Dự án ứng dụng mô hình kinh tế lượng EGARCH(1,1) bằng ngôn ngữ R nhằm phân tích đặc tính rủi ro (phân cụm, bất đối xứng) và dự báo biến động của chỉ số VN-Index (2015 - 2026). Với sai số MAE đạt 0.5%, dự án cung cấp đường cơ sở rủi ro (baseline risk) vững chắc, hỗ trợ đo lường VaR và Stress Testing trong quản trị danh mục đầu tư thực chiến.

My project builds upon the study referenced below. I have proposed a potential follow-up experiment to extend the study’s findings. The project uses simulated data, structured and analyzed as if collected from a real experiment. The results are not scientific evidence; they demonstrate my skills in biological questioning, statistics, experimental design, and programming.

Primera actividad publicada en R de Métodos para el Análisis Económico del grupo teórico #1, "Ejemplos prácticos: Clase 1".

Assignment 5

Cancer datasets often contain thousands of measurements for each sample. For example, a single cell line or patient tumor may have expression measurements for thousands of genes. That creates an important problem: How can we see patterns in data with thousands of dimensions? Principal Component Analysis, or **PCA**, is one way to reduce a large dataset to a smaller number of summary variables while keeping as much of the original information as possible. In this activity, we will use PCA to explore the NCI-60 cancer cell line dataset.

An End-to-End Pipeline with Coarse-to-Fine Tuning and Meta-Ensembling By Jay Prakash (iamjai@live.com)