I have an approved proposal that combines Ensemble Learning techniques with GARCH-derived price series to forecast financial markets. I now need the full academic manuscript developed for submission to a finance journal. You will transform the proposal into a polished 10–20 page paper that can pass a double-blind peer-review process. The manuscript must contain: • Introduction and Literature Review that situates ensemble models (e.g., stacking, boosting, bagging) within the volatility-forecasting literature and clearly frames the contribution of merging these methods with GARCH outputs. • Methodology and Results detailing data collection, GARCH specification, ensemble architecture, validation metrics (RMSE, MAPE, directional accuracy, etc.), and robustness checks. Clear tables, figures, and Python/R code snippets should back every claim. • Discussion and Conclusion that interpret findings for practitioners and academics, address limitations, and outline avenues for future research. I will supply the original proposal, datasets, and any preliminary code. Please follow typical finance-journal conventions (abstract, JEL codes, numbered sections, APA or journal-specific references). All writing must be original, concise, and formatted in LaTeX or Word as you prefer, ready for submission when complete. Acceptance criteria 1. 100% plagiarism-free text verified by Turnitin or similar. 2. Empirical results reproducible with commented scripts. 3. Complete manuscript delivered within the agreed page range and structural requirements. If you have prior publications in quantitative finance or experience with GARCH and ensemble methods, let me know; that expertise will be a strong plus.