CORE classification in ICCS

ICCS is an A-rank conference in CORE classification and one of the most cited events and publications in computational science according to Google Scholar, with h5-index=37 and h5-median=57. For more information, click...

How to quantify synergy in complex systems

  Quantifying synergy among stochastic variables is an important open problem in information theory. Information synergy occurs when multiple sources together predict an outcome variable better than the sum of single-source predictions. As simple examples, consider the correlation between one of the inputs (X1 or X2) with either output S1 or S2, which is zero, whereas taking either the inputs or the outputs together yields maximal correlation. It is an essential phenomenon in biology such as in neuronal networks and cellular regulatory processes, where different information flows integrate to produce a single response, but also in social cooperation processes as well as in statistical inference tasks in machine learning. In our publication we propose a metric of synergistic entropy and synergistic information from first principles. The proposed measure relies on so-called synergistic random variables (SRVs) which are constructed to have zero mutual information about individual source variables but non-zero mutual information about the complete set of source variables. We prove several basic and desired properties of our measure, including bounds and additivity properties. In addition, we prove several important consequences of our measure, including the fact that different types of synergistic information may co-exist between the same sets of variables. A numerical implementation is provided, which we use to demonstrate that synergy is associated with resilience to noise. Our approach is radically different from the previously proposed frameworks. Our measure may be a marked step forward in the study of multivariate information theory and its numerous applications. Rick Quax, Omri Har-Shemesh, Peter M.A. Sloot. Entropy 2017, 19(2), 85;...

Smile and default

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...