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Journal articles on the topic 'Conditional probability of default'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

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1

Capozza, Dennis R., Dick Kazarian, and Thomas A. Thomson. "The Conditional Probability of Mortgage Default." Real Estate Economics 26, no. 3 (1998): 259–89. http://dx.doi.org/10.1111/1540-6229.00750.

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2

CHELLATHURAI, THAMAYANTHI. "PROBABILITY DENSITY OF RECOVERY RATE GIVEN DEFAULT OF A FIRM’S DEBT AND ITS CONSTITUENT TRANCHES." International Journal of Theoretical and Applied Finance 20, no. 04 (2017): 1750023. http://dx.doi.org/10.1142/s0219024917500236.

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This paper derives the theoretical underpinnings behind the following observed empirical facts in credit risk modeling: The probability of default, the seniority, the thickness of the tranche, the debt cushion, and macroeconomic factors are the important determinants of the conditional probability density function of the recovery rate given default (RGD) of a firm’s debt and its tranches. In a portfolio of debt securities, the conditional probability density functions of the recovery rate given default of tranches have point probability masses near zero and one, and the expected value of the recovery rate given default increases as the seniority or debt cushion increases. The paper derives other results as well, such as the fact that the conditional probability distribution function associated with any senior tranche dominates that of any junior tranche by first-order. The standard deviation of the recovery rate given default of a senior security need not be greater than that of a junior security. It is proved that the expected value of the recovery rate given default need not increase as the proportional thickness of the tranche increases.

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3

Lucas, André, Bernd Schwaab, and Xin Zhang. "Conditional Euro Area Sovereign Default Risk." Journal of Business & Economic Statistics 32, no. 2 (2014): 271–84. http://dx.doi.org/10.1080/07350015.2013.873540.

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4

Đurović, Andrija. "Macroeconomic Approach to Point in Time Probability of Default Modeling – IFRS 9 Challenges." Journal of Central Banking Theory and Practice 8, no. 1 (2019): 209–23. http://dx.doi.org/10.2478/jcbtp-2019-0010.

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Abstract This paper aims to present one possible retail estimation framework of lifetime probability of default in accordance with IFRS 9. The framework rests on “term structure of probability of default” conditional to given forward-looking macroeconomic dynamics. Due to the one of the biggest limitation of forward-looking modelling – data availability, model averaging technique for quantification of macroeconomic effect on default probability is explained.

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5

Bo, Lijun, Dan Tang, Yongjin Wang, and Xuewei Yang. "On the conditional default probability in a regulated market: a structural approach." Quantitative Finance 11, no. 12 (2010): 1695–702. http://dx.doi.org/10.1080/14697680903473278.

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6

Bo, Lijun, Xindan Li, Yongjin Wang, and Xuewei Yang. "On the conditional default probability in a regulated market with jump risk." Quantitative Finance 13, no. 12 (2013): 1967–75. http://dx.doi.org/10.1080/14697688.2013.815795.

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7

Ayomi, Sri, and Bambang Hermanto. "MENGUKUR RISIKO SISTEMIK DAN KETERKAITAN FINANSIAL PERBANKAN DI INDONESIA." Buletin Ekonomi Moneter dan Perbankan 16, no. 2 (2014): 103–25. http://dx.doi.org/10.21098/bemp.v16i2.24.

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This paper measures the insolvency risk of bank in Indonesia. We apply Merton model to identify the probability of defaul tover 30 banks during the period of 2002-2013. This paper also identify role of financial linkage a cross banks on transmitting from one bank to another; which enable us to assess if the risk is systemic or not. The results showed the larger total asset of the bank, the larger they contribute to systemic risk. Keywords : Conditional Value at Risk; Probability of Default; systemic risk and financial linkages;Value at Risk. JEL Classification: D81, G21, G33

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8

Ayomi, Sri, and Bambang Hermanto. "SYSTEMIC RISK AND FINANCIAL LINKAGES MEASUREMENT IN THE INDONESIAN BANKING." Buletin Ekonomi Moneter dan Perbankan 16, no. 2 (2014): 91–114. http://dx.doi.org/10.21098/bemp.v16i2.439.

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This paper measures the insolvency risk of bank in Indonesia. We apply Merton model to identify the probability of defaul tover 30 banks during the period of 2002-2013. This paper also identify role of financial linkage a cross banks on transmitting from one bank to another; which enable us to assess if the risk is systemic or not. The results showed the larger total asset of the bank, the larger they contribute to systemic risk. Keywords : Conditional Value at Risk; Probability of Default; systemic risk and financial linkages;Value at Risk.JEL Classification: D81, G21, G33

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9

Chen, Jun, and Yongheng Deng. "Commercial Mortgage Workout Strategy and Conditional Default Probability: Evidence from Special Serviced CMBS Loans." Journal of Real Estate Finance and Economics 46, no. 4 (2012): 609–32. http://dx.doi.org/10.1007/s11146-012-9374-z.

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10

Sarmiento, Camilo. "A Conditional Probability of Default Under The Influence of Both Systematic And Idiosyncratic Components." International Journal of Economics and Management Studies 7, no. 12 (2020): 43–46. http://dx.doi.org/10.14445/23939125/ijems-v7i12p106.

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11

Ambrose, Brent W., and Charles A. Capone. "Modeling the Conditional Probability of Foreclosure in the Context of Single-Family Mortgage Default Resolutions." Real Estate Economics 26, no. 3 (1998): 391–429. http://dx.doi.org/10.1111/1540-6229.00751.

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12

Stefano, Olgiati, and Danovi Alessandro. "ZETA™ Methodology and Variation in the Systemic Risk of Default: Accounting for the Effects of Type II (False Negative) Errors Variation on Lending." Journal of Corporate Finance Research / Корпоративные Финансы | ISSN: 2073-0438 9, no. 1 (2015): 71–81. http://dx.doi.org/10.17323/j.jcfr.2073-0438.9.1.2015.71-81.

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Olgiati Stefano - University of Bergamo, Department of Management, Economics and Quantitative Methods.  Danovi Alessandro - University of Bergamo The loan manager - dealing with one single borrower at a time and being responsible for that single decision to lend - is exposed to the idiosyncratic risk of default of his customer just like the physician is exposed to the risk of a wrong diagnosis with our strep throat. At the same time – if we do not expect the strep throat diagnostic test kit to change - we would still expect that physician reading that test to become more careful – or update his prior beliefs – about his diagnoses when a flu epidemic is likely to kick in with a certain estimated probability (likelihood). However, this has not been the case with loan management - there is in fact some consensus that before 2007 a reduction in the standards of idiosyncratic risk assessment by lenders - prior to risks pooling - coupled with a worsening of the systemic risk scenario, is partly to blame for the well known 2007-2008 financial crisis, with some of the blame falling also on the incapacity of actuarial mathematical models (test kits) to update worst case scenarios or be calibrated continuously on the basis of variation in the likelihood of default of the underlying risks pool.The authors of this paper argue that, on the other hand, a standard Bayesian transformation of the ZETA bankruptcy prediction methodology introduced by Altman in 1968-1977 allows for a continuous a posterioriupdate of conditional Type I and II errors due to variation in the systemic likelihood of default. The Bayesian transformation can be used both to condition the loan manager’s prior decision (generally based on Basel II-compliant Internal Rating Based system or Credit Agency’s Rating) and to update such decision on the basis of any posterior hypothesis (based on actuarial frequentist assumptions of conditional hazard rates) regarding the creditworthiness and the probability of default of an underlying pool of securities.At the same time – under a Bayesian framework - the ZETA diagnostic test can be conditioned on the new evidence introduced by other tests to increase the total sensitivity of the default prediction models (IRB ratings, TTC ratings, logit, probit, neural) to update the commercial bank’s lending decisions.A ground-state, static meta-analysis of Altman’s et al. ZETA original article (1977) reveals that the odds of the commercial bank detecting a default after the ZETA score has been introduced (post-test) is 13.2 times more effective than the a priori prediction. Under the same assumptions, the odds of the commercial bank detecting a survival after (post-test) the ZETA score has been introduced is 12.2 times more effective than the a priori. Integration of the ZETA model with other default prediction models reaches a credibility interval of CI ≥ 95% when the updated likelihood of default is equal to 60%. As expected, the Efficiency Comparison Test ECZETA=.00243 is invariant under the Bayesian transformation.

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13

STRAND, JON. "Developing-country resource extraction with asymmetric information and sovereign debt: a theoretical analysis." Environment and Development Economics 2, no. 3 (1997): 265–89. http://dx.doi.org/10.1017/s1355770x9700003x.

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We consider a two-period model of an indebted developing country endowed with a natural resource whose extraction causes negative global externalities, where the country may borrow in period one and there is asymmetric information about its willingness to service its loans. We show that when the resource is large, the interest rate on new borrowing equals the resource growth rate. A greater initial debt level then leads to reduced new borrowing and more rapid extraction. An outside 'donor' may affect the resource extraction of the country. Donor schemes that tie debt reduction to postponing or abstaining from extraction of the resource are more powerful than non-conditional schemes in reducing the extraction rate for governments that actually repay, but may in some cases lead to a greater probability of default through increased debt. While conditional schemes generally are potentially Pareto-superior to non-conditional ones, the welfare of the borrowing country is higher with non-conditional schemes.

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14

Leijdekker, Vincent, and Peter Spreij. "EXPLICIT COMPUTATIONS FOR A FILTERING PROBLEM WITH POINT PROCESS OBSERVATIONS WITH APPLICATIONS TO CREDIT RISK." Probability in the Engineering and Informational Sciences 25, no. 3 (2011): 393–418. http://dx.doi.org/10.1017/s0269964811000076.

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We consider the filtering problem for a doubly stochastic Poisson or Cox process, where the intensity follows the Cox–Ingersoll–Ross model. In this article we assume that the Brownian motion, which drives the intensity, is not observed. Using filtering theory for point process observations, we first derive the dynamics for the intensity and its moment-generating function, given the observations of the Cox process. A transformation of the dynamics of the conditional moment-generating function allows us to solve in closed form the filtering problem, between the jumps of the Cox process as well as at the jumps, which constitutes the main contribution of the article. Assuming that the initial distribution of the intensity is of the Gamma type, we obtain an explicit solution to the filtering problem for all t>0. We conclude the article with the observation that the resulting conditional moment-generating function at time t, after Nt jumps, corresponds to a mixture of Nt+1 Gamma distributions. Currently, the model that we analyze has become popular in credit risk modeling, where one uses the intensity-based approach for the modeling of default times of one or more companies. In this approach, the default times are defined as the jump times of a Cox process. In such a model, one only has access to observations of the Cox process, and thus filtering comes in as a natural technique in credit risk modeling.

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15

Kim, Jungmu, Yuen Jung Park, and Doojin Ryu. "Hawkes-diffusion process and the conditional probability of defaults in the Eurozone." Physica A: Statistical Mechanics and its Applications 449 (May 2016): 301–10. http://dx.doi.org/10.1016/j.physa.2015.12.087.

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16

CAPPONI, AGOSTINO, and JAKŠA CVITANIĆ. "CREDIT RISK MODELING WITH MISREPORTING AND INCOMPLETE INFORMATION." International Journal of Theoretical and Applied Finance 12, no. 01 (2009): 83–112. http://dx.doi.org/10.1142/s0219024909005129.

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We propose a structural model for the valuation of defaultable securities of a firm which models the effect of deliberate misreporting done by insiders in the firm and unobserved by others. We derive exact formulas for equity and bond prices and approximate expressions for the conditional default probability, recovery rate, and credit spread under the proposed credit risk framework. We propose a novel estimation approach to structural model estimation which accounts for noisy observed asset values. We apply the proposed method to calibrate a simple version of our model to the case of Parmalat and show that the model is able to recover a certain amount of misreporting during the years of accounting irregularities.

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17

VRINS, FRÉDÉRIC. "WRONG-WAY RISK CVA MODELS WITH ANALYTICAL EPE PROFILES UNDER GAUSSIAN EXPOSURE DYNAMICS." International Journal of Theoretical and Applied Finance 20, no. 07 (2017): 1750045. http://dx.doi.org/10.1142/s0219024917500455.

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We consider two classes of wrong-way risk models in the context of CVA: static (resampling) and dynamic (reduced form). Although both potentially suffer from arbitrage problems, their tractability makes them appealing to the industry and therefore deserve additional study. For example, Gaussian copula-based resampling and reduced-form with “Hull–White intensities” yield analytical expected positive exposure (EPE) profiles when the portfolio price process (i.e. exposure process) is Gaussian. However, the first approach disregards credit volatility whilst the second can provide default probabilities larger than 1. We therefore enlarge the study by introducing a new dynamic approach for credit risk, consisting in the straight modeling of the survival (Azéma supermartingale) process using the [Formula: see text]-martingale. This method is appealing in that it helps fixing some drawbacks of the above models. Indeed, it is a dynamic method (it disentangles correlation and credit volatility) that preserves probabilities in [Formula: see text] without affecting the analytical tractability of the model. In particular, calibration to any valid default probability curve is automatic and the closed-form expression for the EPE profiles remains available under Gaussian exposures. For each approach, we derive analytically the EPE profiles (conditional upon default) associated to prototypical exposure processes of Forward Rate Agreement (FRA) and Interest Rate Swap (IRS) in all cases and provide a comparison and discuss the implied Credit Valuation Adjustment (CVA) figures.

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18

Phillips, Richard A., and James H. VanderHoff. "The Conditional Probability of Foreclosure: An Empirical Analysis of Conventional Mortgage Loan Defaults." Real Estate Economics 32, no. 4 (2004): 571–87. http://dx.doi.org/10.1111/j.1080-8620.2004.00103.x.

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19

Chun, So Yeon, and Miguel A. Lejeune. "Risk-Based Loan Pricing: Portfolio Optimization Approach with Marginal Risk Contribution." Management Science 66, no. 8 (2020): 3735–53. http://dx.doi.org/10.1287/mnsc.2019.3378.

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We consider a lender (bank) that determines the optimal loan price (interest rate) to offer to prospective borrowers under uncertain borrower response and default risk. A borrower may or may not accept the loan at the price offered, and both the principal loaned and the interest income become uncertain because of the risk of default. We present a risk-based loan pricing optimization framework that explicitly takes into account the marginal risk contribution, the portfolio risk, and a borrower’s acceptance probability. Marginal risk assesses the incremental risk contribution of a prospective loan to the bank’s overall portfolio risk by capturing the dependencies between the prospective loan and the existing portfolio and is evaluated with respect to the value-at-risk and conditional value-at-risk measures. We examine the properties and computational challenges of the formulations. We design a reformulation method based on the concavifiability concept to transform the nonlinear objective functions and to derive equivalent mixed-integer nonlinear reformulations with convex continuous relaxations. We also extend the approach to multiloan pricing problems, which feature explicit loan selection decisions in addition to pricing decisions. We derive formulations with multiple loans that take the form of mixed-integer nonlinear problems with nonconvex continuous relaxations and develop a computationally efficient algorithmic method. We provide numerical evidence demonstrating the value of the proposed framework, test the computational tractability, and discuss managerial implications. This paper was accepted by Chung Piaw Teo, optimization.

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20

Sugiarto, Sugiarto, and Suroso Suroso. "Innovation of impairment loss allowance model of Indonesian financial accounting standards 71." Journal of Asian Business and Economic Studies 27, no. 3 (2020): 267–83. http://dx.doi.org/10.1108/jabes-11-2019-0114.

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PurposeThis study aims to develop a high-quality impairment loss allowance model in conformity with Indonesian Financial Accounting Standards 71 (PSAK 71) that has significant contribution to national interests and the banking industry.Design/methodology/approachThe determination of the impairment loss allowance model is settled through 7 stages, using integration of some statistical methods such as Markov chain, exponential smoothing, time series analysis of behavioral inherent trends of probability of default, tail conditional expectation and Monte Carlo simulation.FindingsThe model which is developed by the authors is proven to be a high-quality and reliable model. By using the model, it can be shown that the implementation of the expected credit losses model on Indonesian Financial Accounting Standards 71 is more prudent than the implementation of the incurred loss model on Indonesian Financial Accounting Standards 55.Research limitations/implicationsDetermination of defaults was based on days past due, and the analysis in this study did not touch the aspects of hedge accounting in general.Practical implicationsThis developed model will contribute significantly to national interests as a source of reference for other banks operating in Indonesia in calculating impairment loss allowance (CKPN) and can be used by the Financial Services Authority of Indonesia (OJK) as a guideline in assessing the formation of impairment loss allowance for banks operating in Indonesia.Originality/valueAs so far there is not yet an available standardized model for calculating impairment loss allowance on the basis of Indonesian Financial Accounting Standards 71, the model developed by the authors will be a new breakthrough in Indonesia.

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21

Osherson, Daniel N., Joshua Stern, Ormond Wilkie, Michael Stob, and Edward E. Smith. "Default Probability." Cognitive Science 15, no. 2 (1991): 251–69. http://dx.doi.org/10.1207/s15516709cog1502_3.

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22

Voorbraak, Frans. "Default Reasoning: Causal and Conditional Theories." AI Communications 7, no. 1 (1994): 66–67. http://dx.doi.org/10.3233/aic-1994-7107.

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23

Li, Weiping. "Probability of Default and Default Correlations." Journal of Risk and Financial Management 9, no. 3 (2016): 7. http://dx.doi.org/10.3390/jrfm9030007.

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24

Shafer, Glenn. "Conditional Probability." International Statistical Review / Revue Internationale de Statistique 53, no. 3 (1985): 261. http://dx.doi.org/10.2307/1402890.

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25

Janga, Sunitha, and Son iya.K. "Conditional Probability." International Journal of Mathematics Trends and Technology 58, no. 1 (2018): 11–15. http://dx.doi.org/10.14445/22315373/ijmtt-v58p502.

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26

LOWE, E. J. "Conditional Probability and Conditional Beliefs." Mind 105, no. 420 (1996): 603–15. http://dx.doi.org/10.1093/mind/105.420.603.

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27

Dzidzevičiūtė, Laima. "ESTIMATION OF DEFAULT PROBABILITY FOR LOW DEFAULT PORTFOLIOS." Ekonomika 91, no. 1 (2012): 132–56. http://dx.doi.org/10.15388/ekon.2012.0.902.

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This article presents several approaches to estimating the probabilities of default for low default portfolios, their advantages and disadvantages, and provides exemplary calculations using data of one external credit register of Lithuania. The results show that three approaches seem to be most appropriate: those of K. Pluto and D. Tasche (2005) without correlation, and those of N. M. Kiefer (2006) and A. Forrest (2005) without correlation. The first one could be easily implemented by banks; however, if the ordinal ranking of obligors is incorrect, then the monotony of probabilities of default is not ensured. The same problem exists with the second approach. The A. Forrest (2005) approach without correlation ensures the monotony of default probabilities and allows estimating conservative PDs; however, it requires programming skills, otherwise iterative recalculation will be very time-consuming.

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28

Misankova, Maria, Erika Spuchľakova, and Katarina Frajtova –. Michalikova. "Determination of Default Probability by Loss Given Default." Procedia Economics and Finance 26 (2015): 411–17. http://dx.doi.org/10.1016/s2212-5671(15)00815-1.

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29

Luo, Lawrence. "Bootstrapping default probability curves." Journal of Credit Risk 1, no. 4 (2005): 169–79. http://dx.doi.org/10.21314/jcr.2005.028.

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30

Pargetter, Robert. "CONDITIONAL EPISTEMIC PROBABILITY." Southern Journal of Philosophy 26, no. 4 (1988): 555–71. http://dx.doi.org/10.1111/j.2041-6962.1988.tb02164.x.

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31

Tomlinson, Stephen, and Robert Quinn. "Understanding Conditional Probability." Teaching Statistics 19, no. 1 (1997): 2–7. http://dx.doi.org/10.1111/j.1467-9639.1997.tb00309.x.

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32

Schwarz, Wolfgang. "Subjunctive Conditional Probability." Journal of Philosophical Logic 47, no. 1 (2016): 47–66. http://dx.doi.org/10.1007/s10992-016-9416-8.

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33

Friedman, Nir, Joseph Y. Halpern, and Daphne Koller. "First-order conditional logic for default reasoning revisited." ACM Transactions on Computational Logic 1, no. 2 (2000): 175–207. http://dx.doi.org/10.1145/359496.359500.

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34

Geffner, Hector, and Judea Pearl. "Conditional entailment: Bridging two approaches to default reasoning." Artificial Intelligence 53, no. 2-3 (1992): 209–44. http://dx.doi.org/10.1016/0004-3702(92)90071-5.

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35

Asher, Nicholas. "A default, truth conditional semantics for the progressive." Linguistics and Philosophy 15, no. 5 (1992): 463–508. http://dx.doi.org/10.1007/bf00630628.

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36

Blümke, Oliver. "Estimating the probability of default for no‐default and low‐default portfolios." Journal of the Royal Statistical Society: Series C (Applied Statistics) 69, no. 1 (2019): 89–107. http://dx.doi.org/10.1111/rssc.12381.

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37

Halpern, Joseph Y. "Lexicographic probability, conditional probability, and nonstandard probability." Games and Economic Behavior 68, no. 1 (2010): 155–79. http://dx.doi.org/10.1016/j.geb.2009.03.013.

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38

Prakasa Rao, B. L. S. "Conditional independence, conditional mixing and conditional association." Annals of the Institute of Statistical Mathematics 61, no. 2 (2007): 441–60. http://dx.doi.org/10.1007/s10463-007-0152-2.

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39

., P. K. Tripathy, and Anima Bag. "Decision Support Model with Default Risk under Conditional Delay." International Journal of Scientific Research in Mathematical and Statistical Sciences 5, no. 2 (2018): 40–45. http://dx.doi.org/10.26438/ijsrmss/v5i2.4045.

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40

El Karoui, Nicole, Monique Jeanblanc, and Ying Jiao. "What happens after a default: The conditional density approach." Stochastic Processes and their Applications 120, no. 7 (2010): 1011–32. http://dx.doi.org/10.1016/j.spa.2010.02.003.

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41

Gilio, Angelo. "Probabilistic Logic Under Coherence, Conditional Interpretations, and Default Reasoning." Synthese 146, no. 1-2 (2005): 139–52. http://dx.doi.org/10.1007/s11229-005-9080-y.

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42

Sotos, Francisco Escribano. "Interest risk and default risk: A conditional volatility study." International Advances in Economic Research 9, no. 1 (2003): 56–63. http://dx.doi.org/10.1007/bf02295301.

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43

Demir, Hilmi. "What is Conditional Probability?" kilikya 3, no. 2 (2016): 1–15. http://dx.doi.org/10.5840/kilikya2016327.

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44

Evans, Jonathan St B. T., Simon J. Handley, and David E. Over. "Conditionals and conditional probability." Journal of Experimental Psychology: Learning, Memory, and Cognition 29, no. 2 (2003): 321–35. http://dx.doi.org/10.1037/0278-7393.29.2.321.

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45

Guerra Bobo, Isabel. "On Quantum Conditional Probability." THEORIA. An International Journal for Theory, History and Foundations of Science 28, no. 1 (2013): 115–37. http://dx.doi.org/10.1387/theoria.5682.

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46

Lowe, E. J. "What is ‘conditional probability’?" Analysis 68, no. 299 (2008): 218–23. http://dx.doi.org/10.1111/j.1467-8284.2008.00741.x.

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47

Krakau, C. E. T. "Conditional probability and ophthalmology." Acta Ophthalmologica 66, no. 6 (2009): 609–11. http://dx.doi.org/10.1111/j.1755-3768.1988.tb04048.x.

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48

EDGINGTON, D. "Lowe on Conditional Probability." Mind 105, no. 420 (1996): 617–30. http://dx.doi.org/10.1093/mind/105.420.617.

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49

Watanabe, S. "Conditional Probability in Physics." Progress of Theoretical Physics Supplement E65 (May 16, 2013): 135–60. http://dx.doi.org/10.1143/ptps.e65.135.

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50

Lowe, E. J. "What is 'conditional probability'?" Analysis 68, no. 3 (2008): 218–23. http://dx.doi.org/10.1093/analys/68.3.218.

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