LEADER 04748nam 2200721z- 450 001 9910557403603321 005 20231214132842.0 035 $a(CKB)5400000000043640 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/68700 035 $a(EXLCZ)995400000000043640 100 $a20202105d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in Credit Risk Modeling and Management 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 electronic resource (190 p.) 311 $a3-03928-760-5 311 $a3-03928-761-3 330 $aCredit risk remains one of the major risks faced by most financial and credit institutions. It is deeply connected to the real economy due to the systemic nature of some banks, but also because well-managed lending facilities are key for wealth creation and technological innovation. This book is a collection of innovative papers in the field of credit risk management. Besides the probability of default (PD), the major driver of credit risk is the loss given default (LGD). In spite of its central importance, LGD modeling remains largely unexplored in the academic literature. This book proposes three contributions in the field. Ye & Bellotti exploit a large private dataset featuring non-performing loans to design a beta mixture model. Their model can be used to improve recovery rate forecasts and, therefore, to enhance capital requirement mechanisms. François uses instead the price of defaultable instruments to infer the determinants of market-implied recovery rates and finds that macroeconomic and long-term issuer specific factors are the main determinants of market-implied LGDs. Cheng & Cirillo address the problem of modeling the dependency between PD and LGD using an original, urn-based statistical model. Fadina & Schmidt propose an improvement of intensity-based default models by accounting for ambiguity around both the intensity process and the recovery rate. Another topic deserving more attention is trade credit, which consists of the supplier providing credit facilities to his customers. Whereas this is likely to stimulate exchanges in general, it also magnifies credit risk. This is a difficult problem that remains largely unexplored. Kanapickiene & Spicas propose a simple but yet practical model to assess trade credit risk associated with SMEs and microenterprises operating in Lithuania. Another topical area in credit risk is counterparty risk and all other adjustments (such as liquidity and capital adjustments), known as XVA. Chataignier & Crépey propose a genetic algorithm to compress CVA and to obtain affordable incremental figures. Anagnostou & Kandhai introduce a hidden Markov model to simulate exchange rate scenarios for counterparty risk. Eventually, Boursicot et al. analyzes CoCo bonds, and find that they reduce the total cost of debt, which is positive for shareholders. In a nutshell, all the featured papers contribute to shedding light on various aspects of credit risk management that have, so far, largely remained unexplored. 606 $aCoins, banknotes, medals, seals (numismatics)$2bicssc 610 $arecovery rates 610 $abeta regression 610 $acredit risk 610 $acontingent convertible debt 610 $afinancial modelling 610 $arisk management 610 $afinancial crisis 610 $arecovery rate 610 $aloss given default 610 $amodel ambiguity 610 $adefault time 610 $ano-arbitrage 610 $areduced-form HJM models 610 $arecovery process 610 $aCounterparty Credit Risk 610 $aHidden Markov Model 610 $aRisk Factor Evolution 610 $aBacktesting 610 $aFX rate 610 $aGeometric Brownian Motion 610 $atrade credit 610 $asmall and micro-enterprises 610 $afinancial non-financial variables 610 $arisk assessment 610 $alogistic regression 610 $aprobability of default 610 $awrong-way risk 610 $adependence 610 $aurn model 610 $acounterparty risk 610 $acredit valuation adjustment (CVA) 610 $aXVA (X-valuation adjustments) compression 610 $agenetic algorithm 615 7$aCoins, banknotes, medals, seals (numismatics) 700 $aVrins$b Fre?de?ric$4edt$01328358 702 $aVrins$b Fre?de?ric$4oth 906 $aBOOK 912 $a9910557403603321 996 $aAdvances in Credit Risk Modeling and Management$93038520 997 $aUNINA