Asymmetric GARCH model via Yeo-Johnson transformation

In this paper, we introduce an extended GARCH model designed to address asymmetric leverage effects.The variance in the standard GARCH model is composed of past conditional variances and past squared residuals.However, it is not possible to model asymmetric leverage effects with squared residuals alone, so in this paper, we propose a new extended GARCH model to explain the leverage effects using the Yeo-Johnson transformation which adjusts transformation parameter to make asymmetric data more normal or symmetric.We utilize the reverse properties of Yeo-Johnson transformation to model asymmetric volatility.We investigate the characteristics of the proposed model and parameter estimation.We also explore how to derive forecasts and forecast intervals in the proposed model.We compare it with standard GARCH and other extended GARCH models that model asymmetric leverage effects through empirical data analysis.