The Institute for Statistics and Mathematics (Department of Finance, Accounting and Statistics) cordially invites everyone interested to attend the talks in our Research Seminar Series, where internationally renowned scholars from leading universities present and discuss their (working) papers.

The list of talks for the winter term 2018/19 is available via the following link: <link en statmath resseminar>www.wu.ac.at/en/statmath/resseminar

Abstract:
A proven strategy in decision-making to cope with unknown or uncertain future events is to rely on forecasts for these events. Examples range from weather forecasts for agriculture, airlines or a convenient everyday life, to forecasts for supply and demand in a business context, to risk-assessment in finance or predictions for GDP growth and inflation for prudential economic policy. In the presence of multiple different forecasts, a core challenge is to assess their relative quality and to eventually rank them in terms of their historic performance. This calls for an accuracy measure which is commonly given in terms of a loss function specifying the discrepancy between a forecast and the actual observation. Examples include the zero-one loss, the absolute loss or the squared loss. If the ultimate goal of the forecasts is specified in terms of a statistical functional such as the mean, a quantile, or a certain risk measure, the loss should incentivise truthful forecasts in that the expected loss is strictly minimised by the correctly specified forecast. If a functional possesses such an incentive compatible loss function, it is called elicitable. Besides enabling meaningful forecast comparison, the elicitability of a functional allows for M-estimations and regression. Acknowledging that there is a wealth of elicitable functionals (mean, quantiles, expectiles) and non-elicitable functionals (variance, Expected Shortfall), this talk addresses aspects of the following Elicitation Problem:
1) When is a functional elicitable?
2) What is the class of incentive compatible loss functions?
3) What are distinguished loss functions to use in practice?
4) How to cope with the non-elicitability of a functional?
The emphasis will lie on main achievements for multivariate functionals such as the pair of risk measures (Value-at-Risk, Expected Shortfall). It will also give an outlook to modern and very recent achievements in the realm of set-valued functionals which are suited to consider set-valued measures of systemic risk or confidence intervals and regions.

Kindly note that on November 9 two talks are scheduled at our institute:
9:00 – 10:10  Nestor Parolya (Leibniz University Hannover)
11:00 – 12:10  Tobias Fissler (Imperial College London)

http://www.wu.ac.at/en/statmath/resseminar