Disentangling wrong-way risk: pricing credit valuation adjustment via change of measures

2018 ◽  
Vol 269 (3) ◽  
pp. 1154-1164 ◽  
Author(s):  
Damiano BRIGO ◽  
Frédéric VRINS
Keyword(s):  
Change Of Measures ◽  
Risk Pricing ◽  
Credit Valuation Adjustment

scholarly journals COUNTERPARTY RISK PRICING: IMPACT OF CLOSEOUT AND FIRST-TO-DEFAULT TIMES

2012 ◽  
Vol 15 (06) ◽  
pp. 1250039 ◽  
Author(s):  
DAMIANO BRIGO ◽  
CRISTIN BUESCU ◽  
MASSIMO MORINI
Keyword(s):  
Counterparty Risk ◽  
Contagion Effect ◽  
Default Time ◽  
Pricing Error ◽  
Risk Pricing ◽  
Credit Valuation Adjustment ◽  
Default Contagion ◽  
The Impact

In the absence of a universally accepted procedure for the credit valuation adjustment (CVA) calculation, we compare a number of different bilateral counterparty valuation adjustment (BVA) formulas. First we investigate the impact of the choice of the closeout convention used in the formulas. Important consequences on default contagion manifest themselves in a rather different way depending on which closeout formulation is used (risk-free or replacement), and on default dependence between the two entities in the deal. Second we compare the full bilateral formula with an approximation that is based on subtracting two unilateral credit valuation adjustment (UCVA) formulas. Although the latter might be attractive for its instantaneous implementation once one has a unilateral CVA system, it ignores the impact of the first-to-default time, when closeout procedures are ignited. We illustrate in a number of realistic cases both the contagion effect due to the closeout convention, and the CVA pricing error due to ignoring the first-to-default time.

scholarly journals Correction to: Carry Trade Returns and Segmented Risk Pricing

2021 ◽  
Author(s):  
Gordon Schulze
Keyword(s):  
Carry Trade ◽  
Risk Pricing

A correction to this paper has been published: https://doi.org/10.1007/s11293-021-09708-3

Internet financial supervision based on machine learning and improved neural network

2020 ◽  
pp. 1-12
Author(s):  
Cao Yanli
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Credit Risk ◽  
Machine Learning Algorithms ◽  
Financial Supervision ◽  
Loan Pricing ◽  
Comprehensive Management ◽  
Risk Pricing ◽  
Basel Agreement ◽  
Relevant Factors

The research on the risk pricing of Internet finance online loans not only enriches the theory and methods of online loan pricing, but also helps to improve the level of online loan risk pricing. In order to improve the efficiency of Internet financial supervision, this article builds an Internet financial supervision system based on machine learning algorithms and improved neural network algorithms. Moreover, on the basis of factor analysis and discretization of loan data, this paper selects the relatively mature Logistic regression model to evaluate the credit risk of the borrower and considers the comprehensive management of credit risk and the matching with income. In addition, according to the relevant provisions of the New Basel Agreement on expected losses and economic capital, starting from the relevant factors, this article combines the credit risk assessment results to obtain relevant factors through regional research and conduct empirical analysis. The research results show that the model constructed in this paper has certain reliability.

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