Determining the number of factors in a forecast model by a random matrix test: cryptocurrencies

Andr\'es Garc\'ia Medina and Graciela Gonz\'alez-Far\'ias

Papers from arXiv.org

Abstract: 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.

Date: 2019-05
New Economics Papers: this item is included in nep-ets and nep-pay
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