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Autori principali: Medina, Andrés García, González-Farías, Graciela
Natura: Preprint
Pubblicazione: 2019
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Accesso online:https://arxiv.org/abs/1905.00545
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author Medina, Andrés García
González-Farías, Graciela
author_facet Medina, Andrés García
González-Farías, Graciela
contents We determine the number of statistically significant factors in a forecast model using a random matrices test. The applied forecast model is of the type of Reduced Rank Regression (RRR), in particular, we chose a flavor which can be seen as the Canonical Correlation Analysis (CCA). As empirical data, we use cryptocurrencies at hour frequency, where the variable selection was made by a criterion from information theory. The results are consistent with the usual visual inspection, with the advantage that the subjective element is avoided. Furthermore, the computational cost is minimal compared to the cross-validation approach.
format Preprint
id arxiv_https___arxiv_org_abs_1905_00545
institution arXiv
publishDate 2019
record_format arxiv
spellingShingle Determining the number of factors in a forecast model by a random matrix test: cryptocurrencies
Medina, Andrés García
González-Farías, Graciela
Statistical Finance
We determine the number of statistically significant factors in a forecast model using a random matrices test. The applied forecast model is of the type of Reduced Rank Regression (RRR), in particular, we chose a flavor which can be seen as the Canonical Correlation Analysis (CCA). As empirical data, we use cryptocurrencies at hour frequency, where the variable selection was made by a criterion from information theory. The results are consistent with the usual visual inspection, with the advantage that the subjective element is avoided. Furthermore, the computational cost is minimal compared to the cross-validation approach.
title Determining the number of factors in a forecast model by a random matrix test: cryptocurrencies
topic Statistical Finance
url https://arxiv.org/abs/1905.00545