


5.6%

Volatility

6.9%


4.0%

5.9%

5.0%

3.9%

3.6%

4.2%

3.8%

3.3%

5.1%

8.1%

8.7%

6.7%

6.4%

6.0%

4.0%

5.0%

0.81

0.80

0.73

0.58

0.59

0.56

0.69

0.96

0.66

-0.19

-0.13

-0.22

-0.07

-0.04

-0.09

-0.15

-0.05

0.04

-0.17

0.16

0.13

0.13

0.39

0.41

0.37

0.47

0.62


2.2%

4.6%

6.6%

6.3%

4.6%

3.7%

3.3%

4.4%

5.2%

Volatility

6.9%

5.5%

5.6%

6.4%

3.7%

3.4%

2.9%

3.5%

4.8%

IR

0.31

0.84

1.17

0.99

1.25

1.10

1.16

1.27

1.09


0.25

0.24

0.38

0.34

0.13

0.25

0.06

0.20

0.29


-0.41

0.40

0.00

-0.05

0.57

0.60

0.48

0.55

0.75


8.7%

Volatility

6.7%


3.6%

5.3%

3.8%

3.3%

3.5%

4.9%

3.3%

1.7%

5.1%

10.2%

10.6%

8.7%

8.5%

8.0%

4.6%

4.9%

1.28

0.70

0.52

0.36

0.37

0.42

0.62

0.73

0.34

-0.18

-0.11

-0.55

-0.40

-0.18

-0.20

-0.19

-0.31

-0.47

0.14

0.03

0.29

0.32

0.38

0.45

0.41

0.39

0.55


-0.1%

13.2%

5.2%

IR

-0.01

2.21


0.06

-0.74


-0.13

0.60

0.60

# transactions per month Max loss (monthly)

6.0%

9.2%

Volatility

8.6%

9.7%

6.6%

8.3%

4.0%

9.5%

7.3%

10.5%

6.8%

5.5%

9.9%

5.9%

4.4%

0.95

0.81

0.98

1.51

0.55

1.62

1.64

-0.70

-0.59

-0.92

-0.95

-0.53

-0.94

-0.94

0.51

0.84

0.90

0.33

0.88

0.90

0.5

15

2.9

0.9

4.4

0.9

0.8

9.4

0.9

5.3%

4.1%

6.5%

6.5%

6.4%

6.5%

7.3%

3.9%

5.3%

Max gain (monthly)

-6.5%

-4.1%

-3.9%

-4.9%

-6.0%

-5.7%

-8.9%

-2.6%

-3.0%

Max drawdown

-17.6%

-7.0%

-8.1%

-17.1%

-15.9%

-10.9%

-14.8%

-6.0%

-5.7%

Appendix Table 10. Model summary for Most-liquid G-10 pairs Forward Carry models

Carry model

Standard carry

Forward Carry

Forward Carry Overlay, daily

Momentum models Forward Carry Overlay, monthly

Spot momentum (12-mo Spot momentum (12-mo lookback, daily lookback, monthly rebalance) rebalance)

Best-of basket (top 3 Forward Momentum Forward Momentum pairs, 12-mo lookback, Overlay (12-mo lookback, Overlay (12-mo lookback, monthly rebalance) daily rebalance) monthly rebalance)


4.4%

3.7%

5.6%

5.5%

2.6%

2.6%

3.9%

2.6%

2.2%

Volatility

5.6%

3.3%

5.7%

7.1%

4.8%

4.2%

6.0%

2.6%

3.6%

IR

0.77

1.10

0.98

0.77

0.54

0.61

0.65

1.01

0.61


-0.17

-0.21

-0.11

-0.15

-0.02

-0.06

-0.15

-0.14

0.05


-0.08

0.14

0.12

0.17

0.32

0.37

0.35

0.38

0.57


2.1%

4.3%

6.7%

6.2%

3.3%

3.1%

2.9%

2.9%

3.7%

Volatility

6.5%

3.6%

3.2%

5.9%

2.5%

1.8%

3.3%

1.7%

3.1%

IR

0.33

1.20

2.09

1.04

1.36

1.72

0.86

1.78

1.21


0.25

0.23

0.68

0.52

-0.04

0.06

0.07

0.11

0.34


-0.13

0.35

-0.02

0.04

0.37

0.43

0.41

0.42

0.69


6.3%

3.2%

4.6%

4.8%

1.9%

2.1%

4.7%

2.3%

0.9%

Volatility

4.7%

3.2%

7.4%

8.3%

6.3%

5.7%

7.7%

3.3%

3.7%

IR

1.33

0.97

0.63

0.58

0.30

0.36

0.61

0.71

0.24


-0.41

-0.23

-0.66

-0.65

-0.20

-0.20

-0.22

-0.40

-0.49


0.14

0.05

0.21

0.30

0.38

0.48

0.42

0.41

0.55


-1.8%

9.0%

4.9%

6.6%

3.3%

5.1%

3.6%

5.9%

4.5%

Volatility

5.3%

4.2%

7.1%

8.1%

5.1%

4.0%

9.9%

4.2%

3.2%

IR

-0.34

2.17

0.69

0.81

0.66

1.26

0.52

1.38

1.44


0.86

-0.92

0.22

0.09

-0.81

-0.93

-0.34

-0.89

-0.93


-0.93

0.84

-0.41

-0.27

0.69

0.87

0.12

0.79

0.88

# transactions per month Max loss (monthly)

1.0

25

6.0

2.0

8.3

1.7

0.9

15.8

1.8

4.2%

3.0%

5.3%

6.0%

-3.9%

4.1%

7.3%

-2.9%

-2.6%

Max gain (monthly)

-9.1%

-2.8%

-4.0%

-5.7%

4.1%

-3.9%

-8.6%

3.0%

2.8%

Max drawdown

-20.8%

-4.4%

-9.6%

-10.4%

-11.1%

-8.5%

-14.8%

-3.8%

-5.0%

Source: JPMorgan

21


Global FX Strategy



Global FX Strategy J.P. Morgan Securities Ltd. June 27, 2008

JohnNormandAC(44-20)7325-5222 [email protected] Kartik Ghia (44-20) 7325-9865 [email protected]

Index & Alpha Strategy Update • Currency indices During the week G-10 implied vol rose 0.2% in contrast to EM vol which fell 0.1%. YTD the G-10 vol index is roughly flat while the EM index up 0.9%. JPY was the strongest performing currency last week gaining 0.6% (up 2.1% YTD). Following weak economic data, this week’s weakest performer was NZD which fell 0.7%. GBP remains the largest decliner YTD (-5.4%) .

10 carry basket is down 3% while the G-10 overlay and EM carry baskets are up 3.6% and 1.9% respectively. The forward carry indices had contrasting performances last week; The USD pairs index declined 0.4% and is now up 8.8% YTD while the Major pairs index rose 0.1% and is up 4.9% YTD.

• Commodityindices The aggregate index gained 2.2% last week following the rise in energy and is up 37.9% YTD.

• Currency alpha strategies All three carry baskets rose last week; the G-10 carry basket gained 0.2% and both the G-10 carry with overlay basket and the EM carry basket were up 0.3%. YTD the G-

• Commodity alpha strategies

5

Change (VXY, EM-VXY) or Returns (all others)

Current FOREIGN EXCHANGE

1

TM

3

G-10 implied volatility

VXY

10.1%

0.2%

-0.1%

-2.3%

EMimplied volatility

EM-VXY

NA

9.3%

-0.1%

-0.9%

-1.3%

2.5%

0.9%

USD trade-weighted*

USD TCI

NA

79.4

-0.4%

0.4%

0.5%

-8.4%

-3.6%

EUR trade-weighted GBP trade-weighted*

EUR TCI GBP TCI

NA NA

143.1 92.3

0.5% -0.2%

0.6% -0.1%

0.7% 0.1%

10.6% -11.6%

5.1% -5.4%

JPY trade-weighted

JPY TCI

NA

83.9

0.6%

-1.8%

-6.8%

9.4%

TM

AUD trade-weighted

NA

AUD TCI

NA

NZD trade-weighted* NZD TCI CNY trade-weighted CNY TCI TCIsalso available for NOK,CHF, CAD,SEK, MXN, HKD,KRW,SGD & TWD.

This weekly publication provides performance statistics on JPMorgan’s passive/benchmark products in currencies and commodities, and recommendations from alpha strategies. The report has six components:

The long-only momentum index fell 0.7% last week but is up 23.8% YTD. The Optimax (market neutral) index rose 0.8% and is up 1.3% YTD.

Table 1. Performance of indices and alpha strategies in currencies and commodities

Indices/Benchmarks

Investable Indices & Alpha Strategies is available on www.morganmarkets.com, by subscription and in FX Markets Weekly.

136.9

NA NA

3.9%

0.1%

2.1%

0.3%

-0.1%

6.3%

6.1%

6.5%

131.8 103.9

-0.7% -0.1%

-3.4% 1.5%

-4.4% 4.5%

-9.2% 3.2%

-5.3% 3.5%

1 List of index products on implied volatility for G-10 and 1. emerging markets (VXYTM, VXY-EMTM); carry-adjusted, trade-weighted currency indices (Tradeable Currency Indices); and commodity markets (JPMorgan Commodity Curve Index).

Alpha strategies

2

G-10 carry

IncomeFX

Long GBP/USD, NZD/USD, EUR/CHF, AUD/USD

G-10carry with ForwardOverlay

NA

Long EUR/USD, EUR/CHF and EUR/JPY

98.8

0.2%

-0.6%

1.1%

-13.2%

-3.0%

294.4

0.3%

0.2%

0.8%

-0.5%

Emerging Markets carry

IncomeEM

3.6%

Short USD/PHP,USD/TRYand USD/MXN; Long USD/HKD and USD/CNY

109.6

0.3%

2.6%

7.2%

8.7%

Forward Carry (USD pairs)

1.9%

NA

Short USDvsCAD and EUR;Long USD vs NOK,GBP,SEK,CHF, AUD, JPYand NZD

240.3

-0.4%

-0.6%

1.9%

15.3%

8.8%

Forward Carry (Major pairs)

NA

Asabove, plus longEUR vs NOK,GBP SEK,CHF and JPY;Short JPYvs CAD,NOK,GBP,AUD and long JPY vsNZD;Long AUD vs CAD, NZD;long NOK/SEK

244.2

0.1%

1.0%

3.0%

6.4%

4.9%

4

2 List of alpha strategies such as carry (G-10 and EM), 2. Forward Carry, Forward Carry Overlay and Momentum

COMMODITIES Indices/Benchmarks

Aggregate (35 commodities)

2.2%

7.4%

20.8%

55.2%

37.9%

M om en tu m - Lo ng o nl y

JPMCCI C -I GA R

Brent,WTI,Gas Oil, Gasoline, Heating Oil, Silver,Gold,Copper, Soybean,Red Wheat,Corn, Cocoa

NA

367.7 1 63 .3

-0.7%

5.8%

10.0%

43.4%

23.8%

Momentum - Long/Short

C-IGAR L/S

Long as above, plusshortZinc, Nickel, Lead,Aluminium,Sugar, Feeder Cattle

151.2

0.9%

6.9%

11.5%

35.2%

21.9%

Momentum- Optimax(Market Neutral)

Optimax

Long Brent,WTI,GasOil,Silver,Lead,Copper,Lead,Coffee;Short Gasoline,HeatingOil,NaturalGas,Gold,Zinc,Nickel, Aluminium,Wheat,Sugar

103.04

0.8%

0.6%

4.3%

7.5%

1.3%

Alpha strategies

*Although TCI returns in the table assume the investoris long the index, the product can also be sold to express a bearish view on the currency againstmajor trading partners.

Source: JPMorgan

www.morganmarkets.com

The certifying analyst is indicated by an

3 JPMorgan investable products giving access to indices 3. and alpha strategies, where available. 4 Current recommendations for alpha strategies. 4.

AC

. See page 3 for analyst certification and important legal and regulatory disclosures.

J.P. Morgan Securities Ltd. JohnNormand(44-20)7325-5222 [email protected]

5 P&L for each index and alpha strategy. For indices, P&L 5. assumes the investor is long, though each of the indices can also be sold short for bearish trades.

Global FX Strategy Index & Alpha Strategy Update

KartikGhia(44-20)7325-9865 [email protected]

Chart 1. Carry-to-risk rankings for G-10 currencies

Chart 4. Performance of FX alpha strategies

Carry-to-risk = 1mo libor differentials/annualised daily spot vol over past 12 months. First currency listed is the long leg.

YTD performance

6

0.4

105

0.3

100

0.2

95

0.1 0

6 Metrics and performance charts for indices and strategies. 6.

110

0.5

G-10 carry Emerging marketscarry G-10 carrywith Forward Overlay Forward Carry

90 D S U s v P B G

D S U s v D Z N

F H C s v R U E

D S U s v D U A

D S U s v K O N

Y P J s v

R U E

R U E s v K O N

D S U s v R U E

D S U s v K E S

Y P J s v

D S U

R U E s v P B G

D S U s v D A C

R U E s v K E S

F H C s v D S U

85 Jan-08

Feb-08

Mar-08

Apr-08

May-08

Jun-08

Source:JPMorgan

Source:JPMorgan

Chart 2. Carry-to-risk rankings for Emerging Markets currencies

Chart 5. JPMCCI vs Commodity IGAR (Long-only, Long/Short and Conditional Long/Short momentum)

Carry-to-risk = 1-mo implied rate differential from NDF or forward/annualised daily vol of forward outright rate over past 3 months. First currency listed is thelongleg. 2.0

indexed, Jan 2008 = 100, excess returns

130 125 120

1.5

115 110 1.0

105 100 JPMCCI C-IGAR Long-only C-IGAR L/S Optimax(MarketNeutral)

95

0.5

90 85

0.0

80 $ D $ $ Y $ $ $ $ $ $ $ $ $ $ D $ $ $ $ s K s s N s s s s s s s s s s W s s s s v H v v C v v v v v v v v v v T v v v v P s Y N s L R R P F B N D S P s K S K W H v R X v R A N O U U L G R L v K L I Z R C K P $ T M $ B Z I C H R P S A C $ S

Source:JPMorgan

Chart 3. VXY vs EM-VXY indices of implied volatility %, based on 3mo ATMF options

Feb-08

Mar-08

Apr-08

May-08

Jun-08

Chart 6. JPMCCI sector returns indexed, Jan 2008 = 100, excess returns 160

16 14

Jan-08

VXY

150

Energy

Precious metals

EM-VXY

140

I nd us tr ai l me ta sl

A gr ci u lt ur e

12

130 120

10

110 8

100

6

90 80

4 Jan-0 6

Jun-06

Source: JPMorgan

2

22

Nov-06

Ap r-07

Sep -07

Feb -08

Jan-08 Source:JPMorgan

Feb-08

Mar-08

Apr-08

May-08

Jun-08


Global FX Strategy



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Information has been obtained from sources believed to be reliable but JPMorgan Chase & Co. or its affiliates and/or subsidiaries (collectively JPMorgan) do not warrant its completeness or accuracy except with respect to any disclosures relative to JPMSI and/or its affiliates and the analyst’s involvement with the issuer that is the subject of the research. All pricing is as of the close of market for the securities discussed, unless otherwise stated. Opinions and estimates constitute our judgment as of the date of this material and are subject to change without notice. Past performance is not indicative of future results. This material is not intended as an offer or solicitation for the purchase or sale of any financial instrument. The opinions and recommendations herein do not take into account individual client circumstances, objectives, or needs and are not intended as recommendations of particular securities, financial instruments or strategies to particular clients. 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Global FX Strategy



Investment Strategies Series This series aims to offer new approaches and methods on investing and trading profitably in financial markets. 1. Rock-Bottom Spreads , Peter Rappoport, Oct 2001 2. Understanding and Trading Swap Spreads, Laurent Fransolet, Marius Langeland, Pavan Wadhwa, Gagan Singh, Dec 2001 3. New LCPI trading rules: Introducing FX CACI , Larry Kantor, Mustafa Caglayan, Dec 2001 4. FX Positioning with JPMorgan’s Exchange Rate Model , Drausio Giacomelli, Canlin Li, Jan 2002

24. Trading Credit Volatility, Saul Doctor and Alex Sbityokov, August 2006 25. Momentum in Commodities, Ruy Ribeiro, Jan Loeys and John Normand, September 2006 26. Equity Style Rotation , Ruy Ribeiro, November 2006 27. Euro Fixed Income Momentum Strategy, Gianluca Salford, November 2006 28. Variance Swaps, Peter Allen, November 2006

5. Profiting from Market Signals , John Normand, Mar 2002

29. Relative Value in Tranches I , Dirk Muench, November 2006

6. A Framework for Long-term Currency Valuation, Larry Kantor and Drausio Giacomelli , Apr 2002

30. Relative Value in Tranches II , Dirk Muench, November 2006

7. Using Equities to Trade FX: Introducing LCVI, Larry Kantor and Mustafa Caglayan, Oct 2002

31. Exploiting carry with cross-market and curve bond trades, Nikolaos Panigirtzoglou, January 2007

8. Alternative LCVI Trading Strategies, Mustafa Caglayan, Jan 2003

32. Momentum in Money Markets, Gianluca Salford, May 2007

9. Which Trade, John Normand, Jan 2004

33. Rotating between G-10 and Emerging Markets Carry, John Normand, July 2007

10. JPMorgan’s FX & Commodity Barometer , John Normand, Mustafa Caglayan, Daniel Ko, Nikolaos Panigirtzoglou and Lei Shen, Sep 2004

34. A simple rule to trade the curve , Nikolaos Panigirtzoglou, August 2007

11. A Fair Value Model for US Bonds, Credit and Equities, Nikolaos Panigirtzoglou and Jan Loeys, Jan 2005

35. Markowitz in tactical asset allocation , Ruy Ribeiro and Jan Loeys, August 2007

12. JPMorgan Emerging Market Carry-to-Risk Model , Osman Wahid, February 2005

36. Carry-to-Risk for Credit Indices , Saul Doctor and Jonny Goulden, September 2007

13. Valuing cross-market yield spreads, Nikolaos Panigirtzoglou, January 2006

37. Learning Curves – Curve Trading Using Model Signals , Jonny Goulden and Sugandh Mittal, October 2007

14. Exploiting cross-market momentum , Ruy Ribeiro and Jan Loeys, February 2006

38. A Framework for Credit-Equity Investing , Jonny Goulden, Peter Allen and Stephen Einchcomb, November 2007

15. A cross-market bond carry strategy , Nikolaos Panigirtzoglou, March 2006 16. Bonds, Bubbles and Black Holes , George Cooper, March 2006 17. JPMorgan FX Hedging Framework , Rebecca Patterson and Nandita Singh, March 2006 18. Index Linked Gilts Uncovered , Jorge Garayo and Francis Diamond, March 2006 19. Trading Credit Curves I , Jonny Goulden, March 2006 20. Trading Credit Curves II , Jonny Goulden, March 2006

39. Hedge Fund Alternatives , Ruy Ribeiro and Vadim di Pietro, March 2008 40. Optimizing Commodities Momentum, Ruy Ribeiro and Vadim di Pietro, April 2008 41. Momentum in Global Equity Sectors , Vadim di Pietro and Ruy Ribeiro, May 2008 42. Cross-momentum for EM equity sectors, Vadim di Pietro and Ruy Ribeiro, May 2008

21. Yield Rotator , Nikolaos Panigirtzoglou, May 2006

43. Trading the US curve, Grace Koo and Nikolaos Panigirtzoglou, May 2008

22. Relative Value on Curve vs Butterfly Trades, Stefano Di Domizio, June 2006

44. Momentum in Emerging Markets Sovereign Debt , Gerald Tan and William Oswald, May 2008

23. Hedging Inflation with Real Assets, John Normand, July 2006

45. Active Strategies for 130/30 Emerging Markets Portfolios, Gerald Tan and William Oswald, June 2008

24

46. Hedging Illiquid Assets, Peter Rappoport, July 2008

Fx Strategies

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