Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-title: Actuarial Savings Analysis
+title: TVOG Analysis Dashboard
 emoji: 🐢
 colorFrom: purple
 colorTo: yellow
@@ -8,6 +8,134 @@ sdk_version: 5.31.0
 app_file: app.py
 pinned: false
 license: mit
+short_description: 'TVOG pricing: Monte Carlo vs. Black-Scholes tools.'
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# TVOG Analysis Dashboard 📊
+
+An interactive dashboard for analyzing Time Value of Options and Guarantees (TVOG) in Variable Annuity products with Guaranteed Minimum Accumulation Benefits (GMAB).
+
+[![Open in Spaces](https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg)](https://huggingface.co/spaces/alidenewade/tvog-analysis-dashboard)
+
+## 🎯 Overview
+
+This dashboard provides a comprehensive comparison between **Monte Carlo simulation** and **Black-Scholes-Merton analytical solutions** for pricing variable annuity guarantees. It's designed specifically for actuaries, finance professionals, economists, and academics working in insurance and financial risk management.
+
+## ✨ Key Features
+
+### 🔧 Interactive Controls
+- **Monte Carlo Parameters**: Adjustable scenario counts (1K-50K), risk-free rates, volatility levels
+- **Product Configuration**: Customizable sum assured, policy counts, and maturity periods  
+- **Model Point Analysis**: Flexible premium ranges with configurable test points
+
+### 📈 Four Analysis Modules
+
+1. **TVOG Comparison**: Side-by-side Monte Carlo vs Black-Scholes results with convergence ratios
+2. **Simulation Paths**: Account value trajectory visualization with guarantee levels
+3. **Distribution Analysis**: Statistical distributions of final values and GMAB payouts
+4. **Convergence Analysis**: Monte Carlo convergence validation against analytical solutions
+
+### 📊 Professional Output
+- **Results Table**: Detailed numerical comparison data
+- **Real-time Updates**: Dynamic recalculation with parameter changes
+- **Statistical Overlays**: Theoretical distributions and error metrics
+- **Export-Ready Visualizations**: High-quality plots for presentations
+
+## 🚀 Getting Started
+
+### Online Usage
+Simply click the "Open in Spaces" badge above to access the live dashboard - no installation required!
+
+### Local Installation
+```bash
+git clone https://huggingface.co/spaces/alidenewade/tvog-analysis-dashboard
+cd tvog-analysis-dashboard
+pip install -r requirements.txt
+python app.py
+```
+
+## 🔬 Technical Background
+
+### Mathematical Foundation
+The dashboard implements:
+- **Geometric Brownian Motion** for account value simulation: `dS/S = r·dt + σ·ε·√dt`
+- **Black-Scholes-Merton Formula** for European put option pricing
+- **Risk-Neutral Valuation** with Monte Carlo scenarios
+
+### Key Assumptions
+- No policy decrements (mortality/lapse rates = 0)
+- No management fees for clean comparison
+- Constant risk-free rate environment
+- Log-normal asset return distribution
+
+## 👥 Target Audience
+
+### Primary Users
+- **Actuaries**: Pricing and reserving analysis for variable annuity products
+- **Risk Managers**: Quantifying guarantee costs and capital requirements
+- **Product Developers**: Designing and testing new guarantee features
+- **Academics**: Teaching and researching financial guarantee valuation
+
+### Use Cases
+- **Product Pricing**: Determine fair value of GMAB guarantees
+- **Model Validation**: Compare simulation results with analytical benchmarks
+- **Sensitivity Analysis**: Test impact of parameter changes on guarantee costs
+- **Educational Tool**: Demonstrate Monte Carlo vs analytical pricing methods
+
+## 📚 Methodology
+
+### Monte Carlo Simulation
+- Generates thousands of risk-neutral scenarios
+- Simulates account value paths using geometric Brownian motion
+- Calculates present value of guarantee payouts at maturity
+- Provides statistical confidence through large sample sizes
+
+### Black-Scholes-Merton Benchmark
+- Analytical solution for European put option pricing
+- Provides exact theoretical value for comparison
+- Validates Monte Carlo convergence and accuracy
+- Offers computational efficiency for sensitivity analysis
+
+## 🎛️ Parameter Guide
+
+### Critical Parameters
+- **Scenarios**: Higher counts improve accuracy but increase computation time
+- **Volatility**: Key driver of option value - higher volatility increases TVOG
+- **Risk-Free Rate**: Affects both drift and discounting of future payouts
+- **Moneyness**: Initial account value relative to guarantee level
+
+### Recommended Settings
+- **For Quick Analysis**: 5,000-10,000 scenarios
+- **For Production**: 50,000+ scenarios
+- **For Presentations**: 10,000 scenarios (good balance of accuracy/speed)
+
+## 📖 Educational Value
+
+This dashboard serves as an excellent educational tool for:
+- **Understanding Monte Carlo Methods** in financial modeling
+- **Comparing Simulation vs Analytical** approaches
+- **Visualizing Financial Risk** through interactive plots
+- **Learning Option Pricing Theory** in insurance contexts
+
+## ���� Contributing
+
+Found a bug or have suggestions? Feel free to:
+- Open an issue on the repository
+- Submit a pull request with improvements
+- Share feedback through the Hugging Face community tab
+
+## 📄 License
+
+This project is open source and available under the MIT License.
+
+## 🙏 Acknowledgments
+
+Based on the lifelib savings library example, which demonstrates advanced actuarial modeling techniques for variable annuity products.
+
+---
+
+**Built with ❤️ for the actuarial and finance community**
+
+*For technical support or collaboration opportunities, connect through Hugging Face!*
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference