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ファイル | 記述 | サイズ | フォーマット | |
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jor.2022.032.pdf | 7.6 MB | Adobe PDF | 見る/開く |
タイトル: | A new approach to detecting change in credit quality |
著者: | Kevkhishvili, Rusudan https://orcid.org/0000-0003-4568-0081 (unconfirmed) |
キーワード: | credit quality credit risk detection Ornstein-Uhlenbeck mean reversion long-term mean |
発行日: | Jun-2022 |
出版者: | Infopro Digital Services Limited |
誌名: | Journal of Risk |
巻: | 24 |
号: | 5 |
開始ページ: | 51 |
終了ページ: | 73 |
抄録: | This paper provides a new framework for the detection and quantification of change in the corporate credit quality of established companies. These companies are important players in the economy, and analysis of their creditworthiness is of great interest to investors. Our approach is based on the observation that the deterioration of credit quality in established companies is a long-term process, because these firms use their resources as well as strong customer and supplier relationships to respond to market and technological changes. Based solely on the realized path of the stochastic process that represents creditworthiness, we propose an economically plausible endogenous mechanism that governs the shift in the long-term mean of the process. Specifically, we construct a model that detects a change in the mean-reversion level of an Ornstein–Uhlenbeck process representing the company’s leverage. Our model captures this change based on a number of realized upcrossings of a certain level (estimated from data) by the process itself, without introducing an additional source of uncertainty. Our approach is computationally simple and provides an efficient tool for monitoring changes in quantities related to credit quality, such as default probability. |
著作権等: | © 2022 lnfopro Digital Risk (IP) Limited The full-text file will be made open to the public on 12 JULY 2023 in accordance with publisher's 'Terms and Conditions for Self-Archiving'. |
URI: | http://hdl.handle.net/2433/279659 |
DOI(出版社版): | 10.21314/jor.2022.032 |
出現コレクション: | 学術雑誌掲載論文等 |
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