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operator (Lasso) method proposed by Tibshirani (1996) and extended into quantile regression context by Li and Zhu (2008). The …
Persistent link: https://www.econbiz.de/10011580445
simultaneously. We first show the consequences of using Lasso type estimate directly for time series without considering the temporal …
Persistent link: https://www.econbiz.de/10010281503
optimizes all the parameters within the model. We employ Lasso and elastic-net penalty functions as regularization approach. The …
Persistent link: https://www.econbiz.de/10010318767
Quantile regression is in the focus of many estimation techniques and is an important tool in data analysis. When it comes to nonparametric specifications of the conditional quantile (or more generally tail) curve one faces, as in mean regression, a dimensionality problem. We propose a...
Persistent link: https://www.econbiz.de/10010330967
We propose a semiparametric measure to estimate systemic interconnectedness across financial institutions based on tail-driven spill-over effects in a ultra-high dimensional framework. Methodologically, we employ a variable selection technique in a time series setting in the context of a...
Persistent link: https://www.econbiz.de/10010491451
penalization parameter () of a linear quantile lasso regression. The FRM is calculated by taking the average of the penalization …
Persistent link: https://www.econbiz.de/10011663444
quantile lasso regression methods for risk analysis based on NASDAQ data, Yahoo Finance data and some macro variables. The ….quantlet.de with the keyword FRM. The RiskAnalytics package is a convenient tool with the purpose of integrating lasso penalized …
Persistent link: https://www.econbiz.de/10011663447