债券、股票 · 美国 · ETF、期权 · 每月 · 美国 Free

使用 VIX 期权进行投资组合对冲

学术论文

作者A Study in Portfolio Diversification Using VIX Options [点击查看论文]

论文链接https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2792358

策略概要

该策略采用60%的股票和40%的债券投资组合，股票使用SPDR标准普尔500指数ETF（SPY），债券使用iShares 7-10年期美国国债ETF（IEF）。投资组合分配0-100个基点（bps）给VIX看涨期权，投资于VIX期货的1个月、2个月、3个月和4个月期权。VIX看涨期权的权重根据VIX指数水平调整：若VIX在15至30之间，则为1%；若在30至50之间，则为0.5%；否则为0%。期权在到期前展期，若具有内在价值则卖出。股票/债券部分每月重新平衡，期权销售所得现金再投资于该投资组合。该策略利用VIX期权对冲波动性，重点是基于市场状况的系统性动态分配。

II. 策略合理性

该策略通过每月将投资组合的固定百分比分配给VIX看涨期权，利用波动率的均值回复特性。当VIX低于均值时，购买更多期权；当VIX高于均值时，购买较少期权。该策略旨在确定期权的最佳实值性、到期时间和最小资本要求。仅当VIX在15%至50%之间时才购买看涨期权，确保投资者避免为可能不必要的期权支付过高的费用，从而调整投资组合的风险和回报状况。

回测表现

波动率8.44%

夏普比率1.02

索提诺比率0.504

最大回撤-10.47%

胜率35%

完整 Python 代码

1from AlgorithmImports import *
2class PortfolioHedgingUsingVIXOptions(QCAlgorithm):
3def Initialize(self):
4self.SetStartDate(2010, 1, 1)
5self.SetCash(1000000)
6
7data = self.AddEquity("SPY", Resolution.Minute)
8data.SetLeverage(5)
9self.spy = data.Symbol
10
11data = self.AddEquity("IEF", Resolution.Minute)
12data.SetLeverage(5)
13self.ief = data.Symbol
14
15data = self.AddEquity("VIXY", Resolution.Minute)
16data.SetLeverage(5)
17self.vix = data.Symbol
18
19option = self.AddOption('VIXY', Resolution.Minute)
20option.SetFilter(-20, 20, 25, 35)
21
22def OnData(self,slice):
23for i in slice.OptionChains:
24    chains = i.Value
25    # Max 2 positions - spy and ief are opened. That means option expired.
26    invested = [x.Key for x in self.Portfolio if x.Value.Invested]
27    if len(invested) <= 2:
28        calls = list(filter(lambda x: x.Right == OptionRight.Call, chains))
29        
30        if not calls: return
31    
32        underlying_price = self.Securities[self.vix].Price
33        expiries = [i.Expiry for i in calls]
34        
35        # Determine expiration date nearly one month.
36        expiry = min(expiries, key=lambda x: abs((x.date() - self.Time.date()).days - 30))
37        strikes = [i.Strike for i in calls]
38        
39        # Determine out-of-the-money strike.
40        otm_strike = min(strikes, key = lambda x:abs(x - (float(1.35) * underlying_price)))
41        otm_call = [i for i in calls if i.Expiry == expiry and i.Strike == otm_strike]
42
43        if otm_call:
44            # Option weighting.
45            weight = 0.0
46            
47            if underlying_price >= 15 and underlying_price <= 30:
48                weight = 0.01
49            elif underlying_price > 30 and underlying_price <= 50:
50                weight = 0.005
51              
52            if weight != 0: 
53                options_q = int((self.Portfolio.MarginRemaining * weight) / (underlying_price * 100))
54
55                # Set max leverage.
56                self.Securities[otm_call[0].Symbol].MarginModel = BuyingPowerModel(5)
57                
58                # Sell out-the-money call.
59                self.Buy(otm_call[0].Symbol, options_q)
60                
61                self.SetHoldings(self.spy, 0.6)
62                self.SetHoldings(self.ief, 0.4)