Pricing and Hedging Recovery Risk with Structural and Reduced Form Models
The fixed-income literature attempts to explain credit spreads though a decomposition into different risk premia. The most commonly analyzed risk premia are default and liquidity risk. Recovery risk has not received much attention most likely because of the pervasive practice of assuming constant recovery in most credit models. However, assuming a constant recovery has two major effects. The first is we have inconsistent pricing (if recovery is a known constant, what is the price of a recovery swap) and the second is over- or underpricing the default risk portion of the credit spread . In this talk I will present recent work on isolating the recovery risk premium in corporate bond and CDS spreads using both structural and hazard rate models. This allows us to isolate the recovery risk premium from the default risk premium, as well as provide a consistent pricing framework for all recovery linked products including bonds, CDS and recovery swaps. Finally, we discuss some trading opportunities that can be exploited using framework.
Biography
Nick Costanzino received his PhD in Applied Mathematics in 2006 from Brown University in Providence R.I. His thesis combined tools from pseudodifferential operators and dynamical systems to prove multidimensional stability of certain nonlinear wave structures in fluids. He later moved to the Penn State University Math Department as a Chowla Assistant Professor where he was introduced to quantitative finance and helped developed their Mathematical Finance program. After a brief tenure at Wilfrid Laurier University in Canada he then moved to the finance industry working in various credit roles including risk manager for the CDS and corporate bond trading desk at Scotiabank. He is interested in all areas of quantitative finance, but particularly those which lead to improvements in understanding the credit and equity markets.
Nick is currently in the Investment Analytics group at AIG in New York and is a member of RiskLab at the University of Toronto.
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