Gebäude D4

Christa Cuchiero

Christa Cuchiero
Prof. for Quantative Riskmanagement
University of Vienna
Research Interests

Mathematical Finance, Stochastic Analysis, Probability Theory and Machine Learning

Selected Publications
  • Cuchiero, C., Gonon, L., Grigoryeva, L., Ortega, J.-P., Teichmann, J., 2022. Discrete-Time Signatures and Randomness in Reservoir Computing. IEEE Transactions on Neural Networks and Learning Systems, pp. 1–14.
  • Cuchiero, C., Khosrawi, W., Teichmann, J., 2020. A generative adversarial network approach to calibration of local stochastic volatility models. Risk 2020, pp.  1–34.
  • Cuchiero, C., Larsson, M., Teichmann, J., 2020. Deep neural networks, generic universal interpolation, and controlled ODEs. SIAM Journal on Mathematics of Data Science, 2(3), pp. 901 – 919.
  • Cuchiero, C., Teichmann, J., 2020. Generalized Feller processes and Markovian lifts of stochastic Volterra processes: the affine case. Journal of Evolution Equations, pp. 1–48.,
  • Cuchiero, C., 2019. Polynomial processes in stochastic portfolio theory. Stochastic processes and their applications, 129(5), pp. 1829–1872.
  • Cuchiero, C., Schachermayer, W., Wong, L., 2019. Cover’s universal portfolio, stochastic portfolio theory and the numéraire portfolio. Mathematical Finance, 29(3), pp. 773–803.
  • Cuchiero, C., Fontana, C. and Gnoatto, A., 2016. A general HJM framework for multiple yield curve modeling. Finance and Stochastics, 20(2), pp. 267–320.
  • Cuchiero, C. and Teichmann, J.,2015. Fourier transform methods for pathwise covariance estimation in the presence of jumps. Stochastic processes and their applications, 125(1), pp. 116–160.
  • Cuchiero, C.,  Keller-Ressel, M. and Teichmann, J., 2012. Polynomial processes and their applications to mathematical finance. Finance and Stochastics, 16(4), pp. 711–740.
  • Cuchiero, C., Filipović, D., Mayerhofer, E. and Teichmann, J., 2011. Affine processes on positive semidefinite matrices. Annals of Applied Probability, 21(2) pp. 397–463.
Rüdiger Frey
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