Fixed Income and Macro Commentary - August 2025

Impact of Dollar Depreciation on Commodity Prices

Summary of Findings:

USD depreciation acts purely as a short-term tailwind for Copper, rather than a structural volatility driver; VAR(1) estimates on daily log returns reveal a statistically significant negative relationship between lagged DXY returns and Copper returns.
Through applying Johansen cointegration tests, there is no evidence of a stable long-run equilibrium between DXY and Copper prices.
Copper exhibits pronounced volatility clustering, driven primarily by policy and supply shocks, rather than FX dynamics, as evidenced by GARCH(1,1) conditional volatility models.
Short-run Copper dynamics are weakly mean-reverting and largely auto-regressive, as identified by the VAR framework.
Gold and longer-duration US Treasuries are positively correlated as per OLS regression tests.

Section 1: Monthly Market Recap

This month's commentary will adopt a more research-focused tone, compared to July's write-up. We take a deep dive into the US dollar and its broader implications for commodities, focusing on gold and copper. Namely, we shall quantify exactly how much dollar depreciation is providing a tailwind to commodity prices. Subsequently, we shall examine the relationship between Gold Futures and the US 10Y yield, given that Gold is seen as a 'safe-haven' asset, while incorporating a VIX variable to measure market volatility. As a precursor to that analysis, we first summarise market movements from the 1st of August open to the 29th of August close across major equities, ETFs, indices, and commodities. All values are quoted in USD, and are indicative of broader trends rather than exact closing levels. This overview is intended to set the stage for subsequent discussions.

ETFs

SPY: $626.30 → $645.05, a 2.99% increase MoM.
- - NVDA (7.74% of SPY): $174.09 → $174.18, a 0.05% increase MoM.
  - MSFT (6.87% of SPY): $535.00 → $506.69, a 5.29% decrease MoM.
  - AAPL (6.32% of SPY): $210.87 → $232.14, a 10.09% increase MoM.
RSP: $182.26 → $188.62, a 3.49% increase MoM.
IWM: $216.22 → $235.17, a 8.76% increase MoM.

We saw strong advancements in equities markets through August, with the SPY up nearly 3%, reflecting an unusually bullish sentiment amid wider macro uncertainty. This may also be driven by investors looking to price in pending rate cuts, given stronger signals of a more dovish Fed. Surprisingly, the equal-weight index (RSP) outperformed the SPY slightly, underscoring that gains were not entirely concentrated to the mega-caps - a deviation from previous month's. This may be due to rate cuts being seen as more beneficial towards smaller-caps as opposed to larger-caps; evidenced by the fact that IWM, the iShares Russell 2000 ETF, has 3x the returns of the SPY. Meanwhile, Nvidia, which had been SPY's key driver earlier in the year, was essentially flat, suggesting a momentary consolidation phase after months of sustained increase.

TLT: $87.56 → $86.60, a 1.09% decrease MoM.

While Powell's Jackson Hole speech signalled a change in Fed policy, Treasury markets remained under pressure, with the long end selling off modestly as 20Y and 30Y yields ticked higher. The Fed's change in stance has now legitimised concerns over the last few months of a softening US labour market, and continues a broader 2025 theme where bonds have struggled to attract sufficient demand - largely due to sticky core inflation, macroeconomic uncertainty, and tariff-induced volatility. The weakness in TLT contrasts directly with equity gains. Investors in equity markets are seemingly over-optimistic about the short-term outlook of the US economy, but it's all doom and gloom in the Treasury market. Fine, let's just say that investors are more pragmatic about the future. Nonetheless, we see continued preference for risk assets over duration exposure, though this may soon change.

Additional Indices

DXY (Dollar Index): $100.01 → $97.77, a 2.24% decrease MoM.
GLD (SPDR Gold Trust): $308.61 → $318.07, a 3.07% increase MoM.
Gold Futures: $3286.20→ $3473.70, a 5.70% increase MoM.
CPER (US Copper Index Fund): $27.50 → $28.13, a 2.29% increase MoM.

Copper Futures: $4.43→ $4.59, a 3.61% increase MoM.

The most notable macro movement in August was the US dollar weakness, with DXY falling over 2%. This naturally provided a strong tailwind for commodities: gold futures rallied nearly 6%, and copper futures over 3.5%. For gold, this bullish trend was amplified by renewed safe-haven flows, amid greater geopolitical tensions and economic uncertainty, while copper benefitted from consistent increases in demand, particularly from China. The inverse relationship between the dollar and commodities was clearly visible this month, setting up our deeper dive into the structural links between the two.

Key Economic Data

August Unemployment Rate: 4.3%, up from 4.2% in July '25.

August CPI: Headline at 2.9% YoY, core at 3.1% YoY.

The labour market continued to show signs of weakening. For various reasons, we have found that the participation rate is starting to reduce. Normalising this month's data compared to the labour market size earlier this year, we find that unemployment would be closer to 5%. Inflation moderated slightly, though staying well above the Fed's target of 2%, with YoY core coming at 3.1$. However, there are rumours that the Fed is beginning to relax their policies on a strict 2% inflation rate, though only if GDP growth sufficiently outpaces inflation. The combination of a softer labour market and sticky inflation, however, complicates the Fed's policy decision-making. Markets have begun to price a higher probability of rate cuts by EoY, of around 70bps, which also drove dollar weakness, and by extension, strength in commodities.

Note:

(i) These are all approximate prices, meant to depict a trend as opposed to exact price changes.

(ii) All information is as of 31st August, 2025.

Section 2: Commodities and the US Dollar

In this section, we will introduce the research-oriented lens that we adopt to study the interplay between the US dollar, commodity markets, and Treasury yields. Our analysis will be guided by three questions:

How does DXY volatility spill over into commodities markets?
What is the empirical relationship between DXY, Copper Futures, and Gold Spot Prices?
What is the empirical relationship between Gold and US Treasury yields (10Y, 30Y), particularly when factoring in equity-market volatility (VIX)?

Hypothesis and Framework: USD depreciation alone does not drive volatility of commodities prices, but rather acts as a tailwind. Given that commodities are globally priced in USD, a weaker dollar increases the purchasing power of non-dollar buyers, thereby stimulating demand and pushing prices higher. This suggests that USD weakness should be positively correlated with commodity returns, though not necessarily with volatility. For gold in particular, our analysis incorporates a dual lens: (i) gold, as a dollar-denominated asset, and (ii) gold as a 'safe-haven' asset. Addressing (ii), gold competes directly with Treasuries. In recent months, central banks and institutional investors have been reallocating reserves towards gold, as a reflection of waning confidence in US bonds as the sole risk-free asset, and the increased unpredictability of the US economy, not to mention the diminishing autonomy of the Fed. This structural shift may weaken the traditional negative correlation between gold and yields, and we may even find that gold appreciates alongside rising yields. Copper, by contrast, is more cyclical and industrially driven, and would respond to both dollar moves and global economic expectations, alongside supply-side fluctuations, which bring about numerous arbitrage opportunities for investors. Finally, incorporating the VIX allows us to examine whether gold's role as a 'safe-haven' asset intensifies during equity market stress, and whether this alters its relationship with long-term Treasury yields.

Section 3: Commodities and the US Dollar, a Mathematical Approach

Volatility Dynamics of the US Dollar and Commodities Prices

We utilise a GARCH(1,1) model to analyse the volatility of Gold and Copper prices in relation to DXY by considering three components:

Long-Run Variance
Previous-Day Squared Error
Previous-Day Variance

We attempt to capture volatility clustering between the three components from 01-Aug-15 to 31-Aug-25.

All three assets show volatility clustering (albeit at different levels) through periods of heightened macroeconomic fluctuations, followed by gradual mean reversion. Copper exhibits the greatest overall volatility levels throughout 2015-2025, which all but reflects its persistent increased sensitivity to global industrial demand, cyclical macroeconomic factors, and supply-side shocks. Notably, Copper volatility shot up drastically around mid-2025, with Trump announcing blanket 50% tariffs on Copper entering the US. This resulted in a sharp spike in volatility of Copper prices, alongside a briefly sustained 15% increase in price, as a result of investor uncertainty. However, 25 days later, Trump corrected his policy to only capture semi-finished Copper and Copper-intensive derivative products, which makes up an almost negligible fraction of the market, hence resulting in a nearly 20% correction in prices. Uncertainty and unpredictability of economic policies has driven up Copper volatility. Comparatively, Gold volatility is more stable, which is consistent with its role as a safe-haven asset. The DXY displays the lowest and by far the most stable conditional volatility, given that it is still the most dominant currency in the global economy, and its relatively low speculative exposure.

Regardless, there are still major volatility spikes consistent across the three assets, observed during periods of global economic stress. Notably, around early 2020 (Covid-19 induced economic shock), we see Gold, Copper and DXY simultaneously spike in volatility, or around April of 2025, where all three - though less than in 2020 - exhibited greater volatility as an aftermath of Trump's 'Liberation Day' policies. These synchronised vol surges suggest that systemic shocks do occur and have rapid, tangible impact across asset classes.

Empirical Relationship between DXY, Gold and Copper

To quantify the joint dynamics of Gold, Copper and the US Dollar (DXY), we adopt a Vector Autoregression (VAR) framework, applied to daily log returns between August 2015 and August 2025. Before proceeding, we first assess the stationarity of each time series using the Augmented Dickey-Fuller (ADF) test. As expected, the ADF results suggest that the levels of all three series are non-stationary, with p-values of approximately 0.29, 0.99, and 0.54 for DXY, Gold, and Copper respectively, while their first differences are stationary, all with p-values 0.0. Thus, this confirms that the returns series - the first differences of the log prices - are exhibit the necessary behaviour for modelling, as VAR requires stationarity of data to ensure valid, comprehensible results.

Now, given that the series are integrated of order one, we conduct a Johansen co-integration test on the level series to explore potential long-run equilibrium relationships. This test evaluates whether there exists a linear combination of the three series that is stationary. Ultimately, we want to find if DXY, Gold, and Copper move together over the long term. The trace statistics for each of the three variables all exhibited values well below of the critical values at the 90%, 95%, and 99% confidence levels, indicating conclusively that there is no evidence of cointegration - which is surprising. This implies that there is no stable, long-term shared behaviour between DXY, Gold, and Copper prices. Hence, any joint movements must therefore be attributed to short-term dynamics, as opposed to persistent long-run market forces.

Now that we have stationarity in returns, and no cointegration in levels, we estimate a VAR(1) model in first differences to capture the short-term interactions between the three assets. Each equation in the VAR essentially models the current return of one asset as a linear function of lagged returns of all three assets (to test for auto-regression). Below are the key results from the model:

DXY: Lagged Gold and Copper returns do not exhibit significant coefficients, implying that short-term commodity shocks, usually attributed to supply-side pressures, do not materially influence USD movements. In fact, it seems that DXY seems to be exogenous within this VAR framework.
Gold: Lagged DXY returns exhibit a negative coefficient of approximately -0.153 (with p-value 0.051), suggesting that USD depreciation tends to raise Gold prices in the short-term. We see a potential spillover effect in Copper, with lagged Copper returns having a positive coefficient of 0.069 (with p-value 0.051). Though, noticeably Gold is seemingly mean-reverting, with lagged Gold returns taking a negative coefficient of -0.051 (p-value 0.014).
Copper: We find that lagged DXY returns also has a significant negative effect (-0.136, p=0.005), which is a strong indication of DXY weakness translating into higher Copper returns. Lagged Copper returns also depict a similar autoregressive effect (-0.043, p=0.05), while the relationship between Gold and Copper returns is relatively negligible.

The correlation matrix of residuals supports our above claims and interpretations. DXY exhibits negative correlations with Gold and Copper, (-0.28, -0.42 resp.), affirming the notion that USD depreciation coincides directly with upward pressure on commodity prices. Gold and Copper also display moderate positive correlation (0.23), reflecting this partial co-movement/spillover, driven largely by shared USD exposure, and potentially similar supply-side factors. All in all, it is conclusive that USD depreciation acts as a short-term tailwind for commodities prices, though is not the sole determinant of their volatility, or their returns.

Note: Most of the output can be found in the Appendix. To the left is the summary of regressions results from the VAR(1) model estimated using OLS.

Empirical Relationship between Gold and Long Duration Treasuries, including VIX

To explore the interconnected nature of treasury yields, gold price movements, and equity market volatility, we use the following regression model:

The OLS regression results reveal several key findings. To begin, the coefficient on the lagged 10Y yield is significantly negative (-0.0855, p=0.004), which implies that increases in the short-to-mid end of the yield curve tends to precede declines in Gold prices. This is unsurprising - higher yields provide more attractive entry points for investors to place money in essentially risk-free assets, increasing the opportunity cost of holding a non-coupon-bearing asset such as Gold. Conversely, the 30Y yield is quite positive (0.0773, p=0.048), which suggests that movements at the long end of the curve are associated with gains in Gold prices, as the longer end tends to factor in future expectations on the economy, such as future inflation or labour market strength, as opposed to purely rate policy shifts.

The VIX coefficient is slightly negative (-0.0087, p=0.007), which indicates that spikes in volatility in equity markets tend to precede very slight declines in Gold returns - potentially reflecting a short-term liquidity dynamic, where Gold is initially being sold off alongside risky assets as investors seek to raise cash, before reasserting its traditional safe-haven role over the longer term.

Overall, the regression achieves an R-squared value of approximately 1.7%. While this may seem negligible, low R-squared values are common when analysing daily financial data, where much of the variation is inherently stochastic (random). In fact, being able to identify even small predictive edges (≈2%) can be exploited at sufficient scale of capital.

These findings support a nuanced, pragmatic view of Gold's relationship with fixed income markets. We find that gold is inversely linked to short-term treasury yields, but positively related to long-term yields - natural, as investor concerns shift along the curve. The mixed signs on the yield coefficients also portray Gold's dual role in markets, acting as both an inflation hedge and a liquidity-sensitive asset, depending on the prevailing economic conditions, and future outlook amidst fluctuating market uncertainty.

Appendix

GARCH(1, 1) Individual Volatility Workings

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