CSR

Introduction:

Dr. Sayantee Jana, Assistant Professor in the Department of Mathematics, IIT, Hyderabad, leads a groundbreaking project introducing Generalized Multivariate Analysis of Variance (GMANOVA) models for skewed and volatile financial and actuarial data during catastrophic events, enhancing the accuracy of modeling in these domains.

Objective:

This project addresses the limitations of traditional normal distribution in financial modeling, focusing on skewed and volatile datasets during catastrophes. Leveraging Multivariate Skew t (MST) distribution, the team aims to pioneer the MST GMANOVA model. By incorporating skewness, volatility, and temporal aspects, this model promises enhanced decision-making for financial variables. The objectives include defining the MST GMANOVA model, formulating likelihood under MST distribution, applying Expectation Maximization algorithm, conducting Likelihood Ratio Tests, and modeling real insurance risks data during catastrophes. The ultimate goal is to provide a robust statistical framework for multivariate financial data, ensuring accurate modeling under various scenarios.

Timeline & Budget:

Year 1: SRF, Consumables, Contingency, Travel - Total: 7 lakhs

Year 2: SRF, Consumables, Contingency, Travel - Total: 7 lakhs

Proposer Details:

Dr. Sayantee Jana, Assistant Professor, Department of Mathematics, IIT Hyderabad.