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

Document Type : Research Paper

Authors

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

Abstract

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.

Keywords

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
  1. Afza, T., & Alam, A. (2011). Determinants of corporate hedging policies: A case of foreign exchange and interest rate derivative usage. African Journal of Business Management5(14), 5792-5797.

    Amuthan, R. (2014). Financial derivatives. Himalaya Publishing House.

    Aragon, G. O., & Li, L. (2019). The use of credit default swaps by bond mutual funds: Liquidity provision and counterparty risk. Journal of Financial Economics131(1), 168-185.

    Baños-Caballero, S., García-Teruel, P. J., & Martínez-Solano, P. (2014). Working capital management, corporate performance, and financial constraints. Journal of Business Research67(3), 332-338.

    Beytollahi, A., & Zeinali, H. (2020). Comparing prediction power of artificial neural networks compound models in predicting credit default swap prices through Black–Scholes–Merton model. Iranian Journal of Management Studies13(1), 69-93.

    Black, F., & Cox, J. C. (1976). Valuing corporate securities: Some effects of bond indenture provisions. The Journal of Finance31(2), 351-367

    Borio, C., & Gambacorta, L. (2017). Monetary policy and bank lending in a low interest rate environment: Diminishing effectiveness? Journal of Macroeconomics54, 217-231.

    Brigo, D., Buescu, C., & Rutkowski, M. (2017). Funding, repo and credit inclusive valuation as modified option pricing. Operations Research Letters45(6), 665-670.

    Briys, E., & De Varenne, F. (1997). Valuing risky fixed rate debt: An extension. Journal of Financial and Quantitative Analysis32(2), 239-248.

    Byström, H. (2019). Blockchains, real-time accounting, and the future of credit risk odeling. Ledger4 . https://doi.org/10.5195/ledger.2019.100

    Chau, F., Han, C., & Shi, S. (2018). Dynamics and determinants of credit risk discovery: Evidence from CDS and stock markets. International Review of Financial Analysis55, 156-169.

    Chen, J. (February 6, 2019). Can a normal firm value diffusion process improve the performance of the structural approach to pricing corporate liabilities? SSRN Electronic Journal. http://dx.doi.org/10.2139/ssrn.2738706

    Connelly, B. L., Certo, S. T., Ireland, R. D., & Reutzel, C. R. (2011). Signaling theory: A review and assessment. Journal of Management37(1), 39-67.

    Cont, R. (2006). Model uncertainty and its impact on the pricing of derivative instruments. Mathematical Finance16(3), 519-547.

    Das, S. R., & Tufano, P. (1995). Pricing credit sensitive debt when interest rates, credit ratings and credit spreads are stochastic. Journal of Financial Engineering, 50(2), 789–819.

    Dothan, M. (2006). Costs of financial distress and interest coverage ratios. Journal of Financial Research29(2), 147-162.

    Duffie, D., & Singleton, K. J. (1997). An econometric model of the term structure of interest‐rate swap yields. The Journal of Finance52(4), 1287-1321.

    Dupor, B. (2001). Investment and interest rate policy. Journal of Economic Theory98(1), 85-113.

    Feser, J. A., & Broby, D. (2020). The Determinants of Credit Default Swap Premia and the Use of Machine Learning Techniques for their Estimation. (pp. 1-26). University of Strathclyde 

    Geske, T. G., & Rossmiller, R. A. (1977). The politics of school fiscal reform in Wisconsin. Journal of education finance2(4), 513-532.

    Glasserman, P., & Xu, X. (2014). Robust risk measurement and model risk. Quantitative Finance14(1), 29-58.

     Marthinsen, J. (2018). Risk takers: uses and abuses of financial derivatives. Walter de Gruyter GmbH & Co KG.

    Gündüz, Y., & Uhrig-Homburg, M. (2011). Predicting credit default swap prices with financial and pure data-driven approaches. Quantitative Finance11(12), 1709-1727.

    Hull, J. (2009). Options, futures and the other derivatives. Prentice Hall.

    Jarrow, R. A., Lando, D., & Turnbull, S. M. (1997). A Markov model for the term structure of credit risk spreads. The Review of Financial Studies10(2), 481-523.

    Jarrow, R. A., & Turnbull, S. M. (1995). Pricing derivatives on financial securities subject to credit risk. The Journal of Finance50(1), 53-85.

    Ji, H. (2019). The impact of interest coverage ratio on value relevance of reported earnings: Evidence from South Korea. Sustainability11(24),  .  MDPI AG. Retrieved from http://dx.doi.org/10.3390/su1124719

    Jiang, S. J., Lei, M., & Chung, C. H. (2018). An improvement of gain-loss price bounds on options based on binomial tree and market-implied risk-neutral distribution. Sustainability10(6), 1942.

    Jing, J., Yan, W., & Deng, X. (2021). A hybrid model to estimate corporate default probabilities in China based on zero-price probability model and long short-term memory. Applied Economics Letters28(5), 413-420.

    Kim, I. J., Ramaswamy, K., & Sundaresan, S. (1993). Does default risk in coupons affect the valuation of corporate bonds? A contingent claim model. Financial Management, 22(3), 117- .

    Leippold, M., & Schärer, S. (2017). Discrete-time option pricing with stochastic liquidity. Journal of Banking & Finance75, 1-16.

    Leland, H. E. (1994). Corporate debt value, bond covenants, and optimal capital structure. The journal of finance49(4), 1213-1252

    Liang, J., Zhao, Y., & Zhang, X. (2016). Utility indifference valuation of corporate bond with credit rating migration by structure approach. Economic Modelling54, 339-346.

    Longstaff, F. A., & Schwartz, E. S. (1995). A simple approach to valuing risky fixed and floating rate debt. The Journal of Finance50(3), 789-819.

    Madan, D. B. (2010). Pricing and hedging basket options to prespecified levels of acceptability. Quantitative Finance10(6), 607-615.

    Madan, D. B., & Unal, H. (1998). Pricing the risks of default. Review of Derivatives Research2(2-3), 121-160.

    Majewski, A. A., Bormetti, G., & Corsi, F. (2015). Smile from the past: A general option pricing framework with multiple volatility and leverage components. Journal of Econometrics187(2), 521-531.

    Malik, H., Srivastava, S., Sood, Y. R., & Ahmad, A. (2018). Applications of artificial intelligence techniques in engineering. SIGMAvol 1 .

    Matkovskyy, R., & Bouraoui, T. (2019). Application of neural networks to short time series composite indexes: Evidence from the nonlinear autoregressive with exogenous inputs (NARX) model. Journal of Quantitative Economics17(2), 433-446.

    Merton, R. C. (1974). On the pricing of corporate debt: The risk structure of interest rates. The Journal of finance29(2), 449-470.

    Meissner, G. (2009). Credit derivatives: Application, pricing, and risk management. John Wiley & Sons.

    Mengle, D. (2007). Credit derivatives: An overview. Economic Review-Federal Reserve Bank of Atlanta92(4), -24 

    Morini, M. (2011). Understanding and managing model risk: A practical guide for quants, traders and validators. John Wiley & Sons.

    1-21. 

    Pavan, A., Segal, I., & Toikka, J. (2008). Dynamic mechanism design: Revenue equivalence, profit maximization and information disclosure , 82(2), 601-653

    Pennacchi, G. G. (2008). Theory of asset pricing. Pearson/Addison-Wesley.

    Raja, P., & Pahat, B. (2016). A review of training methods of ANFIS for applications in business and economics. International Journal of u-and e-Service, Science and Technology9(7), 165-172.

    Robinson, T. R., Henry, E., Pirie, W. L., and Broihahn, M. A. (2015). International financial statement analysis. John Wiley and Sons.

    Sariev, E., & Germano, G. (2020). Bayesian regularized artificial neural networks for the estimation of the probability of default. Quantitative Finance20(2), 311-328.

    Schöbel, R. (1999). A note on the valuation of risky corporate bonds. OR-Spektrum, 21(1-2), 35-47.

    Shen, F., Zhao, X., Lan, D., & Ou, L. (2017, July). A Hybrid Model of AdaBoost and Back-Propagation Neural Network for Credit Scoring. In International Conference on Management Science and Engineering Management (pp. 78-90). Springer.

    Cham.Šperanda, I., & Tršinski, Z. (2015). Hedging as a business risk protection instrument. Ekonomski Vjesnik: Review of Contemporary Entrepreneurship, Business, and Economic Issues28(2), 551-565.

    Stout, L. A. 2011b. Derivatives and the legal origin of the 2008 credit crisis. Harvard Business Law Review, 1(1): 1–38 

    Terzi, N., & Ulucay, K. (2011). The role of credit default swaps on financial market stability. Procedia-Social and Behavioral Sciences24, 983-990.

    Tucker, I. B. (2016). Microeconomics for today. Cengage Learning.

     

    1. Turfus. Analytic Pricing of Quanto CDS. Working Paper, ResearchGate, 2018c. URL https://www. researchgate.net/publication/325070862_Analytic_Pricing_of_Quanto_CDS.

    Uhrig-Homburg, M. (2002). Valuation of defaultable claims—A survey. Schmalenbach Business Review54(1), 24-57.

    Watson, M. (2007). Searching for the Kuhnian moment: The Black-Scholes-Merton formula and the evolution of modern finance theory. Economy and Society36(2), 325-337.

    Zhang, L., Hu, H., & Zhang, D. (2015). A credit risk assessment model based on SVM for small and medium enterprises in supply chain finance. Financial Innovation1(1), 1- 21 .