Smile and default: the role of stochastic volatility and interest rates in counterparty credit risk

Simaitis, Sarunas; Graaf, Cornelis S. L. de; Hari, N.; Kandhai, Drona

doi:10.1080/14697688.2016.1176240

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…In the case of expected exposure, it appears that the two-dimensional decomposed approximation is already close to the full-scale Monte Carlo solution. This is in line with Simaitis et al (2016), where it is found that for expected exposure, a time dependent volatility function can be used, whereas only for non-linear exposures (such as expected positive or negative exposure), a full stochastic volatility model should be used.…”

Section: Stochastic Volatilitysupporting

confidence: 70%

Efficient exposure computation by risk factor decomposition

Graaf

Kandhai

Reisinger

2018

Quantitative Finance

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The focus of this paper is the efficient computation of counterparty credit risk exposure on portfolio level. Here, the large number of risk factors rules out traditional PDE-based techniques and allows only a relatively small number of paths for nested Monte Carlo simulations, resulting in large variances of estimators in practice. We propose a novel approach based on Kolmogorov forward and backward PDEs, where we counter the high dimensionality by a generalisation of anchored-ANOVA decompositions. By computing only the most significant terms in the decomposition, the dimensionality is reduced effectively, such that a significant computational speed-up arises from the high accuracy of PDE schemes in low dimensions compared to Monte Carlo estimation. Moreover, we show how this truncated decomposition can be used as control variate for the full high-dimensional model, such that any approximation errors can be corrected while a substantial variance reduction is achieved compared to the standard simulation approach. We investigate the accuracy for a realistic portfolio of exchange options, interest rate and cross-currency swaps under a fully calibrated ten-factor model.

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Section: Stochastic Volatilitysupporting

confidence: 70%

Efficient exposure computation by risk factor decomposition

Graaf

Kandhai

Reisinger

2018

Quantitative Finance

Self Cite

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“…In recent years, there has been an increasing interest in modelling credit risk by practitioners as well as academics (see e.g., Gregory, 2015, Green, Kenyon, & Dennis, 2014, Sourabh, Hofer, & Kandhai, 2018, De Graaf, Feng, Kandhai, & Oosterlee, 2014, de Graaf, Kandhai, & Reisinger, 2018, Simaitis, de Graaf, Hari, & Kandhai, 2016, Anagnostou & Kandhai, 2019. Portfolio credit risk models are concerned with the occurrence of large losses due to defaults or deteriorations in credit quality.…”

Section: Introductionmentioning

confidence: 99%

Contagious defaults in a credit portfolio: a Bayesian network approach

Anagnostou¹,

Rivero²,

Sourabh³

et al. 2020

JCR

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Robustness of credit portfolio models is of great interest for financial institutions and regulators, since misspecified models translate to insufficient capital buffers and a crisis-prone financial system. In this paper, we propose a method to enhance credit portfolio models based on the model of Merton by incorporating contagion effects. While in most models the risks related to financial interconnectedness are neglected, we use Bayesian network methods to uncover the direct and indirect relationships between credits, while maintaining the convenient representation of factor models. A range of techniques to learn the structure and parameters of financial networks from real Credit Default Swaps (CDS) data is studied and evaluated. Our approach is demonstrated in detail in a stylized portfolio and the impact on standard risk metrics is estimated.

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“…In addition, stochastic interest rate is crucial for option pricing because it ensures proper discounting of future payoffs. In recent literatures [14][15][16][17], Hull-White stochastic interest rate which is analytically tractable has been incorporated into one-factor stochastic volatility model for pricing path-dependent options. Therefore, the model which incorporates multifactor stochastic volatility and stochastic interest rate may be more reasonable for pricing barrier options.…”

Section: Introductionmentioning

confidence: 99%

Efficient Simulation for Pricing Barrier Options with Two‐Factor Stochastic Volatility and Stochastic Interest Rate

Jieqiong

2017

Mathematical Problems in Engineering

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This paper presents an extension of the double Heston stochastic volatility model by combining Hull-White stochastic interest rates. By the change of numeraire and quadratic exponential scheme, this paper develops a new simulation scheme for the extended model. By combining control variates and antithetic variates, this paper provides an efficient Monte Carlo simulation algorithm for pricing barrier options. Based on the differential evolution algorithm the extended model is calibrated to S&P 500 index options to obtain the model parameter values. Numerical results show that the proposed simulation scheme outperforms the Euler scheme, the proposed simulation algorithm is efficient for pricing barrier options, and the extended model is flexible to fit the implied volatility surface.

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Smile and default: the role of stochastic volatility and interest rates in counterparty credit risk

Cited by 6 publications

References 28 publications

Efficient exposure computation by risk factor decomposition

Efficient exposure computation by risk factor decomposition

Contagious defaults in a credit portfolio: a Bayesian network approach

Efficient Simulation for Pricing Barrier Options with Two‐Factor Stochastic Volatility and Stochastic Interest Rate

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