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Aggiungi al carrelloPAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
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Aggiungi al carrelloPaperback / softback. Condizione: New. New copy - Usually dispatched within 4 working days. 185.
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Aggiungi al carrelloPaperback. Condizione: Brand New. 128 pages. 9.13x6.14x0.35 inches. In Stock.
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Aggiungi al carrelloPaperback. Condizione: Brand New. 128 pages. 9.13x6.14x0.35 inches. In Stock.
Editore: Oxford University Press Dez 2022, 2022
ISBN 10: 0199279241 ISBN 13: 9780199279241
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 52,62
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware - Based on the author's Clarendon Lectures in Finance, this book develops and implements statistical methods for modelling corporate credit risk.
EUR 33,50
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Editore: Oxford University Press, Oxford, 2022
ISBN 10: 0199279241 ISBN 13: 9780199279241
Lingua: Inglese
Da: Grand Eagle Retail, Fairfield, OH, U.S.A.
EUR 40,28
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. This book, based on the author's Clarendon Lectures in Finance, examines the empirical behaviour of corporate default risk. A new and unified statistical methodology for default prediction, based on stochastic intensity modeling, is explained and implemented with data on U.S. public corporations since 1980. Special attention is given to the measurement of correlation of default risk across firms. The underlying work was developed in a series of collaborations over roughly the past decade with Sanjiv Das, Andreas Eckner, Guillaume Horel, Nikunj Kapadia, Leandro Saita, and Ke Wang. Where possible, the content based on methodology has been separated from the substantive empirical findings, in order to provide access to the latter for those less focused on the mathematical foundations.A key finding is that corporate defaults are more clustered in time than would be suggested by their exposure to observable common or correlated risk factors. The methodology allows for hidden sources of default correlation, which are particularly important to include when estimating the likelihood that a portfolio of corporate loans will suffer large default losses. The data also reveal that a substantial amount of power for predicting the default of a corporation can be obtained from the firm's "distance to default," a volatility-adjusted measure of leverage that is the basis of the theoretical models of corporate debt pricing of Black, Scholes, and Merton. The findings are particularly relevant in the aftermath of the financial crisis, which revealed a lack of attention to the proper modelling of correlation of default risk across firms. Based on the author's Clarendon Lectures in Finance, this book develops and implements statistical methods for modelling corporate credit risk. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.