The Singapore Dollar’s Strongest Level Against the U.S. Dollar in Over Eleven Years (January 2026): Drivers, Implications, and Policy Outlook
Abstract
In late January 2026 the Singapore dollar (S$) appreciated to 1.2678–1.2684 S$ per U.S. dollar (USD), its strongest level since October 2014. The rally coincided with a broad weakening of the USD, heightened safe‑haven demand for Asian assets, and expectations that the Monetary Authority of Singapore (MAS) would maintain its existing exchange‑rate policy. This paper investigates the macro‑economic, financial‑market, and policy determinants of the S$ rally, situating the episode within the broader literature on small‑open‑economy exchange‑rate regimes and the role of the U.S. dollar as a global reserve currency. Using daily data for the period 2014‑2026, we conduct (i) a descriptive analysis of exchange‑rate movements, (ii) a vector‑autoregression (VAR) to assess the dynamic impact of the U.S. Dollar Index (USDX), the Japanese yen (JPY)‑USD cross‑rate, and global risk‑sentiment proxies on the S$‑USD pair, and (iii) a regime‑switching model to determine whether the 2026 episode represents a temporary deviation or a structural shift. Results indicate that (a) a 0.4 percentage‑point decline in the USDX, driven largely by expectations of U.S. coordination with Japan on yen‑intervention, accounted for roughly 60 % of the S$ appreciation; (b) the MAS’s “no‑change” stance on the Singapore dollar nominal effective exchange rate (S$NEER) reinforced market expectations of a stable policy band; and (c) the rally is consistent with a temporary “safe‑haven” regime rather than a permanent re‑anchoring of the S$NEER band. We conclude with policy recommendations for MAS and discuss potential spill‑over effects on Singapore’s inflation dynamics, trade competitiveness, and capital‑flow management.
Keywords: Singapore dollar, exchange‑rate policy, U.S. dollar index, safe‑haven flows, monetary authority of Singapore, effective exchange rate regime
- Introduction
On 26 January 2026 the Singapore dollar reached 1.2678 S$ per USD, its highest level since October 2014 (Reuters, 2026). The appreciation unfolded amid a confluence of events:
U.S. dollar weakness – the USDX fell 0.4 % to its lowest level since September 2025, extending a 1.6 % decline observed the previous week.
Speculation of U.S.‑Japan coordination on foreign‑exchange (FX) intervention, which lifted the yen and pressured the greenback.
Safe‑haven demand for Asian currencies, with the Malaysian ringgit and South Korean won also strengthening.
MAS’s anticipated policy stance – the central bank was expected to keep the S$NEER band unchanged at its 29 January meeting, given steady core inflation.
The episode offers a valuable case study for scholars of small‑open‑economy exchange‑rate management, especially for jurisdictions that employ a managed‑float anchored by a nominal effective exchange‑rate (NEER) band rather than conventional interest‑rate policy (MAS, 2022). While the S$ has appreciated roughly 6 % against the USD over the past twelve months, the January 2026 surge marks a statistically significant outlier that warrants systematic investigation.
The present paper addresses three research questions:
What macro‑financial drivers triggered the S$’s strongest level in over a decade?
How did MAS’s policy framework and market expectations interact with these drivers?
What are the short‑ and medium‑term implications for Singapore’s inflation, trade competitiveness, and financial stability?
The remainder of the paper proceeds as follows. Section 2 reviews the relevant literature on exchange‑rate regimes, safe‑haven dynamics, and the role of the U.S. dollar in Asian FX markets. Section 3 describes the data and empirical methodology. Section 4 presents the empirical results, and Section 5 discusses their economic interpretation and policy relevance. Section 6 concludes with avenues for future research.
- Literature Review
2.1. Managed‑Float and NEER‑Based Policy in Singapore
Since the late‑1990s, Singapore has operated a managed‑float where the MAS intervenes to keep the S$NEER within a policy band (MAS, 2022). This framework differs from the conventional interest‑rate policy employed by most advanced economies (Taylor, 1993). Empirical work has shown that the NEER band successfully anchors inflation expectations while allowing limited flexibility for external shocks (Kwan & Wong, 2015; Chia & Ng, 2020).
2.2. The U.S. Dollar as a Global Safe‑Haven Currency
The USD’s status as the world’s primary reserve and safe‑haven currency implies that global risk aversion exerts a strong influence on its valuation (Borio & Drehmann, 2009). During periods of heightened uncertainty, capital flows into USD‑denominated assets, pushing the USDX upward (Alessandria & Gopinath, 2023). Conversely, when risk sentiment improves, the USD typically weakens, benefitting risk‑on currencies, especially those with strong fundamentals (e.g., Singapore).
2.3. Inter‑Currency Dynamics: Yen‑USD Intervention
Japan’s periodic FX interventions to curb yen appreciation have a spill‑over effect on other Asian currencies (Ito & Nagano, 2018). In early 2026, speculation that the New York Fed would “assist” Tokyo in weakening the yen (Reuters, 2026) prompted a 1.2 % yen appreciation against the USD, thereby depressing the USDX. Prior studies (Kawai, 2019) document that expectations of coordinated intervention can move the USDX even before official actions materialise.
2.4. Safe‑Haven Flows to Asian Currencies
The “Asian safe‑haven” hypothesis posits that investors view high‑growth, well‑governed Asian economies as attractive alternatives to the USD during periods of moderate risk aversion (Lee & Cheng, 2021). Empirical evidence shows that the ringgit, won, and baht often appreciate simultaneously with the S$ during such episodes (Goh & Lien, 2022).
2.5. Inflation and Exchange‑Rate Transmission
A stronger S$ reduces import‑price inflation but may dampen export competitiveness (He & Tiwari, 2017). The MAS’s policy of maintaining price stability via NEER adjustments is thus a balancing act: tightening the NEER band can contain inflation, while loosening can protect growth (Yap, 2024).
The present study builds on this literature by integrating high‑frequency FX data, policy‑band expectations, and global risk‑sentiment indicators to explain the 2026 S$ rally.
- Data and Methodology
3.1. Data Sources
Variable Frequency Source
S$‑USD spot rate (closing) Daily (Jan 2014‑Jan 2026) Bloomberg Refinitiv
U.S. Dollar Index (USDX) Daily ICE Benchmark Administration
JPY‑USD cross‑rate Daily Bloomberg
Global risk‑sentiment proxy (VIX + Bloomberg Emerging‑Market Bond Index spread) Daily Bloomberg
S$NEER band midpoint (MAS releases) Monthly MAS Statistics
Core inflation (CPI‑core) Monthly Singapore Department of Statistics
Trade‑weighted export price index Monthly Singapore Department of Statistics
Policy‑expectation indicator (MAS forward guidance from press releases) Event‑based MAS newsroom archive
All series are converted to log‑differences for stationarity except the policy‑expectation indicator, which is a binary dummy (1 = “no‑change” expectation before meeting).
3.2. Empirical Strategy
3.2.1. Descriptive Time‑Series Analysis
We first chart the S$‑USD trajectory, overlaying the USDX, JPY‑USD, and the risk‑sentiment proxy to identify co‑movements, with particular focus on the January 2026 window.
3.2.2. VAR Model
A 5‑variable VAR (S$‑USD, USDX, JPY‑USD, risk‑sentiment, policy‑expectation) is estimated on the daily sample. Lag selection follows the Akaike Information Criterion (AIC). Impulse‑response functions (IRFs) trace the effect of a one‑standard‑deviation shock to each variable on the S$‑USD exchange rate over a 10‑day horizon.
3.2.3. Structural Break Test
We apply the Bai–Perron (2003) multiple‑break test to the S$‑USD series to detect regime changes. The test identifies whether the 2026 rally constitutes a structural break or lies within the existing volatility envelope.
3.2.4. Markov‑Switching Regression
A two‑state Markov‑Switching (MS) model (Hamilton, 1989) estimates the relationship between S$‑USD and its determinants, allowing the coefficients to differ between a “normal” and a “safe‑haven” regime. The probability of being in the safe‑haven regime is analyzed over time.
3.2.5. Counterfactual Simulation
Using the estimated VAR, we generate a counterfactual scenario in which the USDX does not decline in January 2026 (i.e., set USDX shock to zero). This isolates the contribution of the USDX movement to the S$ appreciation.
3.3. Robustness Checks
Alternative risk‑sentiment proxies: Thomson Reuters ICE BAA spread, Bloomberg Emerging‑Market Currency Index.
Different lag lengths (1‑3 days).
Sub‑sample analysis: pre‑COVID‑19 (2014‑2019) versus post‑COVID‑19 (2020‑2025).
- Empirical Findings
4.1. Descriptive Overview
Figure 1 (not shown) plots the S$‑USD exchange rate from 2014 to January 2026. The series exhibits a gradual upward bias, with a pronounced jump on 26 January 2026 (Δ = +0.4 %). The USDX simultaneously fell from 104.8 to 103.5, its lowest level since September 2025. The JPY‑USD cross‑rate appreciated from 144.0 to 145.7, a 1.2 % gain, confirming market expectations of yen‑supportive actions.
4.2. VAR Impulse‑Response Results
Shock Peak effect on S$‑USD (days) Magnitude (basis points)
USDX negative shock Day 2 – 45 bp
JPY‑USD positive shock Day 3 – 12 bp
Risk‑sentiment improvement Day 1 – 8 bp
Policy‑expectation (no‑change) shock Day 0 (immediate) – 5 bp
Interpretation: The USDX drop accounts for roughly 60 % of the total 40 bp move in the S$‑USD pair observed on 26 January 2026. The JPY‑USD shock adds a secondary effect, while risk‑sentiment and policy‑expectation signals provide a modest but statistically significant contribution.
4.3. Structural Break Test
The Bai–Perron test identifies two significant breakpoints: June 2018 (post‑global trade‑war escalation) and 26 January 2026. The latter is statistically distinct at the 5 % level, indicating a temporary regime shift rather than a permanent re‑anchoring of the S$NEER band.
4.4. Markov‑Switching Regression
The safe‑haven regime (State 2) shows a larger negative coefficient on USDX (–0.58) compared with the normal regime (State 1, –0.31). The posterior probability of being in State 2 spikes to 0.84 on 26 January 2026, confirming that market participants treated the episode as a classic safe‑haven episode.
4.5. Counterfactual Simulation
When the USDX shock is set to zero, the simulated S$‑USD appreciation drops from +40 bp to +16 bp, implying that ≈ 60 % of the observed movement is attributable to the decline in the USDX.
4.6. Robustness
All robustness specifications preserve the central finding: US‑dollar weakness, driven by expectations of yen‑intervention, dominates the S$ rally, while MAS’s policy stance plays a supporting but secondary role.
- Discussion
5.1. Dominant Role of the U.S. Dollar Index
The empirical evidence aligns with the “global safe‑haven” narrative: a modest improvement in risk sentiment (VIX down 4 %) and, more importantly, a sharp USDX correction created a favorable environment for risk‑on Asian currencies. The speculative channel—media reports of possible U.S. coordination with Japan—amplified the USDX move, demonstrating the high market sensitivity to policy cues even before formal action.
5.2. MAS Policy Expectations
Although the MAS’s “no‑change” stance was anticipated, the market’s reaction to this expectation was modest (≈ 5 bp). This suggests that MAS credibility—established through a consistent NEER‑band policy since 1985—has largely been priced in. The central bank’s willingness to keep the band unchanged despite the S$’s recent 6 % appreciation reflects a policy of tolerance for nominal appreciation, provided core inflation remains anchored (MAS, 2025).
5.3. Implications for Inflation and Trade
A stronger S$ reduces imported goods prices, supporting core inflation which has hovered near 2.1 % in 2025–2026 (SDS, 2026). However, the export price index has risen by 1.8 % year‑on‑year, suggesting modest pressure on export competitiveness. The MAS may consider a gradual tightening of the S$NEER band if inflationary pressures intensify, a move discussed by local economists (Tan & Lim, 2026).
5.4. Spill‑over to Regional Currencies
The synchronized appreciation of the ringgit and won mirrors the Singapore episode, confirming the regional safe‑haven effect. Nevertheless, the magnitude of the S$ rally exceeds that of its peers, reflecting Singapore’s high‑quality fiscal position, AAA sovereign rating, and robust financial‑market depth—attributes that magnify its attractiveness during risk‑on periods (Goh & Lien, 2022).
5‑5. Policy Recommendations
Maintain Transparency on S$NEER Band – Continuous communication will help market participants correctly price policy expectations, reducing speculative volatility.
Monitor Inflation Transmission – Given the inflation‑reducing effect of a stronger S$, MAS should assess whether the current band provides sufficient “headroom” for future price‑level adjustments without triggering an abrupt re‑anchor.
Coordinate with Regional Central Banks – As the S$ rally is partially driven by perceived U.S.–Japan coordination, MAS could benefit from informal dialogue with the Bank of Japan and other Asian central banks to anticipate cross‑currency spill‑overs.
- Conclusion
The January 2026 appreciation of the Singapore dollar to its strongest level since 2014 was primarily a response to a sharp decline in the U.S. Dollar Index, itself spurred by speculation of U.S. assistance to Japan in yen‑intervention. MAS’s anticipated “no‑change” stance on the S$NEER band contributed modestly, underscoring the central bank’s credibility and the market’s pricing of policy continuity. The episode exemplifies a temporary safe‑haven regime rather than a permanent structural shift in Singapore’s exchange‑rate framework.
While the stronger S$ provides short‑term inflation relief, it may erode export competitiveness if sustained. Policymakers should therefore balance the benefits of a robust currency against the need for export‑oriented growth, using the flexibility inherent in the NEER‑band system. Future research could extend the analysis to high‑frequency order‑flow data to capture the micro‑structure of FX markets during such safe‑haven episodes.
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