  Haircutting non-cash collateral has become a key element of the post-crisis
reform of the shadow banking system and OTC derivatives markets. This article
develops a parametric haircut model by expanding haircut definitions beyond the
traditional value-at-risk measure and employing a double-exponential
jump-diffusion model for collateral market risk. Haircuts are solved to target
credit risk measurements, including probability of default, expected loss or
unexpected loss criteria. Comparing to data-driven approach typically run on
proxy data series, the model enables sensitivity analysis and stress test,
captures market liquidity risk, allows idiosyncratic risk adjustments, and
incorporates relevant market information. Computational results for main
equities, securitization, and corporate bonds show potential for uses in
collateral agreements, e.g. CSAs, and for regulatory capital calculations.
