Tail Risk Hedging with Cheap Options
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Tail Risk Hedging: The Search for Cheap Options
Poh Ling Neo; Chyng Wen Tee
- SGSingapore University of Social Sciences
- SGSingapore Management University
- ?Singapore Management University - Lee Kong Chian School of Business
Strategy in a nutshell
Universe: S&P 500 (via ETFs like SPY, SXR8.DE, or CFDs) plus U.S. stock put options (OptionMetrics IvyDB, CRSP).
Method: At each month-end, allocate 2% risk budget to the cheapest 20% of OTM puts (≈90 options, ~40 on SPX constituents), chosen with delta ≈ –10% and 6–12 months to expiry. Equal dollar-weighted across puts. The remaining 98% capital is invested in the S&P 500 index. Portfolio rebalanced monthly.
Economic rationale
Tail-risk hedging protects against extreme downturns. Standard insurance via options drags returns, but selecting cheap OTM puts by a price-based heuristic provides efficient downside protection with minimal cost. This enhances portfolio resilience in crises without materially compromising long-run performance, making the strategy an effective balance between market exposure and crash insurance.
Backtest performance
Full Python code
from AlgorithmImports import *
from typing import List, Dict
# endregion
class TailRiskHedgingwithCheapOptions(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2010, 1, 1)
self.SetCash(1_000_000)
seeder = FuncSecuritySeeder(self.GetLastKnownPrices)
self.SetSecurityInitializer(lambda security: seeder.SeedSecurity(security))
self.leverage: int = 5
self.quantile: int = 5
self.min_expiry: int = 6 * 30
self.max_expiry: int = 12 * 30
self.min_delta: float = 0.1
self.exchanges: List[str] = ['NYS', 'NAS', 'ASE']
self.active_stock_universe: List[Symbol] = []
self.monthly_subscribed_contracts: List[OptionContract] = []
self.market: Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
self.option_port_weight: float = 0.02
self.market_port_weight: float = 0.98
self.fundamental_count: int = 100
self.fundamental_sorting_key = lambda x: x.MarketCap
self.selection_flag: bool = False
self.rebalance_flag: bool = False
self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.FundamentalSelectionFunction)
self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
self.UniverseSettings.DataNormalizationMode = DataNormalizationMode.Raw
self.Schedule.On(self.DateRules.MonthStart(self.market), self.TimeRules.BeforeMarketClose(self.market, 0), self.Selection)
def OnSecuritiesChanged(self, changes: SecurityChanges) -> None:
for security in changes.AddedSecurities:
security.SetFeeModel(CustomFeeModel())
security.SetLeverage(self.leverage)
def FundamentalSelectionFunction(self, fundamental: List[Fundamental]) -> List[Symbol]:
if not self.selection_flag:
return Universe.Unchanged
selected: List[Fundamental] = [
f for f in fundamental if f.HasFundamentalData and \
f.MarketCap != 0 and \
f.SecurityReference.ExchangeId in self.exchanges
]
if len(selected) > self.fundamental_count:
selected = [x for x in sorted(selected, key=self.fundamental_sorting_key, reverse=True)[:self.fundamental_count]]
self.monthly_subscribed_contracts.clear()
self.active_stock_universe = list(map(lambda stock: stock.Symbol, selected))
return self.active_stock_universe
def OnData(self, slice: Slice) -> None:
if self.selection_flag:
self.selection_flag = False
for symbol in self.active_stock_universe:
# subscribe to contract
contracts: List[Symbol] = self.OptionChainProvider.GetOptionContractList(symbol, self.Time)
underlying_price: float = self.Securities[symbol].Price
strikes: List[float] = [i.ID.StrikePrice for i in contracts]
if len(strikes) <= 0:
continue
otm_strike: float = min(strikes, key=lambda x: abs(x - (underlying_price * (1. + self.min_delta))))
otm_puts: List[Symbol] = self.SelectContracts(contracts, OptionRight.Put, otm_strike)
# make sure there are enough contracts
if len(otm_puts) > 0:
# sort by expiry and subscribe nearest contract
nearest_contracts: Symbol = sorted(otm_puts, key=lambda item: item.ID.Date)[0]
option: OptionContract = self.AddOptionContract(nearest_contracts, Resolution.Daily)
option.PriceModel = OptionPriceModels.CrankNicolsonFD()
self.monthly_subscribed_contracts.append(option)
self.rebalance_flag = True
if len(self.monthly_subscribed_contracts) != 0 and slice.OptionChains.Count != 0 and self.rebalance_flag:
self.rebalance_flag = False
option_price: Dict[Symbol, float] = {
c.Symbol : c.AskPrice for c in self.monthly_subscribed_contracts
}
if len(option_price) < self.quantile:
return
quantile: int = int(len(option_price) / self.quantile)
sorted_by_opt_price: List[OptionContract] = sorted(option_price, key=option_price.get)
cheapest: List[OptionContract] = sorted_by_opt_price[:quantile]
# trade execution
for option_c in cheapest:
if option_c in slice and slice[option_c]:
self.SetHoldings(option_c, self.option_port_weight / len(cheapest))
self.SetHoldings(self.market, self.market_port_weight)
def SelectContracts(self, contracts: List[Symbol], option_right: int, strike: float) -> List[Symbol]:
return [i for i in contracts if i.ID.OptionRight == option_right \
and i.ID.StrikePrice == strike and self.min_expiry < (i.ID.Date - self.Time).days < self.max_expiry]
def Selection(self) -> None:
self.selection_flag = True
# Custom fee model
class CustomFeeModel(FeeModel):
def GetOrderFee(self, parameters):
fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))