Debtor-in-possession financing and the resolution of uncertainty in Chapter 11 reorganizations

This paper investigates the use of debtor-in-possession (DIP) financing by firms reorganizing under Chapter 11. A model is developed in which there is asymmetric information between the creditors of a distressed firm and its management. In this context, it is demonstrated that reliance on DIP financing resolves informational asymmetries regarding the true economic value of distressed firms. The model's conclusions are empirically supported in the paper and by results of extant research. The signaling role of DIP financing is evidenced both by the positive stock price reaction to DIP announcements and the fact that firms employing DIP financing have more successful reorganizations.     

Impact of multiple curve dynamics in credit valuation adjustments under collateralization

We present a detailed analysis of interest rate derivatives valuation under credit risk and collateral modeling. We show how the credit and collateral extended valuation framework presented in Pallavicini et al. [Funding valuation adjustment: FVA consistent with CVA, DVA, WWR, collateral, netting and re-hyphotecation, 2011], and the related collateralized valuation measure, can be helpful in defining the key market rates underlying the multiple interest rate curves that characterize current interest rate markets. A key point is that spot Libor rates are to be treated as market primitives rather than being defined by no-arbitrage relationships. We formulate a consistent realistic dynamics for the different rates emerging from our analysis and compare the resulting model performances to simpler models used in the industry. We include the often neglected margin period of risk, showing how this feature may increase the impact of different rates dynamics on valuation. We point out limitations of multiple curve models with deterministic basis considering valuation of particularly sensitive products such as basis swaps. We stress that a proper wrong way risk analysis for such products requires a model with a stochastic basis and we show numerical results confirming this fact.