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

According to Basel III, financial institutions need to charge a Credit Valuation Adjustment (CVA) to account for counterparty default risk. This adjustment is typically driven by a large number of uncertain risk factors, which makes efficient computation of CVA and the corresponding risk measures a complex mathematical and numerical modelling problem. In “Smile and default: the role of stochastic volatility and interest rates in counterparty credit risk” (S. Simaitis 2016), published in Quantitative Finance, Kees de Graaf et al., applied this method to study the complex multi-dimensional problem of the role of fat-tailed distributions of underlying correlated risk factors on default risk. Their studies confirmed that deviations from normality of asset prices significantly impacts exposure dynamics. In particular, for more complex path-dependent derivatives, the risk measures become highly model-dependent. Citation info: S. Simaitis, C.S.L. de Graaf, B.D. Kandhai and N. Hari. 2016. “Smile and default: the role of stochastic volatility and interest rates in counterparty credit risk.” Quantitative Finance...

Efficient Estimation of Sensitivities for Counterparty Credit Risk with the Finite Difference Monte-Carlo Method

According to Basel III, financial institutions need to charge a Credit Valuation Adjustment (CVA) to account for counterparty default risk. This adjustment is typically driven by a large number of uncertain risk factors, which makes efficient computation of CVA and the corresponding risk measures a complex mathematical and numerical modelling problem. In “Efficient Estimation of Sensitivities for Counterparty Credit Risk with the Finite Difference Monte-Carlo Method” (C.S.L. de Graaf 2016), published in the Journal of Computational Finance, Kees de Graaf, Drona Kandhai and Peter Sloot, introduced a novel and efficient numerical method for the estimation of CVA and its risk measures.  For a wide range of benchmark cases, it is shown that the numerical estimates are highly accurate. Citation info: C.S.L. de Graaf, B.D. Kandhai and P.M.A. Sloot. 2016. “Efficient estimation of sensitivities for counterparty credit risk with the finite difference Monte Carlo method.” Journal of Computational Finance...

Drona Kandhai appointed as professor

It is with great pleasure that we can let you know that the University of Amsterdam has appointed Drona Kandhai as professor by special appointment in Computational Finance. For further info and details please read...

Rick Quax has been appointed as Assistant Professor

Rick Quax has been appointed as the new Assistant Professor on the topic of ‘Information Processing in Complex Adaptive Systems’ in the group. His new role started on September 1, 2016. Together with Prof. Peter Sloot he will spearhead the ‘Complex Systems Theory’ theme ( http://uva.computationalscience.nl/research/ )....