Dynamic DCF Modeling Using OU Process and Monte Carlo
Project Overview
The model simulates mean-reverting interest rates and quantifies valuation risk using thousands of Monte Carlo scenarios.
Tech Stack
Team
Mentors
- Bhavya Saini
- Hridya Jain
- Anantha Krishnan
- Chethana Ramesh
- Ashmit Singhal
Mentees
- Atharva Banni
- Harsh Vardhan
- Shashank
- Shamik Neendoor
- Adi Nahata
Problem Statement
Static discount-rate DCF models do not capture mean-reverting rates, shocks and uncertainty.
Objectives
- - Implement OU-based dynamic DCF
- - Estimate OU parameters from historical data
- - Run Monte Carlo rate-path simulations
- - Integrate VaR/CVaR and confidence analytics
Methodology
The project follows a structured implementation approach that includes Historical rate ingestion and preprocessing, OU equation implementation with MLE calibration, Monte Carlo engine construction, and Comparison against static DCF baseline. These steps are executed iteratively to validate assumptions, improve performance, and ensure reliable delivery of the final solution.
Expected Outcome
By the end of this project, the team is expected to deliver Uncertainty-aware valuation distribution, Robust risk metrics for rate-sensitive assets, and Improved realism versus static valuation. Together, these outcomes reflect both technical feasibility and practical value for demos, evaluation, and future scaling.
Future Scope
- - Multi-factor stochastic models
- - ML-based regime and parameter estimation
- - Portfolio-level risk tools