"Raising Green Debt Globally" identified 13 currencies deep enough for an Indian issuer to actually borrow in, ranked by headline policy-rate gap against India's 5.25% repo — and flagged Switzerland's franc, the widest gap of all, as "close to the least useful," on the reasoning that hedging costs would eat most of the saving. That was an inference from covered-interest-parity theory, not a measured number. This piece measures it: real 10-year INR volatility against the reachable currencies this site now has data for, to test whether the currencies with the biggest rate gap are also the ones actually riskiest to borrow in.
Which ECB Currencies Are Actually Investable, Once Real Volatility Replaces the Theory
"Raising Green Debt Globally" made a theoretical point: covered interest parity means the forward points on any currency pair price in almost exactly the interest-rate differential a borrower is trying to harvest, so the hedged cost of borrowing in a cheap currency converges toward the same number regardless of which cheap currency you pick — that's a structural, no-arbitrage fact, not something real data can overturn. What real data can answer is the separate question that piece only guessed at: for a borrower who doesn't fully hedge — because hedging is expensive, partial, or deliberately skipped to bank a favourable trend — which of these currencies is actually risky to be exposed to against the rupee? That's a volatility question, and this site now has real, computed answers for it.
| Currency | Policy rate, % | Gap vs India (5.25%) | 10y INR volatility (annualised), % | Gap ÷ volatility |
|---|---|---|---|---|
| Swiss franc (CHF) | 0.00 | 5.25pp | 12.53 | 0.42 |
| Japanese yen (JPY) | 1.00 | 4.25pp | 12.09 | 0.35 |
| Euro (EUR) | 2.25 | 3.00pp | 9.44 | 0.32 |
| US dollar (USD) | 3.62 | 1.63pp | 6.00 | 0.27 |
| British pound (GBP) | 3.75 | 1.50pp | 9.34 | 0.16 |
Policy rates and gaps as previously published in "Raising Green Debt Globally" (BIS central bank policy rate statistics, retrieved 4 Aug 2026). INR volatility derived from Yahoo Finance daily closes (CHFINR=X, JPYINR=X, EURINR=X, USDINR=X, GBPINR=X), 10-year annualised standard deviation of daily returns, retrieved 14 Aug 2026. "Gap ÷ volatility" is a simple ratio, not a risk-adjusted-return measure in the formal finance sense — it is a rough way of asking "how much rate advantage per unit of currency risk," nothing more precise than that.
The honest, slightly awkward finding: the two currencies with the biggest headline rate gap are also the two most volatile against the rupee. CHF and JPY sit at 12.5% and 12.1% annualised INR volatility respectively — roughly double the dollar's 6.0% and meaningfully above the euro's and pound's ~9.3–9.4%. That is not a coincidence a covered-interest-parity theorist would find surprising: a wider policy-rate gap against India generally reflects a more different monetary-policy stance, and more different stances tend to produce more currency movement, not less. The green-debt piece's original caution about Switzerland turns out to be directionally right, now for a measured reason rather than a theoretical one — but the same caution applies just as strongly to Japan, which the rest of this site's research (see the companion INR/JPY piece) has otherwise been treating as the standout cheap-financing case.
This is not a contradiction with the companion piece on INR/JPY — it's the other half of the same picture. That piece showed JPY/INR fell 10.3% over five years, meaning unhedged yen borrowers got lucky on trend, not just on the low coupon. This piece shows that same currency carries 12.09% annualised volatility — more than double the dollar's. Both facts are true simultaneously: the yen has been a favourable currency to have borrowed in on realised outcome, while also being a volatile, risky currency to be carrying unhedged exposure to on a forward-looking basis. A five-year favourable draw from a high-volatility distribution is a real, bankable outcome — and also not a reason to expect the next draw to go the same way.
The RBI's February 2026 ECB liberalisation (raising the automatic-route ceiling to the higher of $1bn or 300% of net worth, removing the all-in-cost ceiling) widened the pipe without changing this arithmetic. For a borrower planning to hedge fully, the covered-interest-parity point still holds: the choice of currency matters less than the depth and cost of that specific currency's hedge market, and CHF/JPY/EUR/USD/GBP are all liquid enough that hedging costs, not headline rate gaps, should drive the decision. For a borrower planning to run any meaningful unhedged residual — which is where the real coupon savings actually show up, per the green-debt piece's own framing — this data says the dollar is the conservative choice (smallest gap, but by far the lowest volatility), the euro and pound sit in a moderate middle tier, and CHF and JPY are the highest-conviction, highest-risk bets: the biggest potential saving, and the biggest potential currency loss, in the same two currencies.
Everything above assumes an ECB borrower can actually access a functioning INR hedge market on reasonable terms. As of April 2026, that market changed in a way that matters directly to this piece's argument. The RBI barred authorised dealers from offering rupee non-deliverable forward (NDF) contracts to resident Indians and NRIs, and prohibited rebooking cancelled NDF contracts, effective 1 April 2026. A separate but related move on 27 March 2026 replaced banks' prior Net Open Position limit (up to 25% of Tier-I/Tier-II capital, which had let some institutions run $1 billion or more in open FX positions) with a universal $100 million cap for all banks, effective by 10 April 2026. The RBI's own stated aim was to dismantle the offshore-onshore arbitrage channel that speculative NDF positioning had been running through, on top of addressing structural macro headwinds (crude prices, FPI outflows) documented elsewhere on this site.
What this means for the "run it unhedged" case above. Companies with genuine trade-related FX exposure — importers and exporters, which is what most ECB borrowers structurally are — still hedge through onshore deliverable forwards, which the NDF ban does not touch. What the ban removes is the offshore, non-deliverable route that speculative and some carry-trade-style positioning had used to bet on or hedge INR moves without ever settling in actual rupees. For an ECB borrower deciding whether to hedge fully or run an unhedged residual (Section 4), the practical menu just narrowed to deliverable, onshore instruments — a real, current constraint on top of the currency-choice logic this piece has otherwise focused on.
On the other side of the ledger, foreign capital entering India through rupee debt directly — rather than an Indian entity borrowing abroad — faces its own spread requirement: with US 10-year Treasuries yielding around 4.5%, Indian government securities have historically needed spreads of roughly 250–300 basis points over Treasuries to generate meaningful foreign demand, compensating investors for both rupee risk and India's credit risk. That is a different channel from the ECB route this piece covers, but it is the same underlying question — what does a foreign holder of rupee-linked exposure actually get paid to bear that risk — approached from the opposite direction.
What this piece does not establish. "Gap ÷ volatility" is a simplification, not a formal Sharpe ratio or a hedged-cost model — it ignores the actual forward-points cost of hedging (which, by covered interest parity, should already be close to the rate gap itself, making the ratio closer to "unhedged risk per unit of theoretical unhedged reward" than a true cost-benefit figure). It also doesn't cover the remaining 8 of the green-debt piece's 13 reachable currencies (SEK, DKK, CAD, KRW, CNY already covered elsewhere on this site, HKD, NOK, AUD) — INR-pair volatility for those was not computed for this piece. Nor does it model how the April 2026 NDF ban and NOP cap specifically change deliverable-forward pricing or liquidity — only that the offshore alternative is now closed.
About this article: Researched, written and edited by Umashankar Triplicane Dwarakanathan, with AI research assistance; every figure is meant to trace to the primary source cited. See the Editorial Policy for how sourcing, AI use and corrections work.