A Monte Carlo Simulation Analysis of the NARDL Method with Regard to Cryptocurrencies

Abstract

One of the nonlinear techniques utilized in the analysis of economic and financial variables is the nonlinear autoregressive distributed lag (NARDL) method. This study primarily focuses on the NARDL approach, which offers the chance to assess the asymmetric relationships between cryptocurrencies and economic and financial variables. Monte Carlo experiments were carried out while developing the NARDL method for the purpose of investigating the finite sample properties of estimators under the premise of normal distribution for a simple data generation procedure. This study examines the NARDL method's dependability for non-normal distributions. The return distributions of cryptocurrencies are obviously non-normal and heavy-tailed, making this a significant research challenge. This study simulates the NARDL model using both several heavy-tailed distributions as well as a normal distribution. To the best of our knowledge, no research has yet occurred on the NARDL method's finite sample qualities for time series with non-normality. The findings from this study could have a significant impact on how accurately predictions are made regarding the impact cryptocurrencies have on the economy and finance.