A股尾部对冲/黑天鹅保护策略。当用户说"尾部对冲"、"tail hedge"、"黑天鹅保护"、"极端风险对冲"、"保险策略"、"protective put"、"崩盘保护"时触发。基于 cn-stock-data 获取数据,设计尾部风险对冲方案。支持 formal/brief 两种输出风格。
Scanned 9/7/2026
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---
name: a-share-tail-hedge
description: A股尾部对冲/黑天鹅保护策略。当用户说"尾部对冲"、"tail hedge"、"黑天鹅保护"、"极端风险对冲"、"保险策略"、"protective put"、"崩盘保护"时触发。基于 cn-stock-data 获取数据,设计尾部风险对冲方案。支持 formal/brief 两种输出风格。
---
# 尾部对冲/黑天鹅保护助手
## 数据获取
通过 cn-stock-data skill 获取数据:
- **期权数据**: OTM Put期权价格
- **VIX类指标**: 波动率指数
- **组合数据**: 当前持仓与风险敞口
## 分析工作流
### Step 1: 尾部风险评估
- 历史最大回撤:A股历史极端下跌幅度
- VaR/CVaR:组合的尾部风险度量
- 尾部概率估计:极端事件发生的概率
- 当前风险环境:波动率/信用利差/流动性指标
### Step 2: 对冲工具选择
- OTM Put期权:最直接的尾部保护
- Put Spread:买深度OTM Put+卖更深OTM Put(降低成本)
- VIX类产品:波动率飙升时获利
- 股指期货空头:简单但成本高(放弃上行)
### Step 3: 对冲方案设计
- 保护水平:组合下跌X%时开始保护
- 对冲比例:保护组合的百分比(通常50-100%)
- 对冲成本预算:年化成本占组合的0.5-2%
- 展期策略:月度/季度展期OTM Put
### Step 4: 成本优化
- Collar策略:买Put+卖Call,零成本或低成本
- Put Spread降低权利金支出
- 择时对冲:风险指标升高时加大对冲
- 结构化产品:利用波动率偏斜降低成本
### Step 5: 输出报告
## 输出格式
### formal 风格(研报级)
```
# 尾部对冲方案报告
## 一、风险评估
| 指标 | 数值 | 评估 |
|------|------|------|
## 二、对冲方案
[工具选择、合约明细]
## 三、成本分析
[对冲成本、成本优化]
## 四、情景测试
[极端场景下的保护效果]
```
### brief 风格(快速分析)
```
## 尾部对冲速览
- 组合CVaR(99%) = -8.5%
- 方案:买入5% OTM Put Spread
- 年化成本约0.8%
- 保护效果:极端下跌时减少损失60%
```
参考 `references/tail-hedge-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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