scholarly journals An Accurate Solution for Credit Valuation Adjustment (CVA) and Wrong Way Risk

2019 ◽  
Author(s):  
Tim Xiao
Keyword(s):  
Monte Carlo ◽  
Probability Distribution ◽  
Accurate Solution ◽  
Least Square ◽  
Backward Induction ◽  
Order Of Accuracy ◽  
Monte Carlo Approach ◽  
Default Time ◽  
Credit Valuation Adjustment ◽  
High Order Of Accuracy

This paper presents a Least Square Monte Carlo approach for accurately calculating credit value adjustment (CVA). In contrast to previous studies, the model relies on the probability distribution of a default time/jump rather than the default time itself, as the default time is usually inaccessible. As such, the model can achieve a high order of accuracy with a relatively easy implementation. We find that the valuation of a defaultable derivative is normally determined via backward induction when their payoffs could be positive or negative. Moreover, the model can naturally capture wrong or right way risk. https://arabixiv.org/2cqbg/download

scholarly journals An Accurate Solution for Credit Valuation Adjustment (CVA) and Wrong Way Risk

2019 ◽  
Author(s):  
Tim Xiao
Keyword(s):  
Monte Carlo ◽  
Probability Distribution ◽  
Accurate Solution ◽  
Least Square ◽  
Backward Induction ◽  
Order Of Accuracy ◽  
Monte Carlo Approach ◽  
Default Time ◽  
Credit Valuation Adjustment ◽  
High Order Of Accuracy

This paper presents a Least Square Monte Carlo approach for accurately calculating credit value adjustment (CVA). In contrast to previous studies, the model relies on the probability distribution of a default time/jump rather than the default time itself, as the default time is usually inaccessible. As such, the model can achieve a high order of accuracy with a relatively easy implementation. We find that the valuation of a defaultable derivative is normally determined via backward induction when their payoffs could be positive or negative. Moreover, the model can naturally capture wrong or right way risk.

scholarly journals An Accurate Solution for Credit Valuation Adjustment (CVA) and Wrong Way Risk

2019 ◽  
Author(s):  
Tim Xiao
Keyword(s):  
Monte Carlo ◽  
Probability Distribution ◽  
Accurate Solution ◽  
Least Square ◽  
Backward Induction ◽  
Order Of Accuracy ◽  
Monte Carlo Approach ◽  
Default Time ◽  
Credit Valuation Adjustment ◽  
High Order Of Accuracy

This paper presents a Least Square Monte Carlo approach for accurately calculating credit value adjustment (CVA). In contrast to previous studies, the model relies on the probability distribution of a default time/jump rather than the default time itself, as the default time is usually inaccessible. As such, the model can achieve a high order of accuracy with a relatively easy implementation. We find that the valuation of a defaultable derivative is normally determined via backward induction when their payoffs could be positive or negative. Moreover, the model can naturally capture wrong or right way risk. https://frenxiv.org/2rtya/download

scholarly journals An Accurate Solution for Credit Valuation Adjustment (CVA) and Wrong Way Risk

2019 ◽  
Author(s):  
Tim Xiao
Keyword(s):  
Monte Carlo ◽  
Probability Distribution ◽  
Accurate Solution ◽  
Least Square ◽  
Backward Induction ◽  
Order Of Accuracy ◽  
Monte Carlo Approach ◽  
Default Time ◽  
Credit Valuation Adjustment ◽  
High Order Of Accuracy

This paper presents a Least Square Monte Carlo approach for accurately calculating credit value adjustment (CVA). In contrast to previous studies, the model relies on the probability distribution of a default time/jump rather than the default time itself, as the default time is usually inaccessible. As such, the model can achieve a high order of accuracy with a relatively easy implementation. We find that the valuation of a defaultable derivative is normally determined via backward induction when their payoffs could be positive or negative. Moreover, the model can naturally capture wrong or right way risk. https://osf.io/preprints/socarxiv/3yjk2/download

scholarly journals An Accurate Solution for Credit Valuation Adjustment (CVA) and Wrong Way Risk

2019 ◽  
Author(s):  
Tim Xiao
Keyword(s):  
Monte Carlo ◽  
Probability Distribution ◽  
Accurate Solution ◽  
Least Square ◽  
Backward Induction ◽  
Order Of Accuracy ◽  
Monte Carlo Approach ◽  
Default Time ◽  
Credit Valuation Adjustment ◽  
High Order Of Accuracy

This paper presents a Least Square Monte Carlo approach for accurately calculating credit value adjustment (CVA). In contrast to previous studies, the model relies on the probability distribution of a default time/jump rather than the default time itself, as the default time is usually inaccessible. As such, the model can achieve a high order of accuracy with a relatively easy implementation. We find that the valuation of a defaultable derivative is normally determined via backward induction when their payoffs could be positive or negative. Moreover, the model can naturally capture wrong or right way risk.

scholarly journals Calculation of Credit Valuation Adjustment Based on Least Square Monte Carlo Methods

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Qian Liu
Keyword(s):  
Monte Carlo ◽  
Credit Risk ◽  
Financial Markets ◽  
Financial Institutions ◽  
Least Square ◽  
Copula Functions ◽  
Default Intensity ◽  
Interest Rate Swaps ◽  
Counterparty Credit Risk ◽  
Credit Valuation Adjustment

Counterparty credit risk has become one of the highest-profile risks facing participants in the financial markets. Despite this, relatively little is known about how counterparty credit risk is actually priced mathematically. We examine this issue using interest rate swaps. This largely traded financial product allows us to well identify the risk profiles of both institutions and their counterparties. Concretely, Hull-White model for rate and mean-reverting model for default intensity have proven to be in correspondence with the reality and to be well suited for financial institutions. Besides, we find that least square Monte Carlo method is quite efficient in the calculation of credit valuation adjustment (CVA, for short) as it avoids the redundant step to generate inner scenarios. As a result, it accelerates the convergence speed of the CVA estimators. In the second part, we propose a new method to calculate bilateral CVA to avoid double counting in the existing bibliographies, where several copula functions are adopted to describe the dependence of two first to default times.

Wigner-Boltzmann Monte Carlo approach to nanodevice simulation: from quantum to semiclassical transport

2009 ◽  
Vol 8 (3-4) ◽  
pp. 324-335 ◽  
Author(s):  
Damien Querlioz ◽  
Huu-Nha Nguyen ◽  
Jérôme Saint-Martin ◽  
Arnaud Bournel ◽  
Sylvie Galdin-Retailleau ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Approach ◽  
Semiclassical Transport
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