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More in this section The Centre for Robust Inference in a Digital Economy




The following are key publications on the topics of robust inference and decision making by RIDE's members. More publications are to appear shortly.

Publications by Alex Gammerman

Gammerman, A. and V. Vovk (2007). Hedging predictions in machine learning (with discussion). Computer Journal, Vol. 50, No. 2, p. 151-163.

Nouretdinov, I., T. Bellotti, and A. Gammerman (2014). Diagnostic and prognostic by conformal predictors. In: Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications (ed. by V. Balasubramanian, S.-S. Ho, and V. Vovk), p. 217–230. Elsevier, Amsterdam.

Publications by Alessio Sancetta

Sancetta, A. (2012) Universality of Bayesian predictions. 'Bayesian Analysis, Vol. 7, No 1, p. 1-36, p. 45-46 (rejoinder)'. This article establishes finite sample bounds for the loss incurred using Bayesian predictions in a variety of settings, including almost arbitrary loss functions, model averaging, predictions in a non-stationary environment and under model misspecification.

Kurov, A., A. Sancetta, G.Strasser, and M. Halova Wolfe (2016) Price drift before U.S. macroeconomic news: private information about public announcements?  This article shows that trading on information leakage of US economic announcements could have taken place since 2008 resulting in profits possibly in the order of hundred of millions in US dollars.

Publications by Vladimir Vovk

Shafer, G. and V. Vovk (2001) Probability and Finance: It's Only a Game! New York : Wiley-Interscience. This book introduces a robust approach to mathematical finance and the foundations of probability. A new edition is forthcoming.

Vovk, V. (2012) Continuous-time trading and the emergence of probability. 'Finance and Stochastics, Vol. 16, No. 4,  p. 561 - 609'. This article introduces a probability-free approach to the study of security prices in idealised financial markets.


Impactful research

Members of RIDE contribute to research that makes an impact on the world. Read our news story - 'Information leak helps US traders turn a fat profit,' which was covered by Bloomberg, CNN and the Financial Times in 2016.


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