Flexible supercapacitor electrodes with vertically aligned carbon nanotubes grown on aluminum foils

In this work, vertically aligned carbon nanotubes (VACNTs) grown on aluminum foils were used as flexible supercapacitor electrodes. Aluminum foils were used as readily available, cheap and conductive substrates, and VACNTs were grown directly on these foils through chemical vapor deposition (CVD) method. Solution based ultrasonic spray pyrolysis (USP) method was used for the deposition of the CNT catalyst. Direct growth of VACNTs on aluminum foils ruled out both the internal resistance of the supercapacitor electrodes and the charge transfer resistance between the electrode and electrolyte. A specific capacitance of 2.61 mF/cm2 at a scan rate of 800 mV/s was obtained from the fabricated electrodes, which is further improved through the bending cycles.     

Comparison of forecasting performances: Does normalization and variance stabilization method beat GARCH(1,1)‐type models? Empirical evidence from the stock markets

In this paper, we present a comparison between the forecasting performances of the normalization and variance stabilization method (NoVaS) and the GARCH(1,1), EGARCH(1,1) and GJR‐GARCH(1,1) models. Hence the aim of this study is to compare the out‐of‐sample forecasting performances of the models used throughout the study and to show that the NoVaS method is better than GARCH(1,1)‐type models in the context of out‐of sample forecasting performance. We study the out‐of‐sample forecasting performances of GARCH(1,1)‐type models and NoVaS method based on generalized error distribution, unlike normal and Student's t‐distribution. Also, what makes the study different is the use of the return series, calculated logarithmically and arithmetically in terms of forecasting performance. For comparing the out‐of‐sample forecasting performances, we focused on different datasets, such as S&P 500, logarithmic and arithmetic B?ST 100 return series. The key result of our analysis is that the NoVaS method performs better out‐of‐sample forecasting performance than GARCH(1,1)‐type models. The result can offer useful guidance in model building for out‐of‐sample forecasting purposes, aimed at improving forecasting accuracy.