The Diversification Benefits Of Asia FX Futures

• ‘Emerging’ Asia economies are now as large as advanced economies, yet investors continue to show bias towards trading developed markets.
• At the same time, the performance of systematic trading strategies that solely focus on G10 have underperformed in recent years.
• We find that Asian FX provide important diversification benefits that G10 currencies cannot capture. This could provide an avenue to improve returns by expanding the universe of currencies traded.

For decades, investors have preferred trading developed markets over so-called emerging markets. This has especially been so for systematic traders and mode funds that use futures. Part of the reason is the size of developed economies and markets, part is that the investment returns possibilities were ample, and finally part is habit. However, all these factors are changing or should change.

Many emerging markets have now emerged – they now have larger economies and financial markets than many developed countries. Advanced economies made up 63% of the world economy in 1990, while emerging Asia made up 12%. Today, advanced economies make up 39% and emerging Asia a comparable 36% (Chart 1). Admittedly, the rest of emerging markets has not quite made the same strides as Asia – both emerging Europe and Latin America made up 14% of the world economy in 1990 and still make up 14% in 2020. So, the real story is the dramatic growth in Asian economies.

As for the returns possibilities, popular trading strategies such as FX momentum have struggled to deliver returns in G10 FX.^[1] Low interest rates and crises have likely reduced the differences across G10 markets, which could be impacting returns. Therefore, a crucial next step for investors is to broaden their universe of currencies, with Asian FX futures being the obvious candidate given the size of the Asian markets.

To better understand the diversification benefits of Asian currencies compared with G10 currencies and other asset classes, we will outline the correlation and volatility characteristics of Asian markets. Hopefully, this will prompt investors accustomed to trading only G10 markets to change their habits.

How Asian FX Futures Correlate With G10 FX Futures

A starting point would be to compare the major Asian FX futures markets – USD/CNH, INR/USD, USD/SGD, TWD/USD and KRW/USD – with their G10 counterparts. All currencies should have a strong correlation to the dollar index, or DXY, not least because they are quoted against the dollar. Nevertheless, we can see that both INR and CNH often diverge even from the DXY (Charts 2 and 3).

Looking at the rolling correlation of short-term changes in Asian FX futures, we find that in general the correlation is negative – that is, a weak dollar is associated with strong Asian FX and vice versa (Chart 4). But the correlation has fluctuated between slightly positive to -60% – whether INR, CNH or the average of the correlations across the five major Asian FX futures. This suggests there are some meaningful diversification benefits of using Asian FX. The DXY fails to capture their movements fully.

Of course, other G10 currencies could be more correlated to Asian FX, which would reduce the benefit of Asian FX. Therefore, we run a battery of correlations of various G10 FX currencies against short-term (3 days), medium-term (1 month) and long-term (12 months) changes in Asian currencies.

Starting with the dollar index, or DXY, versus Asian FX, we find that SGD has the biggest correlations with DXY (Chart 5). Its correlations range from -60% to -90%. The largest correlation is with the longer-term changes, which is the case with other currencies and markets too. INR, meanwhile, has the smallest correlations with DXY. Its correlations range from -15% to -35%. CNH has similarly low correlations, while KRW and TWD are somewhere in between INR and SGD.

The correlations with EUR/USD are similar in magnitude to the DXY but with the opposite (Chart 7). This makes sense as EUR is the largest component of the DXY. But the results are quite different when comparing with USD/JPY. Most of the correlations are much lower and, in the case of INR and CNH,

are close to zero in some cases (Chart 7). The outlier behaviour of USD/JPY is expected as USD/JPY tends to behave differently from all currencies whether G10 or EM.

AUD/USD is perhaps the more interesting G10 currency. It is typically used as proxy to trade to China and economically has strong export links to Asia. Despite this, the correlations are not dramatically larger with Asia FX (Chart 8). The strongest correlation remains with SGD – the correlations range from +60% to +80%. Both the KRW and TWD have higher correlations than against DXY and are in the 50% to 60% range for the shorter-term changes. The less correlated currencies CNH and INR have correlations in the range of 30% to 40%.

The broad message from these correlations is that Asian FX does appear to bring diversification to G10 currencies. Their correlations are lower and within Asian there are variations in correlations. INR and CNH stand out as the least correlated currencies, while SGD is most similar to G10.

Volatility Behaviour

Another important potential diversification benefit of using Asian FX is their volatility characteristics. So while the above correlation analysis gives a sense of how Asian and G10 currencies move up and down together or not, volatility best captures the way they move up or down.

First up, we can look at how INR/USD and USD/CNH volatility moves over time compared with EUR/USD volatility. We find some important differences. The most glaring one is that EUR/USD volatility has been trending down over the past five years until the spike around the COVID crisis. But neither USD/CNH nor INR/USD have exhibited the same trend (Charts 9 and 10).

The other difference is that the jumps in volatility have often occurred at different times – so 2015 in the case of USD/CNH and 2018 in the case of INR. Both were periods when EUR/USD volatility was stable or falling. Admittedly, INR volatility has moved more closely with EUR/USD volatility in the 2019 to 2020 period.

We can broaden the volatility analysis to all currencies – both G10 and Asia. We look at the correlation of volatility of each currency to EUR/USD volatility. And, we look at their fit over the past one, three and five years (Chart 11).

Starting with the whole sample period, the past five years. Here we find that EUR/USD volatility is most similar to AUD, CAD, NZD and SGD, while it is least similar to CNH, INR, GBP and JPY. And TWD, KRW and CHF volatilities correlate with EUR volatility in the middling range. When looking at the past one and three years, G10 FX volatilities start to look much more similar to EUR/USD volatilities. However, CNH, TWD and to some extent INR remain notably different.

Together, these results suggest that Asian FX futures contain important differences in volatility compared with G10 currencies. This would also provide a crucial diversification benefit.

Asia FX vs Other Asset Classes

An important driver of investor and model performance is combining different asset classes together – say equities with FX or rates with commodities. Therefore, we can compare Asian FX futures with other asset classes. We perform a similar correlation analysis of returns as we did in the earlier section. We compare the correlation of 3-day, 1-month and 12-month changes in various asset class futures with Asian FX futures.

Starting with equities, we use US S&P500 futures as our benchmark measure. We find that Asia FX correlations are in the +20% to +40% range for the shorter-term changes, while they are in the +40% to +60% range for the longer-term changes (Chart 12). This shows that Asia FX tends to go up when equities do, but the correlation is imperfect, so there is significant variation uncaptured by stocks.

The rates results – we use US 10y futures – is starker (Chart 13). The correlations are close to zero in most cases for the shorter-term changes. They do increase to the -20% to -50% range for longer term changes – so when bonds sell off (yields go up), Asia FX tends to perform well. Again, the correlations are still low enough to imply diversification benefits of using Asia FX.

Finally, we look at two commodity markets: oil and copper. We find generally low correlations with oil when looking at shorter-term changes (Chart 14). With long-term changes, the correlations

increase for KRW, SGD and TWD. The correlations with copper are higher and similar to that which we see with equities (Chart 15).

Overall, we find low enough correlations of Asia FX with other asset classes to suggest important diversification benefits, especially when we look at shorter-term changes like 3-day or 1-month changes.

Bottom Line

Running through a range of correlation and volatility analyses, we find that Asia FX futures – INR/USD, USD/CNH, USD/SGD, TWD/USD and KRW/USD – provide important and distinctive differences from G10 currencies and other asset classes. Therefore, they would deliver diversification benefits to investors should these currencies be added to developed market portfolios. Doing so could also arrest the decline seen in many systematic trading strategies that focus solely on developed markets.

1. See “How Asia FX futures can improve FX momentum strategy performance”, Macro Hive, July 2020 ↑