Deep quantile and deep composite triplet regression
Year of publication: |
2023
|
---|---|
Authors: | Fissler, Tobias ; Merz, Michael ; Wüthrich, Mario V. |
Published in: |
Insurance. - Amsterdam : Elsevier, ISSN 0167-6687, ZDB-ID 8864-X. - Vol. 109.2023, p. 94-112
|
Subject: | Bregman divergence | Conditional tail expectation | Consistent loss function | Elicitability | Neural network regression | Proper scoring rule | Quantile and expected shortfall regression | Splicing model | Regressionsanalyse | Regression analysis | Neuronale Netze | Neural networks | Risikomaß | Risk measure | Schätztheorie | Estimation theory |
-
Nonlinear prediction of conditional percentiles for value-at-risk
Chang, Isaac J., (1999)
-
Vidal-Llana, Xenxo, (2023)
-
Risk measures in a quantile regression credibility framework with Fama/French data applications
Pitselis, Georgios, (2017)
- More ...
-
Demand of insurance under the cost-of-capital premium calculation principle
Merz, Michael, (2014)
-
Modified Munich chain-ladder method
Merz, Michael, (2015)
-
Financial modeling, actuarial valuation and solvency in insurance
Wüthrich, Mario V., (2012)
- More ...