The study evaluates nonlinear price transmission mechanisms between clean energy stock and crude oil price in levels, mean, and error variances. We propose a novel way of combining a two-regime threshold vector error correction with the DCC-GARCH model to demonstrate a statistical coherency. The study advances the literature by examining the long-and short-term dynamics of these assets in their levels where the information of nonstationarity in the first moment of these assets is preserved, which generally disappears or becomes a random walk process in the return series. The combined model is then applied to derive a regime dependent dynamic hedging strategy, which has been complemented by a wavelet-based hedging strategy. The data spans from 2nd April 2004 to 10th July 2020 is divided into sub-periods to incorporate the financial crisis and ongoing COVID pandemic. Our findings suggest a nonlinear regime-dependent long-term connectedness among the assets in the first and second moments. The study affirms that the price transmission path between the two asset classes is nonlinear. The research indicates that the clean energy index emerges as the dominant influencer on the crude oil price over the post-crisis subsample. A nonparametric nonlinear causality further validates the theoretical rationale of an integrated model. While examining the impact of several control variables on the relationship between these assets, we find that policy uncertainty is an important thread which further demonstrates the prominence of clean energy stocks. Our findings are in accordance with the global focus of divestment in the non-fossil fuel energy sector. This study differs from previous studies in its apt application of statistical modelling techniques on the theoretical and empirical ground. The outcomes are encouraging for the global investors and traders communities as the integrated model has the potential to fetch higher returns compared to commonly used volatility-based models. (c) 2021 Elsevier B.V. All rights reserved.