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2008 | 12 | 2 | 69-94

Article title

Determinants of Commercial Mortgage-Backed Securities Credit Ratings: Australian Evidence


Title variants

Komercine Hipoteka Užtikrintų Vertybinių Popierių Kredito Reitingų Empirinė Analizė: Australijos Pavyzdys

Languages of publication


Using artificial neural networks (ANN) and ordinal regression (OR) as alternative methods to predict Commercial Mortgage-backed Securities (CMBS) credit ratings, we examine the role that various financial and industry-based variables have on CMBS credit ratings issued by Standard and Poor's from 1999-2005. Our OR results show that rating agencies use only a subset of variables they describe or indicate as important to CMBS credit rating as some of the variables they use were statistically insignificant. Overall, ANN show superior results to OR in predicting CMBS credit ratings.
Sisteminant komercine hipoteka užtikrintų vertybinių popierių prekybos sandorius, svarbiausias tikslas - gauti aukštą kredito reitingą, nes tai daro poveikį pelningumui ir emitento sėkmei. Kredito reitingų agentūros teigia, kad jų vertinimai išreiškia kiekvienos agentūros nuomonę apie potencialią emitento nemokumo riziką ir daugiausia remiasi emitento gebėjimo bei noro grąžinti savo skolą analize, kurią atlieka komitetas, taigi tyrinėtojams jų reitingų kiekybiškai replikuoti nepavyktų. Tačiau tyrinėtojai replikavo obligacijų reitingus, remdamiesi prielaida, kad finansiniai koeficientai turi daug informacijos apie įmonės kredito riziką. Prognozuodami komercine hipoteka užtikrintų vertybinių popierių reitingus, kaip alternatyvius metodus naudojame dirbtinius neuroninius tinklus ir ranginę regresiją. Ranginės regresijos rezultatai rodo, kad reitingų agentūros naudoja tik tą kintamųjų poaibį, kuriuos jos apibūdina arba nurodo kaip svarbius komercine hipoteka užtikrintų vertybinių popierių reitingui, nes kai kurie iš naudojamų kintamųjų statistiškai nereikšmingi. Apskritai dirbtinių neuroninių tinklų rezultatai, prognozuojant komercine hipoteka užtikrintų vertybinių popierių reitingus, geresni nei ranginės regresijos.









Physical description


  • School of Urban Development, Queensland University of Technology, Brisbane 4001, Queensland, Australia
  • School of Economics and Finance, Curtin University of Technology, Perth WA 6845, Western Australia


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