Analysis and forecasting of risk in count processes
Please always quote using this URN: urn:nbn:de:bvb:20-opus-236692
- 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 areRisk 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.…
Author: | Annika Homburg, Christian H. Weiß, Gabriel Frahm, Layth C. Alwan, Rainer Göb |
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URN: | urn:nbn:de:bvb:20-opus-236692 |
Document Type: | Journal article |
Faculties: | Fakultät für Mathematik und Informatik / Institut für Mathematik |
Language: | English |
Parent Title (English): | Journal of Risk and Financial Management |
ISSN: | 1911-8074 |
Year of Completion: | 2021 |
Volume: | 14 |
Issue: | 4 |
Article Number: | 182 |
Source: | Journal of Risk and Financial Management (2021) 14:4, 182. https://doi.org/10.3390/jrfm14040182 |
DOI: | https://doi.org/10.3390/jrfm14040182 |
Dewey Decimal Classification: | 3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft |
5 Naturwissenschaften und Mathematik / 51 Mathematik / 510 Mathematik | |
Tag: | Gaussian approximation; count time series; expected shortfall; expectiles; mid quantiles; tail conditional expectation; value at risk |
Release Date: | 2022/09/05 |
Date of first Publication: | 2021/04/16 |
Licence (German): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |