Test anfragen
Damodaran begins with the sovereign default spread — the additional premium investors demand to hold a risky sovereign's debt over a risk-free government bond. He derives this in one of two ways, and the distinction matters.
The primary method is rating-based. Damodaran first collects sovereign CDS data and Moody's ratings for all countries where CDS is actively traded. He uses this data to build a lookup table that maps each Moody's rating category to an average CDS spread — effectively a benchmark spread for each credit tier. For rating buckets where no CDS data exists, he interpolates from surrounding data points to fill the gaps. Once this table is established, he does not rebuild it from scratch at each update. Instead, he anchors to the previous calculation date and rolls the spreads forward by the median percentage change in CDS across all countries with available data — giving him a reference dataset that evolves with market conditions without being overly reactive to any single observation. Each country's adjusted default spread is then derived by looking up that country's Moody's rating in the table.
The alternative method is country-specific. For countries with actively traded sovereign CDS, Damodaran uses each country's own CDS spread directly.
A sovereign CDS spread represents the annual premium an investor pays to insure against the default of that government's debt. Even the safest sovereigns trade with a small positive CDS spread — the market prices a residual baseline of systemic risk into every contract. A country's raw CDS spread therefore reflects both its own credit risk and this market-wide baseline. To isolate what is truly country-specific, Damodaran nets each country's CDS against Switzerland's — the sovereign with one of the lowest actively traded spreads in the market — stripping out the baseline and leaving only the incremental country risk. Historically he used the US CDS spread for this netting, but switched to Switzerland following the US sovereign downgrade.
A sovereign default spread is a debt market signal. It tells you how much more investors demand to hold a risky sovereign's bonds relative to a risk-free government bond. But equity occupies a different and riskier position — equity investors are junior in the capital structure, they are last to be compensated if things deteriorate, and the value of their stake is subject to greater volatility than the debt above them. Applying a bond market spread directly to a cost of equity build-up would therefore understate the true equity risk premium.
To bridge this gap, Damodaran applies a scalar: the ratio of equity market volatility to sovereign bond market volatility. The underlying assumption is that equity and bond markets are responding to the same country risk conditions, with equity amplifying that risk by a measurable factor. The scalar converts the bond market signal into equity terms.
A consideration for the user: the scalar is derived from two emerging market indices — the S&P Emerging BMI on the equity side and the iShares JPMorgan USD Emerging Markets Bond ETF on the bond side — yet it is applied universally to all countries in Damodaran's dataset, including developed markets such as France or Australia. Practitioners applying this framework to developed market valuations should be aware that the scalar may not reflect the volatility dynamics of the specific markets they are working with.
Damodaran computes the scalar using five years of price volatility data across the two indices described above. For each of the five calendar years, he calculates the volatility of daily price returns for both indices. He then averages the five annual volatility figures separately for equity and bonds, and divides the two averages to produce the scalar. His published figure as of January 2026 is 1.52, implies that equity markets have been approximately 52% more volatile than sovereign bond markets over the measurement period.
A consideration for the user: this calculation uses price returns rather than total returns. Total return data would produce a cleaner volatility estimate, though the practical difference is unlikely to be large. Lastly, in his published dataset, the scalar is applied to all countries by default.
A consideration for the user: within a WACC build-up, the scalar is a cost of equity adjustment only. For the cost of debt, Damodaran's own framework applies the pre-scalar default spread directly — the reasoning being that the scalar exists to convert a bond market signal into equity terms, and no such conversion is needed when estimating the cost of debt itself.
Understanding how these numbers are constructed is not just an academic exercise. The methodology involves a series of design choices — how the default spread is derived, whether and how the scalar is applied, and which figures flow into the cost of equity versus the cost of debt — each of which can materially affect the output. Practitioners who use Damodaran's published figures directly without understanding these mechanics risk applying numbers that do not reflect the assumptions appropriate totheir specific valuation context.
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