A股系统性风险/传染效应分析。当用户说"系统性风险"、"systematic risk"、"传染效应"、"风险传染"、"系统风险"、"金融风险传导"时触发。基于 cn-stock-data 获取数据,评估系统性风险水平。支持 formal/brief 两种输出风格。
Scanned 9/7/2026
Install to Claude Code
npx -y skills add aifinlab/FinClaw --skill a-share-systematic-risk --agent claude-codeInstalls into .claude/skills of the current project.
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---
name: a-share-systematic-risk
description: A股系统性风险/传染效应分析。当用户说"系统性风险"、"systematic risk"、"传染效应"、"风险传染"、"系统风险"、"金融风险传导"时触发。基于 cn-stock-data 获取数据,评估系统性风险水平。支持 formal/brief 两种输出风格。
---
# 系统性风险/传染效应分析助手
## 数据获取
通过 cn-stock-data skill 获取数据:
- **市场数据**: 指数/个股/债券/汇率
- **资金数据**: 融资余额/北向资金
- **宏观数据**: 信用利差/流动性指标
## 分析工作流
### Step 1: 系统性风险指标
- 市场波动率(VIX类):恐慌程度
- 信用利差:AA-vs国债利差扩大=风险上升
- 流动性指标:银行间利率/质押回购利率
- 相关性指标:股票间相关性飙升=系统风险
### Step 2: 传染效应分析
- Granger因果检验:风险传导方向
- DCC-GARCH:动态条件相关性
- CoVaR:条件在险价值,衡量溢出效应
- 网络分析:风险传播的网络拓扑
### Step 3: 风险状态评估
- 综合风险指数:多指标加权合成
- 风险状态分类:低/中/高/极端
- 历史对比:当前风险水平的历史分位
- 预警信号:哪些指标率先恶化
### Step 4: 防御策略
- 系统风险升高时降低仓位
- 增加对冲:股指期货/期权保护
- 转向防御性资产:高股息/低波动
- 现金为王:极端风险时提高现金比例
### Step 5: 输出报告
## 输出格式
### formal 风格(研报级)
```
# 系统性风险评估报告
## 一、风险指标
| 指标 | 数值 | 分位数 | 状态 |
|------|------|--------|------|
## 二、传染效应
[风险传导路径]
## 三、风险状态
[综合评估与历史对比]
## 四、防御建议
```
### brief 风格(快速分析)
```
## 系统性风险速览
- 综合风险指数 45 (P55),中等
- 信用利差 85bp,正常
- 市场相关性 0.45,未见异常
- 状态:正常,无需特别防御
```
参考 `references/systematic-risk-guide.md` 获取详细方法论与 A股实证研究。
## 使用示例
### 示例 1: 基本使用
```python
# 调用 skill
result = run_skill({
"param1": "value1",
"param2": "value2"
})
```
### 示例 2: 命令行使用
```bash
python scripts/run_skill.py --input data.json
```
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