Sequential Importance Sampling and Resampling for Dynamic Portfolio Credit Risk

Deng, Shaojie; Giesecke, Kay; Lai, Tze Leung

doi:10.1287/opre.1110.1008

Cited by 13 publications

(3 citation statements)

References 28 publications

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“…Another line of research on modeling of portfolio credit risk focuses on dynamic intensity-based point process models. For such models, Deng et al [2012] recently proposed a sequential importance sampling and resampling scheme for estimating rare-event probabilities. They showed that a logarithmically efficient estimator of the probability of large loss can be obtained by selecting appropriate resampling weights.…”

Section: Credit Portfoliosmentioning

confidence: 99%

Monte Carlo Methods for Value-at-Risk and Conditional Value-at-Risk

Liu

2014

ACM Trans. Model. Comput. Simul.

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Value-at-risk (VaR) and conditional value-at-risk (CVaR) are two widely used risk measures of large losses and are employed in the financial industry for risk management purposes. In practice, loss distributions typically do not have closed-form expressions, but they can often be simulated (i.e., random observations of the loss distribution may be obtained by running a computer program). Therefore, Monte Carlo methods that design simulation experiments and utilize simulated observations are often employed in estimation, sensitivity analysis, and optimization of VaRs and CVaRs. In this article, we review some of the recent developments in these methods, provide a unified framework to understand them, and discuss their applications in financial risk management.

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Section: Credit Portfoliosmentioning

confidence: 99%

Monte Carlo Methods for Value-at-Risk and Conditional Value-at-Risk

Liu

2014

ACM Trans. Model. Comput. Simul.

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“…There is a large literature on modelling PD-LGD correlation (Frye 2000); (Pykhtin 2003); (Miu and Ozdemir 2006); (Kupiec 2008); (Sen 2008); (Witzany 2011); (de Wit 2016); (Eckert et al 2016); and others listed in (Frye and Jacobs 2012), but there is a much smaller literature on using IS to estimate large deviation probabilities in such models. To the best of our knowledge only (Deng et al 2012) and (Jeon et al 2017) have developed algorithms that allow for PD-LGD correlation (the former paper considers a dynamic intensity-based framework, the latter considers a static model with asymmetric and heavy-tailed risk factors). The present paper contributes to this nascent literature by developing algorithms that can be applied in a wide variety of PD-LGD correlation models that have been proposed in the literature, and are popular in practice.…”

Section: Problem Formulation and Related Literaturementioning

confidence: 99%

Importance Sampling in the Presence of PD-LGD Correlation

Metzler

Scott

2020

Risks

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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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“…Since typically it is hard to compute the loss distribution, approximations through limit theory or simulation are called for. For example, Deng, Giesecke and Lai [8] propose an importance sampling technique to estimate rare events probabilities in a credit risk portfolio. The method is based on a change of measure and resampling to approximate the zero-variance importance measure connected to the rare events.…”

Section: Introductionmentioning

confidence: 99%

Computing Optimal Recovery Policies for Financial Markets

Benth

Dahl

Mannino

2012

Operations Research

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The current nancial crisis motivates the study of correlated defaults in nancial systems. In this paper we focus on such a model which is based on Markov random elds. This is a probabilistic model where uncertainty in default probabilities incorporates expert's opinions on the default risk (based on various credit ratings). We consider a bilevel optimization model for nding an optimal recovery policy: which companies should be supported given a xed budget. This is closely linked to the problem of nding a maximum likelihood estimator of the defaulting set of agents, and we show how to compute this solution e ciently using combinatorial methods. We also prove properties of such optimal solutions. A practical procedure for estimation of model parameters is also given. Computational examples are presented and experiments indicate that our methods can nd optimal recovery policies for up to about 100 companies. The overall approach is evaluated on a real-world problem concerning the major banks in Scandinavia and public loans. To our knowledge this is a rst attempt to apply combinatorial optimization techniques to this important, and expanding, area of default risk analysis.

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Sequential Importance Sampling and Resampling for Dynamic Portfolio Credit Risk

Cited by 13 publications

References 28 publications

Monte Carlo Methods for Value-at-Risk and Conditional Value-at-Risk

Monte Carlo Methods for Value-at-Risk and Conditional Value-at-Risk

Importance Sampling in the Presence of PD-LGD Correlation

Computing Optimal Recovery Policies for Financial Markets

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