Search results
Results: 56
Number of items: 56
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Sourabh, S., Hofer, M., & Kandhai, D. (2020). Quantifying systemic risk using Bayesian networks. Web publication or website, Risk.net. https://www.risk.net/7462701 -
Moysiadis, G., Anagnostou, I., & Kandhai, D. (2019). Calibrating the Mean-Reversion Parameter in the Hull-White Model Using Neural Networks. In C. Alzate, & A. Monreale (Eds.), ECML PKDD 2018 Workshops: MIDAS 2018 and PAP 2018, Dublin, Ireland, September 10-14, 2018 : proceedings (pp. 23-36). (Lecture Notes in Computer Science; Vol. 11054), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-030-13463-1_2
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Anagnostou, I., & Kandhai, D. (2019). Risk Factor Evolution for Counterparty Credit Risk under a Hidden Markov Model. Risks, 7(2), Article 66. https://doi.org/10.3390/risks7020066 -
Boersma, M., Sourabh, S., Hoogduin, L., & Kandhai, D. (2018). Financial statement networks: an application of network theory in audit. The Journal of Network Theory in Finance, 4(4), 59-85. https://doi.org/10.21314/JNTF.2018.048
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Sourabh, S., Hofer, M., & Kandhai, D. (2018). Liquidity risk in derivatives valuation: an improved credit proxy method. Quantitative Finance, 18(3), 467-481 . https://doi.org/10.1080/14697688.2017.1315166 -
Anagnostou, I., Sourabh, S., & Kandhai, D. (2018). Incorporating Contagion in Portfolio Credit Risk Models Using Network Theory. Complexity, 2018, Article 6076173. https://doi.org/10.1155/2018/6076173 -
de Graaf, C. S. L., Kandhai, D., & Sloot, P. M. A. (2017). Efficient Estimation of Sensitivities for Counterparty Credit Risk with the Finite Difference Monte Carlo Method. Journal of Computational Finance, 21(1), 83-113. https://doi.org/10.2139/ssrn.2521431, https://doi.org/10.21314/JCF.2016.325 -
Simaitis, S., de Graaf, C. S. L., Hari, N., & Kandhai, D. (2016). Smile and Default: The Role of Stochastic Volatility and Interest Rates in Counterparty Credit Risk. Quantitative Finance, 16(11), 1725-1740. https://doi.org/10.1080/14697688.2016.1176240
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