Predicting Value at Risk for Cryptocurrencies Using Generalized Random Forests
Year of publication: |
[2022]
|
---|---|
Authors: | Görgen, Konstantin ; Meirer, Jonas ; Schienle, Melanie |
Publisher: |
[S.l.] : SSRN |
Subject: | Risikomaß | Risk measure | Prognoseverfahren | Forecasting model | Forstwirtschaft | Forestry | Theorie | Theory |
Extent: | 1 Online-Ressource (43 p) |
---|---|
Type of publication: | Book / Working Paper |
Language: | English |
Notes: | Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments February 24, 2022 erstellt |
Other identifiers: | 10.2139/ssrn.4053537 [DOI] |
Classification: | c58 ; G17 - Financial Forecasting ; C22 - Time-Series Models |
Source: | ECONIS - Online Catalogue of the ZBW |
-
A Forest Full of Risk Forecasts for Managing Volatility
Kleen, Onno, (2022)
-
Gatarek, Lukasz T., (2014)
-
Dynamic semiparametric models for expected shortfall (and value-at-risk)
Patton, Andrew J., (2017)
- More ...
-
Model Diagnostics and Forecast Evaluation for Quantiles
Gneiting, Tilmann, (2023)
-
Capturing the zero: A new class of zero-augmented distributions and multiplicative error processes
Hautsch, Nikolaus, (2010)
-
Misspecification Testing in GARCH-MIDAS Models
Conrad, Christian, (2015)
- More ...