# Econophysics

> Physics methods for financial markets

- Skill: `ffsshhttiikk/econophysics` (Agent Skill)
- Install (CLI): `npx skillmds@latest add ffsshhttiikk/econophysics`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ffsshhttiikk/econophysics/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: MIT
- Author: ffsshhttiikk (https://skillmd.com/u/ffsshhttiikk)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/ffsshhttiikk/econophysics

---


## What I do
- Apply statistical physics to financial systems
- Model market dynamics and crashes
- Analyze price fluctuations and correlations
- Predict market behavior patterns
- Study risk and wealth distributions

## When to use me
When analyzing financial data, modeling economic systems, or predicting market behavior using physics-based approaches.

## Key Concepts

### Statistical Properties
```
Power Laws: P(x) ~ x^(-α)
- Wealth distribution (Pareto)
- Stock returns (fat tails)
- City sizes (Zipf's law)

Scaling Laws:
- Volatility clustering
- Long-range correlations
- Multifractal behavior
```

### Key Models
```
Black-Scholes: Option pricing via PDE
ARCH/GARCH: Volatility clustering
Hawkes Processes: Event cascades
```

### Market Phenomena
- Fat-tailed return distributions
- Volatility clustering
- Anti-correlations in sign
- Market crashes (phase transitions)
- Herding behavior

### Correlation Analysis
```python
# Correlation matrix analysis
import numpy as np

# Eigenvalue spectrum (Marchenko-Pastur)
# Random matrix theory filtering
# Minimum spanning tree networks
```

### Risk Metrics
```python
# Value at Risk (VaR)
# Expected Shortfall
# Correlation breakdown scenarios
# Systemic risk indicators
```

### Applications
- Portfolio optimization
- Risk management
- Algorithmic trading
- Market microstructure
- Crisis prediction
- Cryptocurrencies

### Fat-Tailed Distributions
```python
# Lévy stable distributions
from scipy.stats import levy_stable
# Student-t for returns
# Power law for极端 events
```