91 research outputs found

    Liquidation Triggers and the Valuation of Equity and Debt

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    Net-worth covenants, as introduced by Black and Cox (1976), provide the firm’s bondholders with the right to force reorganization or liquidation if the value of the firm falls below a certain threshold. In the event of default, however, many bankruptcy codes stipulate an automatic stay of assets that prevent bondholders from triggering liquidation and thus impact many positive net-worth covenants. To consider this impact on a corporation’s capital structure we develop a general model of liquidation driven by a liquidation trigger. This trigger accumulates with time and severity of distress. In addition, current distress periods may have greater weight than old ones. The tractability of the approach stems from its ability to allow parameters appropriate for different legal rules and types of bondholder safety covenants. The proposed model includes several well-known models, like Merton, Black- Cox and others. We show how to valuate various types of corporate securities by using this model. Numerical results and sensitivity analysis are presented for selected basic cases.default, bankruptcy, liquidation trigger, debt pricing, corporate finance

    Ground Water Monitoring System and Procedures at Kin-Buc I Landfill Middlesex County New Jersey

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    37 pages (includes illustrations and map)

    Unsupervised Scale-Invariant Multispectral Shape Matching

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    Alignment between non-rigid stretchable structures is one of the hardest tasks in computer vision, as the invariant properties are hard to define on one hand, and on the other hand no labelled data exists for real datasets. We present unsupervised neural network architecture based upon the spectrum of scale-invariant geometry. We build ontop the functional maps architecture, but show that learning local features, as done until now, is not enough once the isometric assumption breaks but can be solved using scale-invariant geometry. Our method is agnostic to local-scale deformations and shows superior performance for matching shapes from different domains when compared to existing spectral state-of-the-art solutions
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