XVA analysis from the balance sheet

Albanese, Claudio; Crépey, Stéphane; Hoskinson, Rodney; Saadeddine, Bouazza

doi:10.1080/14697688.2020.1817533

Cited by 26 publications

(27 citation statements)

References 29 publications

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“…In this context, we also mention Fecamp et al [2019], who use an ANN as a computational tool to solve the pricing and hedging problem under market frictions such as transaction costs. Albanese et al [2019] use an ANN to compute the conditional value-at-risk and expected shortfall necessary for certain XVA computations, by solving a quantile regression.…”

Section: Approximating Value Functions In Optimal Control Problemsmentioning

confidence: 99%

Neural networks for option pricing and hedging: a literature review

Ruf¹,

Wang²

2020

JCF

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Neural networks have been used as a nonparametric method for option pricing and hedging since the early 1990s. Far over a hundred papers have been published on this topic. This note intends to provide a comprehensive review. Papers are compared in terms of input features, output variables, benchmark models, performance measures, data partition methods, and underlying assets. Furthermore, related work and regularisation techniques are discussed.

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Section: Approximating Value Functions In Optimal Control Problemsmentioning

confidence: 99%

Neural networks for option pricing and hedging: a literature review

Ruf¹,

Wang²

2020

JCF

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“…Financial statements are one of the evaluation criteria used by banks to monitor commercial borrowers [19]. "Three financial statements" refer to three basic statements in the financial report of an enterprise, which are profit and loss balance sheet (income statement) [20,21], balance sheet (statement of shareholder equity) [22,23], and cash flow statement [24,25]. Among them, the "cash flow statement" is calculated from the "profit and loss statement" and "balance sheet", which has a testing effect.…”

Section: Financial Statements and Financial Ratiosmentioning

confidence: 99%

Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry

Chen

Lin

et al. 2021

Mathematics

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In recent years in Taiwan, scholars who study financial bankruptcy have mostly focused on individual listed and over-the-counter (OTC) industries or the entire industry, while few have studied the independent electronics industry. Thus, this study investigated the application of an advanced hybrid Z-score bankruptcy prediction model in selecting financial ratios of listed companies in eight related electronics industries (semiconductor, computer, and peripherals, photoelectric, communication network, electronic components, electronic channel, information service, and other electronics industries) using data from 2000 to 2019. Based on 22 financial ratios of condition attributes and one decision attribute recommended and selected by experts and in the literature, this study used five classifiers for binary logistic regression analysis and in the decision tree. The experimental results show that for the Z-score model, samples analyzed using the five classifiers in five groups (1:1–5:1) of different ratios of companies, the bagging classifier scores are worse (40.82%) than when no feature selection method is used, while the logistic regression classifier and decision tree classifier (J48) result in better scores. However, it is significant that the bagging classifier score improved to over 90% after using the feature selection technique. In conclusion, it was found that the feature selection method can be effectively applied to improve the prediction accuracy, and three financial ratios (the liquidity ratio, debt ratio, and fixed assets turnover ratio) are identified as being the most important determinants affecting the prediction of financial bankruptcy in providing a useful reference for interested parties to evaluate capital allocation to avoid high investment risks.

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“…KVA should be calculated under the historical measure, which is typically assumed to be the same as the risk neutral measure to preserve analytical tractability. Such an approach is followed, for example, by Albanese and Crépey (2018), who define the KVA as the solution to a BSDE under the risk neutral measure. Green, Kenyon, and Dennis (2014) derive the KVA using an extension of the semireplication approach in Burgard and Kjaer (2011b) by grouping together all capital-dependent terms in the Feynman-Kac representation of their pricing partial differential equation (PDE).…”

Section: Valuation Measure Collateralization and Closeoutmentioning

confidence: 99%

Robust XVA

Bichuch

Capponi

Sturm

2020

Mathematical Finance

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We introduce an arbitrage-free framework for robust valuation adjustments. An investor trades a credit default swap portfolio with a risky counterparty, and hedges credit risk by taking a position in defaultable bonds. The investor does not know the return rate of her counterparty bond, but is confident that it lies within a uncertainty interval. We derive both upper and lower bounds for the XVA process of the portfolio, and show that these bounds may be recovered as solutions of nonlinear ordinary differential equations. The presence of collateralization and closeout payoffs leads to important differences with respect to classical credit risk valuation. The value of the superreplicating portfolio cannot be directly obtained by plugging one of the extremes of the uncertainty interval in the valuation equation, but rather depends on the relation between the XVA replicating portfolio and the close-out value throughout the life of the transaction. Our comparative statics analysis indicates that credit contagion has a nonlinear effect on the replication strategies and on the XVA.

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XVA analysis from the balance sheet

Cited by 26 publications

References 29 publications

Neural networks for option pricing and hedging: a literature review

Neural networks for option pricing and hedging: a literature review

Comparable Studies of Financial Bankruptcy Prediction Using Advanced Hybrid Intelligent Classification Models to Provide Early Warning in the Electronics Industry

Robust XVA

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