Enhancing credit default swap valuation with meshfree methods

Guarin, Alexander; Liu, Xiaoquan; Ng, Wing Lon

doi:10.1016/j.ejor.2011.05.046

Cited by 11 publications

(3 citation statements)

References 33 publications

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“…More recently, Escobar et al (2012) have offered a multivariate extension of the CreditGrades model under the assumption of stochastic variance and correlation among the companies' assets. Credit Default Swap is a topic of interest in much recent operational research literature (Guarin et al, 2011;Tomohiro, 2014;Cont and Minca, 2016;Chalamandaris and Vlachogiannakis, 2018;Koutmos, 2018;Irresberger et al, 2018), but in practice, models for traded CDS spreads tend not to be popular among portfolio managers. Although the aforementioned models provide very close approximations (e.g.…”

Section: Introductionmentioning

confidence: 99%

Credit spread approximation and improvement using random forest regression

Mercadier

Lardy²

2019

European Journal of Operational Research

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Credit Default Swap (CDS) levels provide a market appreciation of companies' default risk. These derivatives are not always available, creating a need for CDS approximations. This paper offers a simple, global and transparent CDS structural approximation, which contrasts with more complex and proprietary approximations currently in use. This Equity-to-Credit formula (E2C), inspired by CreditGrades, obtains better CDS approximations, according to empirical analyses based on a large sample spanning 2016-2018. A random forest regression run with this E2C formula and selected additional financial data results in an 87.3% out-of-sample accuracy in CDS approximations. The transparency property of this algorithm confirms the predominance of the E2C estimate, and the impact of companies' debt rating and size, in predicting their CDS.

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Section: Introductionmentioning

confidence: 99%

Credit spread approximation and improvement using random forest regression

Mercadier

Lardy²

2019

European Journal of Operational Research

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“…They are widely used in engineering in providing numerical solutions to PDEs (see Liu (2003) and Fasshauer (2007)). In nance, the applications are concentrated in the solution of time-dependent PDEs for pricing options (Fasshauer et al (2004), Pettersson et al (2008) and Mei and Cheng (2008)) and credit derivatives (Guarin et al (2011)). …”

Section: The Meshfree Methods and The Radial Basis Function Interpolamentioning

confidence: 99%

Recovering default risk from CDS spreads with a nonlinear filter

Guarin¹,

Liu²,

Ng³

2014

Journal of Economic Dynamics and Control

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We propose a nonlinear lter to estimate the time-varying instantaneous default risk from the term structure of credit default swap (CDS) spreads. Based on the numerical solution of the Fokker-Planck equation (FPE) using a meshfree interpolation method, the lter performs a joint estimation of default intensities and CIR model parameters. As the FPE can account for nonlinear functions and nonGaussian errors, the proposed framework provides more exibility and accuracy.We test the nonlinear lter on simulated CDS spreads and apply it to daily CDS spreads of the Dow Jones Industrial Average component companies from 2005 to 2010 with supportive results.

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“…Radial basis function (RBF) neural network (NN) method is a novel and effective feedforward neural network with best approximation and global optimal performance. The training method is fast and easy and has been successfully applied in many fields with its unique information processing capability, especially in the financial, economic, and management fields [5][6][7][8]. This study incorporates RBF NN to investigate the impacts of equity distribution on the acquisition of USOs, aiming to deliver quantitative references for policymakers, founders, and financing providers to promote the development and growth of USOs.…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Acquisition of University Spin-Outs: An RBF Neural Network Approach

Liu

Yang

2017

Complexity

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University spin-outs (USOs), creating businesses from university intellectual property, are a relatively common phenomena. As a knowledge transfer channel, the spin-out business model is attracting extensive attention. In this paper, the impacts of six equities on the acquisition of USOs, including founders, university, banks, business angels, venture capitals, and other equity, are comprehensively analyzed based on theoretical and empirical studies. Firstly, the average distribution of spin-out equity at formation is calculated based on the sample data of 350 UK USOs. According to this distribution, a radial basis function (RBF) neural network (NN) model is employed to forecast the effects of each equity on the acquisition. To improve the classification accuracy, the novel set-membership method is adopted in the training process of the RBF NN. Furthermore, a simulation test is carried out to measure the effects of six equities on the acquisition of USOs. The simulation results show that the increase of university's equity has a negative effect on the acquisition of USOs, whereas the increase of remaining five equities has positive effects. Finally, three suggestions are provided to promote the development and growth of USOs.

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Enhancing credit default swap valuation with meshfree methods

Cited by 11 publications

References 33 publications

Credit spread approximation and improvement using random forest regression

Credit spread approximation and improvement using random forest regression

Recovering default risk from CDS spreads with a nonlinear filter

Forecasting the Acquisition of University Spin-Outs: An RBF Neural Network Approach

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