TY  - JOUR
A1  - Homburg, Annika
A1  - Weiß, Christian H.
A1  - Frahm, Gabriel
A1  - Alwan, Layth C.
A1  - Göb, Rainer
T1  - Analysis and forecasting of risk in count processes
T2  - Journal of Risk and Financial Management
N2  - Risk measures are commonly used to prepare for a prospective occurrence of an adverse event. If we are concerned with discrete risk phenomena such as counts of natural disasters, counts of infections by a serious disease, or counts of certain economic events, then the required risk forecasts are to be computed for an underlying count process. In practice, however, the discrete nature of count data is sometimes ignored and risk forecasts are calculated based on Gaussian time series models. But even if methods from count time series analysis are used in an adequate manner, the performance of risk forecasting is affected by estimation uncertainty as well as certain discreteness phenomena. To get a thorough overview of the aforementioned issues in risk forecasting of count processes, a comprehensive simulation study was done considering a broad variety of risk measures and count time series models. It becomes clear that Gaussian approximate risk forecasts substantially distort risk assessment and, thus, should be avoided. In order to account for the apparent estimation uncertainty in risk forecasting, we use bootstrap approaches for count time series. The relevance and the application of the proposed approaches are illustrated by real data examples about counts of storm surges and counts of financial transactions.
KW  - count time series
KW  - expected shortfall
KW  - expectiles
KW  - Gaussian approximation
KW  - mid quantiles
KW  - tail conditional expectation
KW  - value at risk
Y1  - 2021
UR  - https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/23669
UR  - https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-236692
SN  - 1911-8074
VL  - 14
IS  - 4
ER  -