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Journal articles on the topic 'LGD (Loss Given Default)'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 February 2022

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1

Van Dyk, Jenni, Jaun Lange, and Gary Van Vuuren. "The Impact Of Systemic Loss Given Default On Economic Capital." International Business & Economics Research Journal (IBER) 16, no. 2 (March 31, 2017): 87–100. http://dx.doi.org/10.19030/iber.v16i2.9884.

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Empirical studies have demonstrated that loan default probabilities (PD) and loss given defaults (LGD) are positively correlated because of a common, business cycle, dependency. Regulatory capital requirements demand that banks use downturn LGD estimates because the correlation between PD and LGD is not captured. Economic capital models are not bound by this constraint. We extend and implement a model which captures the PD and LGD correlation by exploring the link between defaults and recoveries from a systemic point of view. We investigate the impact of correlated defaults and resultant loss rates on a portfolio comprising default-sensitive financial instruments. We demonstrate that the systemic component of recovery risk (driven by macroeconomic conditions) exerts greater influence on loss estimation and fair risk pricing than its standalone component.

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2

Li, Shouwei, and Jianmin He. "Loss distribution of systemic defaults in different interbank networks." International Journal of Modern Physics C 27, no. 10 (August 29, 2016): 1650121. http://dx.doi.org/10.1142/s0129183116501217.

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We analyze the impact of the network structure, the default probability and the loss given default (LGD) on the loss distribution of systemic defaults in the interbank market, where network structures analyzed include random networks, small-world networks and scale-free networks. We find that the network structure has little effect on the shape of the loss distribution, whereas the opposite is true to the default probability; the LGD changes the shape of the loss distribution significantly when default probabilities are high; the maximum of the possible loss is sensitive to the network structure and the LGD.

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3

Metzler, Adam, and Alexandre Scott. "Importance Sampling in the Presence of PD-LGD Correlation." Risks 8, no. 1 (March 10, 2020): 25. http://dx.doi.org/10.3390/risks8010025.

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This paper seeks to identify computationally efficient importance sampling (IS) algorithms for estimating large deviation probabilities for the loss on a portfolio of loans. Related literature typically assumes that realised losses on defaulted loans can be predicted with certainty, i.e., that loss given default (LGD) is non-random. In practice, however, LGD is impossible to predict and tends to be positively correlated with the default rate and the latter phenomenon is typically referred to as PD-LGD correlation (here PD refers to probability of default, which is often used synonymously with default rate). There is a large literature on modelling stochastic LGD and PD-LGD correlation, but there is a dearth of literature on using importance sampling to estimate large deviation probabilities in those models. Numerical evidence indicates that the proposed algorithms are extremely effective at reducing the computational burden associated with obtaining accurate estimates of large deviation probabilities across a wide variety of PD-LGD correlation models that have been proposed in the literature.

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Joubert, Morne, Tanja Verster, and Helgard Raubenheimer. "Making use of survival analysis to indirectly model loss given default." ORiON 34, no. 2 (January 14, 2019): 107–32. http://dx.doi.org/10.5784/34-2-588.

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A direct or indirect modelling methodology can be used to predict Loss Given Default (LGD). When using the indirect LGD methodology, two components exist, namely, the loss severity component and the probability component. Commonly used models to predict the loss severity and the probability component are the haircut- and the logistic regression models, respectively. In this article, survival analysis was proposed as an improvement to the more traditional logistic regression method. The mean squared error, bias and variance for the two methodologies were compared and it was shown that the use of survival analysis enhanced the model's predictive power. The proposed LGD methodology (using survival analysis) was applied on two simulated datasets and two retail bank datasets, and according to the results obtained it outperformed the logistic regression LGD methodology. Additional benefits included that the new methodology could allow for censoring as well as predicting probabilities over varying outcome periods.

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5

Breed, Douw Gerbrand, Tanja Verster, Willem D. Schutte, and Naeem Siddiqi. "Developing an Impairment Loss Given Default Model Using Weighted Logistic Regression Illustrated on a Secured Retail Bank Portfolio." Risks 7, no. 4 (December 13, 2019): 123. http://dx.doi.org/10.3390/risks7040123.

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This paper proposes a new method to model loss given default (LGD) for IFRS 9 purposes. We develop two models for the purposes of this paper—LGD1 and LGD2. The LGD1 model is applied to the non-default (performing) accounts and its empirical value based on a specified reference period using a lookup table. We also segment this across the most important variables to obtain a more granular estimate. The LGD2 model is applied to defaulted accounts and we estimate the model by means of an exposure weighted logistic regression. This newly developed LGD model is tested on a secured retail portfolio from a bank. We compare this weighted logistic regression (WLR) (under the assumption of independence) with generalised estimating equations (GEEs) to test the effects of disregarding the dependence among the repeated observations per account. When disregarding this dependence in the application of WLR, the standard errors of the parameter estimates are underestimated. However, the practical effect of this implementation in terms of model accuracy is found to be negligible. The main advantage of the newly developed methodology is the simplicity of this well-known approach, namely logistic regression of binned variables, resulting in a scorecard format.

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6

Van Vuuren, Gary, Riaan De Jongh, and Tanja Verster. "The Impact Of PD-LGD Correlation On Expected Loss And Economic Capital." International Business & Economics Research Journal (IBER) 16, no. 3 (June 30, 2017): 157–70. http://dx.doi.org/10.19030/iber.v16i3.9975.

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The Basel regulatory credit risk rules for expected losses require banks use downturn loss given default (LGD) estimates because the correlation between the probability of default (PD) and LGD is not captured, even though this has been repeatedly demonstrated by empirical research. A model is examined which captures this correlation using empirically-observed default frequencies and simulated LGD and default data of a loan portfolio. The model is tested under various conditions dictated by input parameters. Having established an estimate of the impact on expected losses, it is speculated that the model be calibrated using banks' own loss data to compensate for the omission of correlation dependence. Because the model relies on observed default frequencies, it could be used to adapt in real time, forcing provisions to be dynamically allocated.

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7

Shi, Baofeng, Xue Zhao, Bi Wu, and Yizhe Dong. "Credit rating and microfinance lending decisions based on loss given default (LGD)." Finance Research Letters 30 (September 2019): 124–29. http://dx.doi.org/10.1016/j.frl.2019.03.033.

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8

Sanches, Guilherme Fernandes, and André Alves Portela Santos. "Validação da perda dado o descumprimento na abordagem IRB avançada." Brazilian Review of Finance 14, no. 2 (June 27, 2016): 299. http://dx.doi.org/10.12660/rbfin.v14n2.2016.60908.

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The goal of our paper is to contribute to the discussion about the most important aspects of the loss given default validation process, with special attention to the brazilian case, as the Central Bank of Brazil determines in Circular no 3.648/2013. The authors suggest the application of a few non-linear statistical measures to the study of dependence between default frequency and loss given default, like Kendall ad Somers statistics and non-binary receiver operation characterisc (ROC). An estimation methodology for Downturn LGD is proposed, having as foundation a correlation adjustment derived from expected loss and ordination of quantiles of the forecasted LGD distribution according to the dependence level for different credit portfolios.

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9

Schneider, Paul, Leopold Sögner, and Tanja Veža. "The Economic Role of Jumps and Recovery Rates in the Market for Corporate Default Risk." Journal of Financial and Quantitative Analysis 45, no. 6 (September 17, 2010): 1517–47. http://dx.doi.org/10.1017/s0022109010000554.

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AbstractUsing an extensive cross section of U.S. corporate credit default swaps (CDSs), this paper offers an economic understanding of implied loss given default (LGD) and jumps in default risk. We formulate and underpin empirical stylized facts about CDS spreads, which are then reproduced in our affine intensity-based jump-diffusion model. Implied LGD is well identified, with obligors possessing substantial tangible assets expected to recover more. Sudden increases in the default risk of investment-grade obligors are higher relative to speculative grade. The probability of structural migration to default is low for investment-grade and heavily regulated obligors because investors fear distress rather through rare but devastating events.

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10

Orlando, Giuseppe, and Roberta Pelosi. "Non-Performing Loans for Italian Companies: When Time Matters. An Empirical Research on Estimating Probability to Default and Loss Given Default." International Journal of Financial Studies 8, no. 4 (November 9, 2020): 68. http://dx.doi.org/10.3390/ijfs8040068.

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Within bank activities, which is normally defined as the joint exercise of savings collection and credit supply, risk-taking is natural, as in many human activities. Among risks related to credit intermediation, credit risk assumes particular importance. It is most simply defined as the potential that a bank borrower or counterparty fails to fulfil correctly at maturity the pecuniary obligations assumed as principal and interest. Whenever this happens, a loan is non-performing. Among the main risk components, the Probability of Default (PD) and the Loss Given Default (LGD) have been the subject of greater interest for research. In this paper, logit model is used to predict both components. Financial ratios are used to estimate the PD. Time of recovery and presence of collateral are used as covariates of the LGD. Here, we confirm that the main driver of economic losses is the bureaucratically encumbered recovery system and the related legal environment. The long time required by Italian bureaucratic procedures, simply put, seems to lower dramatically the chance of recovery from defaulting counterparties.

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11

Hunt, Clive, and Ross Taplin. "Aggregation of Incidence and Intensity Risk Variables to Achieve Reconciliation." Risks 7, no. 4 (October 25, 2019): 107. http://dx.doi.org/10.3390/risks7040107.

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The aggregation of individual risks into total risk using a weighting variable multiplied by two ratio variables representing incidence and intensity is an important task for risk professionals. For example, expected loss (EL) of a loan is the product of exposure at default (EAD), probability of default (PD), and loss given default (LGD) of the loan. Simple weighted (by EAD) means of PD and LGD are intuitive summaries however they do not satisfy a reconciliation property whereby their product with the total EAD equals the sum of the individual expected losses. This makes their interpretation problematic, especially when trying to ascertain whether changes in EAD, PD, or LGD are responsible for a change in EL. We propose means for PD and LGD that have the property of reconciling at the aggregate level. Properties of the new means are explored, including how changes in EL can be attributed to changes in EAD, PD, and LGD. Other applications such as insurance where the incidence ratio is utilization rate (UR) and the intensity ratio is an average benefit (AB) are discussed and the generalization to products of more than two ratio variables provided.

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12

Kreienkamp, Tim, and Andrey Kateshov. "Credit Risk Modeling: Combining Classification And Regression Algorithms to Predict Expected Loss." Journal of Corporate Finance Research / Корпоративные Финансы | ISSN: 2073-0438 8, no. 4 (December 9, 2014): 4–10. http://dx.doi.org/10.17323/j.jcfr.2073-0438.8.4.2014.4-10.

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Credit risk assessment is of paramount importance in the financial industry. Machine learning techniques have been used successfully over the last two decades to predict the probability of loan default (PD). This way, credit decisions can be automated and risk can be reduced significantly. In the more recent parts, intensified regulatory requirements led to the need to include another parameter – loss given default (LGD), the share of the loan which cannot be recovered in case of loan default – in risk models. We aim to build a unified credit risk model by estimating both parameters jointly to estimate expected loss. A large, highdimensional, real world dataset is used to benchmark several combinations of classification, regression and feature selection algorithms. The results indicate that non-linear techniques work especially well to model expected loss.

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13

González, Marta Ramos, Antonio Partal Ureña, and Pilar Gómez Fernández-Aguado. "Regulatory Estimates for Defaulted Exposures: A Case Study of Spanish Mortgages." Mathematics 9, no. 9 (April 28, 2021): 997. http://dx.doi.org/10.3390/math9090997.

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The capital requirements derived from the Basel Accord were issued with the purpose of deploying a transnational regulatory framework. Further regulatory developments on risk measurement is included across several documents published both by the European Banking Authority and the European Central Bank. Among others, the referred additional documentation focused on the models’ estimation and calibration for credit risk measurement purposes, especially the Advanced Internal-Ratings Based models, which may be estimated both for non-defaulted and defaulted assets. A concrete proposal of the referred defaulted exposures models, namely the Expected Loss Best Estimate (ELBE) and the Loss Given Default (LGD) in-default, is presented. The proposed methodology is eventually calibrated on the basis of data from the mortgage’s portfolios of the six largest financial institutions in Spain. The outcome allows for a comparison of the risk profile particularities attached to each of the referred portfolios. Eventually, the economic sense of the results is analyzed.

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14

Cheng, Dan, and Pasquale Cirillo. "An Urn-Based Nonparametric Modeling of the Dependence between PD and LGD with an Application to Mortgages." Risks 7, no. 3 (July 7, 2019): 76. http://dx.doi.org/10.3390/risks7030076.

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We propose an alternative approach to the modeling of the positive dependence between the probability of default and the loss given default in a portfolio of exposures, using a bivariate urn process. The model combines the power of Bayesian nonparametrics and statistical learning, allowing for the elicitation and the exploitation of experts’ judgements, and for the constant update of this information over time, every time new data are available. A real-world application on mortgages is described using the Single Family Loan-Level Dataset by Freddie Mac.

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15

Leow, Mindy, and Christophe Mues. "Predicting loss given default (LGD) for residential mortgage loans: A two-stage model and empirical evidence for UK bank data." International Journal of Forecasting 28, no. 1 (January 2012): 183–95. http://dx.doi.org/10.1016/j.ijforecast.2011.01.010.

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16

Shi, Baofeng, Bin Meng, and Jing Wang. "An Optimal Decision Assessment Model Based on the Acceptable Maximum LGD of Commercial Banks and Its Application." Scientific Programming 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/9751243.

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This paper introduces a novel decision assessment method which is suitable for customers’ credit risk evaluation and credit decision. First of all, the paper creates an optimal credit rating model, and it consisted of an objective function and two constraint conditions. The first constraint condition of the strictly increasing LGDs eliminates the unreasonable phenomenon that the higher the credit rating is, the higher the LGD (loss given default) is. Secondly, on the basis of the credit rating results, a credit decision-making assessment model based on measuring the acceptable maximum LGD of commercial banks is established. Thirdly, empirical results using the data on 2817 farmers’ microfinance of a Chinese commercial bank suggest that the proposed approach can accurately find out the good customers from all the loan applications. Moreover, our approach contributes to providing a reference for decision assessment of customers in other commercial banks in the world.

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17

Volarević, Hrvoje, and Mario Varović. "Internal model for IFRS 9 - Expected credit losses calculation." Ekonomski pregled 69, no. 3 (June 21, 2018): 269–97. http://dx.doi.org/10.32910/ep.69.3.4.

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This article explores and analyzes the implementation problem of International Financial Reporting Standard 9 (IFRS 9) which is in use from 1 January 2018. IFRS 9 is most relevant for financial institutions, but also for all business subjects with a significant share of financial assets in their Balance sheet. The main objective of this article is the implementation of new impairment model for financial instruments, which is measurable through Expected Credit Losses (ECL). The use of this model is in correlation with a credit risk of the company for which it is necessary to determine basic variables of the model: Exposure at Default (EAD), Loss Given Default (LGD) and Probability of Default (PD). Basel legislation could be used for LGD calculation while PD calculation is based on specific methodology with two different solutions. In the first option, PD is taken as an external data from reliable rating agencies. When there is no external rating, an internal model for PD calculation has to be created. In order to develop an internal model, authors of this article propose application of multi-criteria decision-making model based on Analytic Hierarchy Process (AHP) method. Input data in the model are based on information from financial statements while MS Excel is used for calculation of such multi-criteria problem. Results of internal model are mathematically related with PD values for each analyzed company. Simple implementation of this internal model is an advantage compared to other much more complicated models.

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18

De Jongh, Riaan, Tanja Verster, Elzabe Reynolds, Morne Joubert, and Helgard Raubenheimer. "A Critical Review Of The Basel Margin Of Conservatism Requirement In A Retail Credit Context." International Business & Economics Research Journal (IBER) 16, no. 4 (October 2, 2017): 257–74. http://dx.doi.org/10.19030/iber.v16i4.10041.

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The Basel II accord (2006) includes guidelines to financial institutions for the estimation of regulatory capital (RC) for retail credit risk. Under the advanced Internal Ratings Based (IRB) approach, the formula suggested for calculating RC is based on the Asymptotic Risk Factor (ASRF) model, which assumes that a borrower will default if the value of its assets were to fall below the value of its debts. The primary inputs needed in this formula are estimates of probability of default (PD), loss given default (LGD) and exposure at default (EAD). Banks for whom usage of the advanced IRB approach have been approved usually obtain these estimates from complex models developed in-house. Basel II recognises that estimates of PDs, LGDs, and EADs are likely to involve unpredictable errors, and then states that, in order to avoid over-optimism, a bank must add to its estimates a margin of conservatism (MoC) that is related to the likely range of errors. Basel II also requires several other measures of conservatism that have to be incorporated. These conservatism requirements lead to confusion among banks and regulators as to what exactly is required as far as a margin of conservatism is concerned. In this paper, we discuss the ASRF model and its shortcomings, as well as Basel II conservatism requirements. We study the MoC concept and review possible approaches for its implementation. Our overall objective is to highlight certain issues regarding shortcomings inherent to a pervasively used model to bank practitioners and regulators and to potentially offer a less confusing interpretation of the MoC concept.

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19

Kapasný, Juraj, and Martin Řezáč. "Three-way ROC analysis using SAS Software." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 61, no. 7 (2013): 2269–75. http://dx.doi.org/10.11118/actaun201361072269.

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The most commonly used measure of model accuracy in medicine with three categories of target variable is the volume under ROC surface (VUS), which is the extension of the area under curve (AUC) for binary models (Le and Lili, 2013). This paper deals primarily with usage of the multinomial logistic regression and the three–way ROC analysis in the financial sector, especially in the credit risk management. Moreover, SAS system is very often used software in the financial sector. Therefore this paper is focused on ways of doing three–way ROC analysis in this statistical software, in particular on estimating the VUS.We propose an estimate of the VUS based on the confusion matrix, which is compared to estimates based on Mann-Whitney statistic and on empirical distribution functions. We developed three SAS macros based on these approaches for computing the VUS. Further- more, we developed some logistic models for three-value target variable based on the Loss Given Default (LGD). This was done on real financial data. Results obtained by the SAS macros on these models are presented a discussed in the paper.

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20

Pederson, Glenn, and Nicholas Sakaimbo. "Default and loss given default in agriculture." Agricultural Finance Review 71, no. 2 (August 2, 2011): 148–61. http://dx.doi.org/10.1108/00021461111152546.

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21

Wan, Zailong, and Ashish Dev. "Correlation between default events and loss given default and downturn loss given default in Basel II." Journal of Credit Risk 3, no. 4 (2007): 69–80. http://dx.doi.org/10.21314/jcr.2007.054.

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22

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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23

Li, Ke, Fanyin Zhou, Zhiyong Li, Xiao Yao, and Yashu Zhang. "Predicting loss given default using post-default information." Knowledge-Based Systems 224 (July 2021): 107068. http://dx.doi.org/10.1016/j.knosys.2021.107068.

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24

Frye, Jon, and Michael Jacobs. "Credit loss and systematic loss given default." Journal of Credit Risk 8, no. 1 (March 2012): 109–40. http://dx.doi.org/10.21314/jcr.2012.138.

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25

Miu, Peter, and Bogie Ozdemir. "Basel requirements of downturn loss given default: modeling and estimating probability of default and loss given default correlations." Journal of Credit Risk 2, no. 2 (2006): 43–68. http://dx.doi.org/10.21314/jcr.2006.037.

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26

Hurlin, Christophe, Jérémy Leymarie, and Antoine Patin. "Loss functions for Loss Given Default model comparison." European Journal of Operational Research 268, no. 1 (July 2018): 348–60. http://dx.doi.org/10.1016/j.ejor.2018.01.020.

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27

Bastos, João A. "Forecasting bank loans loss-given-default." Journal of Banking & Finance 34, no. 10 (October 2010): 2510–17. http://dx.doi.org/10.1016/j.jbankfin.2010.04.011.

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28

Hlawatsch, Stefan, and Sebastian Ostrowski. "Simulation and estimation of loss given default." Journal of Credit Risk 7, no. 3 (September 2011): 39–73. http://dx.doi.org/10.21314/jcr.2011.129.

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29

Farinelli, Simone, and Mykhaylo Shkolnikov. "Two models of stochastic loss given default." Journal of Credit Risk 8, no. 2 (June 2012): 3–20. http://dx.doi.org/10.21314/jcr.2012.141.

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30

Yashkir, Olga, and Yuri Yashkir. "Loss given default modeling: a comparative analysis." Journal of Risk Model Validation 7, no. 1 (March 2013): 25–59. http://dx.doi.org/10.21314/jrmv.2013.101.

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31

Frontczak, Robert, and Stefan Rostek. "Modeling loss given default with stochastic collateral." Economic Modelling 44 (January 2015): 162–70. http://dx.doi.org/10.1016/j.econmod.2014.10.006.

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32

Calabrese, Raffaella. "Downturn Loss Given Default: Mixture distribution estimation." European Journal of Operational Research 237, no. 1 (August 2014): 271–77. http://dx.doi.org/10.1016/j.ejor.2014.01.043.

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33

IJtsma, Pieter, and Laura Spierdijk. "Systemic risk with endogenous loss given default." Journal of Empirical Finance 44 (December 2017): 145–57. http://dx.doi.org/10.1016/j.jempfin.2017.09.012.

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34

Vujnovic, Milos, Nebojsa Nikolic, and Anja Vujnovic. "Validation of loss given default for corporate." Istrazivanja i projektovanja za privredu 14, no. 4 (2016): 465–76. http://dx.doi.org/10.5937/jaes14-11752.

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35

Wei, Li, and Zhongyi Yuan. "The loss given default of a low-default portfolio with weak contagion." Insurance: Mathematics and Economics 66 (January 2016): 113–23. http://dx.doi.org/10.1016/j.insmatheco.2015.10.005.

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36

Bade, Benjamin, Daniel Rösch, and Harald Scheule. "Empirical performance of loss given default prediction models." Journal of Risk Model Validation 5, no. 2 (June 2011): 25–44. http://dx.doi.org/10.21314/jrmv.2011.072.

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37

Garcia-Feijoo, Luis. "Modeling Ultimate Loss Given Default on Corporate Debt." CFA Digest 41, no. 4 (November 2011): 70–71. http://dx.doi.org/10.2469/dig.v41.n4.25.

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38

J. Jaber, Jamil, Noriszura Ismail, Siti Norafidah Mohd Ramli, Baker Al-badareen, and Nawaf N. Hamadneh. "Estimating Loss Given Default Based on Beta Regression." Computers, Materials & Continua 66, no. 3 (2021): 3329–44. http://dx.doi.org/10.32604/cmc.2021.014509.

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39

Bonini, Stefano, and Giuliana Caivano. "Estimating loss-given default through advanced credibility theory." European Journal of Finance 22, no. 13 (January 31, 2014): 1351–62. http://dx.doi.org/10.1080/1351847x.2013.870918.

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40

Jacobs, Michael, and Ahmet K. Karagozoglu. "Modeling Ultimate Loss Given Default on Corporate Debt." Journal of Fixed Income 21, no. 1 (June 30, 2011): 6–20. http://dx.doi.org/10.3905/jfi.2011.21.1.006.

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41

Yao, Xiao, Jonathan Crook, and Galina Andreeva. "Support vector regression for loss given default modelling." European Journal of Operational Research 240, no. 2 (January 2015): 528–38. http://dx.doi.org/10.1016/j.ejor.2014.06.043.

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42

Miller, Patrick, and Eugen Töws. "Loss given default adjusted workout processes for leases." Journal of Banking & Finance 91 (June 2018): 189–201. http://dx.doi.org/10.1016/j.jbankfin.2017.01.020.

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43

Qi, Min, and Xinlei Zhao. "Comparison of modeling methods for Loss Given Default." Journal of Banking & Finance 35, no. 11 (November 2011): 2842–55. http://dx.doi.org/10.1016/j.jbankfin.2011.03.011.

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44

Van Damme, Geert. "A generic framework for stochastic Loss-Given-Default." Journal of Computational and Applied Mathematics 235, no. 8 (February 2011): 2523–50. http://dx.doi.org/10.1016/j.cam.2010.11.006.

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45

Loterman, Gert, Iain Brown, David Martens, Christophe Mues, and Bart Baesens. "Benchmarking regression algorithms for loss given default modeling." International Journal of Forecasting 28, no. 1 (January 2012): 161–70. http://dx.doi.org/10.1016/j.ijforecast.2011.01.006.

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46

Olson, Luke M., Min Qi, Xiaofei Zhang, and Xinlei Zhao. "Machine learning loss given default for corporate debt." Journal of Empirical Finance 64 (December 2021): 144–59. http://dx.doi.org/10.1016/j.jempfin.2021.08.009.

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47

Conrad, Jennifer, Robert F. Dittmar, and Allaudeen Hameed. "Implied Default Probabilities and Losses Given Default from Option Prices*." Journal of Financial Econometrics 18, no. 3 (2020): 629–52. http://dx.doi.org/10.1093/jjfinec/nbaa017.

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Abstract We propose a novel method of estimating default probabilities using equity options data. The resulting default probabilities are highly correlated with estimates of default probabilities extracted from CDS spreads, which assume constant losses given default. Additionally, the option-implied default probabilities are higher in bad economic times and for firms with poorer credit ratings and financial positions. A simple inferred measure of loss given default is related to underlying business conditions, and varies across sectors; the time series properties of this measure are similar after controlling for liquidity effects.

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48

Huajian Yang, Bill, and Mykola Tkachenko. "Modeling exposure at default and loss given default: empirical approaches and technical implementation." Journal of Credit Risk 8, no. 2 (June 2012): 81–102. http://dx.doi.org/10.21314/jcr.2012.139.

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Porto, Rogério. "A brief note on implied historical loss given default." Journal of Credit Risk 7, no. 2 (June 2011): 73–81. http://dx.doi.org/10.21314/jcr.2011.124.

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Huang, Xinzheng, and Cornelis Oosterlee. "Generalized beta regression models for random loss-given-default." Journal of Credit Risk 7, no. 4 (December 2011): 45–70. http://dx.doi.org/10.21314/jcr.2011.150.

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