FX vols might be under-pricing EUR risk
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Foreign Exchange

FX vols might be under-pricing EUR risk

Investors wishing to position for EUR weakness or hedge against a re-intensification of sovereign debt concerns should consider the good value offered in EURUSD and EURGBP risk reversals, says Credit Suisse.

The EUR risk premium priced into the FX options market has fallen significantly since the start of the year, as fears of disorderly contagion have waned and funding conditions have improved post long-term refinancing operation. While implied volatility is low across the board, the fall in EUR vols has been among the highest of G10 currencies. The share of total G10 FX variance explained by EUR has also fallen sharply after reaching record highs from the middle of 2010 to late 2011 as systemic risk concerns grew.

Share of total G10 FX variance explained by
EUR has fallen
 Source: Credit Suisse

The rapid fall in the eurozone risk premium is also visible looking at option skew – the premium for EUR puts versus calls are now at their cheapest since the first quarter of 2011.

To gauge whether the options market might be under-pricing perceived euro risks relative to other markets, Credit Suisse plotted a basket of six-month EUR/G10 25-delta risk reversals against the German five-year credit default swap (CDS) – a good gauge of underlying EUR systemic stress. The two variables have shown a very strong correlation but recent levels suggest EUR risk reversals are cheap relative to the German CDS.

EUR risk reversals look cheap realtive to correlate German CDS
Source: Credit Suisse

Using principal component analysis (PCA) to six-month EUR/G10 risk reversals suggests EURUSD and EURGBP risk reversals – buying puts and selling calls – offer the best value. Despite also receiving a strong PCA score, Credit Suisse do not favour buying EURAUD given their bearish Pacific Rim view. EURNOK, EURSEK and EURJPY are most expensive using the PCA framework.

 PCA suggests EURUSD and EURGBP risk reversals offer best value
Source: Credit Suisse

PCA uses orthogonal transformation to convert a group of possibly correlated series into a set of values of linearly uncorrelated variables called principal components.

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