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Authors = Nick Costanzino

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7 pages, 313 KiB  
Article
A Simple Traffic Light Approach to Backtesting Expected Shortfall
by Nick Costanzino and Michael Curran
Risks 2018, 6(1), 2; https://doi.org/10.3390/risks6010002 - 9 Jan 2018
Cited by 18 | Viewed by 11892
Abstract
We propose a Traffic Light approach to backtesting Expected Shortfall which is completely consistent with, and analogous to, the Traffic Light approach to backtesting VaR (Value at Risk) initially proposed by the Basel Committee on Banking Supervision in their 1996 consultative document Basle [...] Read more.
We propose a Traffic Light approach to backtesting Expected Shortfall which is completely consistent with, and analogous to, the Traffic Light approach to backtesting VaR (Value at Risk) initially proposed by the Basel Committee on Banking Supervision in their 1996 consultative document Basle Committee on Banking Supervision (1996). The approach relies on the generalized coverage test for Expected Shortfall developed in Costanzino and Curran (2015). Full article
19 pages, 402 KiB  
Article
A General Framework for Incorporating Stochastic Recovery in Structural Models of Credit Risk
by Albert Cohen and Nick Costanzino
Risks 2017, 5(4), 65; https://doi.org/10.3390/risks5040065 - 20 Dec 2017
Cited by 4 | Viewed by 3548
Abstract
In this work, we introduce a general framework for incorporating stochastic recovery into structural models. The framework extends the approach to recovery modeling developed in Cohen and Costanzino (2015, 2017) and provides for a systematic way to include different recovery processes into a [...] Read more.
In this work, we introduce a general framework for incorporating stochastic recovery into structural models. The framework extends the approach to recovery modeling developed in Cohen and Costanzino (2015, 2017) and provides for a systematic way to include different recovery processes into a structural credit model. The key observation is a connection between the partial information gap between firm manager and the market that is captured via a distortion of the probability of default. This last feature is computed by what is essentially a Girsanov transformation and reflects untangling of the recovery process from the default probability. Our framework can be thought of as an extension of Ishizaka and Takaoka (2003) and, in the same spirit of their work, we provide several examples of the framework including bounded recovery and a jump-to-zero model. One of the nice features of our framework is that, given prices from any one-factor structural model, we provide a systematic way to compute corresponding prices with stochastic recovery. The framework also provides a way to analyze correlation between Probability of Default (PD) and Loss Given Default (LGD), and term structure of recovery rates. Full article
28 pages, 350 KiB  
Article
Bond and CDS Pricing via the Stochastic Recovery Black-Cox Model
by Albert Cohen and Nick Costanzino
Risks 2017, 5(2), 26; https://doi.org/10.3390/risks5020026 - 19 Apr 2017
Cited by 6 | Viewed by 6734
Abstract
Building on recent work incorporating recovery risk into structural models by Cohen & Costanzino (2015), we consider the Black-Cox model with an added recovery risk driver. The recovery risk driver arises naturally in the context of imperfect information implicit in the structural framework. [...] Read more.
Building on recent work incorporating recovery risk into structural models by Cohen & Costanzino (2015), we consider the Black-Cox model with an added recovery risk driver. The recovery risk driver arises naturally in the context of imperfect information implicit in the structural framework. This leads to a two-factor structural model we call the Stochastic Recovery Black-Cox model, whereby the asset risk driver At defines the default trigger and the recovery risk driver Rt defines the amount recovered in the event of default. We then price zero-coupon bonds and credit default swaps under the Stochastic Recovery Black-Cox model. Finally, we compare our results with the classic Black-Cox model, give explicit expressions for the recovery risk premium in the Stochastic Recovery Black-Cox model, and detail how the introduction of separate but correlated risk drivers leads to a decoupling of the default and recovery risk premiums in the credit spread. We conclude this work by computing the effect of adding coupons that are paid continuously until default, and price perpetual (consol bonds) in our two-factor firm value model, extending calculations in the seminal paper by Leland (1994). Full article
(This article belongs to the Special Issue A Celebration of the Ties That Bind Us: Connections between Actuarial Science and Mathematical Finance)
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