The Effect of Company’s Interest Coverage Ratio on the Structural and Reduced-Form Models in Predicting Credit Derivatives Price

Document Type : Research Paper


Department of Accounting, Kerman Branch, Islamic Azad University, Kerman, Iran


Derivative pricing models use either fixed or variable interest rates at the corporate level to compensate for the devaluation, which results in an estimated accounting profit caused by the cash inflation at the maturity date. These models also fail to take into account the lost opportunity costs, which are considered a deficiency. Accordingly, the present study set out to remove this problem by adding the company’s Interest Coverage Ratio (ICR) to pricing models, which is the novelty of this study. The research data was extracted from the Bloomberg Terminal for an eight-year period from 2008 to 2015. The statistical population of the research included the North American and European companies recognized as the reference entities for Credit Default Swaps (CDS) in the given period, and the statistical sample consisted of 125 companies. The data was analyzed using four Artificial Neural Network (ANN) algorithms, namely ANFIS, NNARX, AdaBoost, and SVM. The research results indicated the increased predictive accuracy of the pricing models under scrutiny after adding the ICR. The findings also shed light on the superiority of the intensity model over the structural model in prognosticating the price of CDS contracts.


Main Subjects

Article Title [فارسی]

تاثیر نسبت پوشش بهره ی شرکتی برمدلهای ساختاری و کاهشی در پیش بینی قیمت مشتقه های اعتباری

Authors [فارسی]

  • امیر حسین تائبی نقندری
  • حدیث زینلی
  • اصغر بیت اللهی
گروه حسابداری، دانشگاه آزاد اسلامی واحد کرمان، ایران
Abstract [فارسی]

مدلهای قیمت گذاری از نرخهای بهره ی ثابت یا مختلف جهت قیمت گذاری اوراق مشتقه استفاده میکنند که موجب پیش بینی قیمتی بر طبق سود حسابداری ناشی از تورم وجه نقد در سررسید میشود. این مدلها فرصت ازدست رفته را لحاظ نمیکنند و به این ترتیب پژوهش جاری با اضافه کردن نسبت پوشش بهره به مدلهای قیمت گذاری، سعی در برطرف کردن این کاستی دارد که نوآوری این پژوهش نیز میباشد. داده های مورد نیاز پژوهش از پایگاه بلومبرگ طی دوره ی مالی 2015-2008 برای شرکتهای آمریکای شمالی و اروپایی استخراج شده است که از قراردادهای سواپ نکول اعتباری استفاده کرده اند. برای آنالیز داده ها از مدلهای مختلف شبکه های عصبی مصنوعی مثل آدابوست، انفیس، نارکس، و رگرسیون ماشین بردار پشتیبان استفاده شده است. نتایج حاکی از آنست که افزودن نسبت پوشش بهره به اطلاعات موجود در قراردادهای سواپ نکول اعتباری موجب افزایش قدرت پیش بینی کنندگی مدلهای ساختاری و شدتی میگردد. همچنین این نتیجه حاصل شد که مدلهای شدتی از قدرت پیش بینی کنندگی بیشتری نسبت به مدلهای ساختاری برخوردارند

Keywords [فارسی]

  • مدل مرتون
  • مدلهای کاهشی
  • سواپ نکول اعتباری
  • نسبت پوشش بهره
  • شبکه های عصبی مصنوعی
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