The potential of machine learning algorithms in the assessment of market risks has not been completely investigated in the literature, such as in the forecasting Value-at-Risk (VaR). In this paper we introduce the Dynamic Quantile Regression Forest, a model combining Quantile Regression Forests with a dynamic VaR. The model is dynamic as the quantile prediction of the previous random forest becomes part of the training set used to train the next random forest. Thus, it is possible to estimate the response variable conditional distribution by accounting for the evolution of the quantile over time among other covariates
Dynamic Quantile Regression Forest / Andreani, Mila; Petrella, Lea. - (2020), pp. 1054-1058. (Intervento presentato al convegno 50th Scientific Meeting of the Italian Statistical Society tenutosi a Pisa).
Dynamic Quantile Regression Forest
Andreani Mila;Petrella Lea
2020
Abstract
The potential of machine learning algorithms in the assessment of market risks has not been completely investigated in the literature, such as in the forecasting Value-at-Risk (VaR). In this paper we introduce the Dynamic Quantile Regression Forest, a model combining Quantile Regression Forests with a dynamic VaR. The model is dynamic as the quantile prediction of the previous random forest becomes part of the training set used to train the next random forest. Thus, it is possible to estimate the response variable conditional distribution by accounting for the evolution of the quantile over time among other covariates| File | Dimensione | Formato | |
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