Liquidity risk in derivatives valuation: an improved credit proxy method

Abstract

The models used to calculate post-crisis valuation adjustments, market risk and capital measures for derivatives are subject to liquidity risk due to severe lack of available information to obtain market implied model parameters. The European Banking Authority has proposed an intersection methodology to calculate a proxy CDS or Bond spread. Due to practical issues of this method, Chourdakis et al. introduce a cross-section approach. In this paper, we extend the cross-section methodology using equity returns, and show that our methodology is significantly more accurate compared to both existing methodologies, and produces more reliable, stable and robust market risk and capital measures, and credit valuation adjustment.

Proxying credit curves via Wasserstein distances

“…However, not all counterparties have liquid CDSs trading in the market, and many have no CDSs trading at all. Therefore, proxy methodologies should be employed, as for instance the intersection methodology described in EBA (2013) (see also Chourdakis et al (2013a) and Sourabh et al (2018) for further details). In this approach the proxy spread for an illiquid entity with a given rating, region, sector, etc.…”

“…This approach suggests to bucket liquid CDSs according to their rating, region and sector. By averaging the CDS quotes in each bucket, one can then define proxy CDS quotes per bucket and maturity (see also Chourdakis et al (2013a) and Sourabh et al (2018)). This approach is very intuitive and easy to implement.…”

“…This approach has the advantage, as its name suggests, of taking into account the cross-sectional information of CDS spreads arising from different ratings, regions and sectors. The cross-sectional methodology of Chourdakis et al (2013a) has been extended in Sourabh et al (2018) in order to potentially include equity returns in the regression to further enhance the reliability and stability of the results.…”

Contagious defaults in a credit portfolio: a Bayesian network approach

“…In recent years, there has been an increasing interest in modelling credit risk by practitioners as well as academics (see e.g., Gregory, 2015, Green, Kenyon, & Dennis, 2014, Sourabh, Hofer, & Kandhai, 2018, De Graaf, Feng, Kandhai, & Oosterlee, 2014, de Graaf, Kandhai, & Reisinger, 2018, Simaitis, de Graaf, Hari, & Kandhai, 2016, Anagnostou & Kandhai, 2019. Portfolio credit risk models are concerned with the occurrence of large losses due to defaults or deteriorations in credit quality.…”