Original paper
Abstract
I investigate the relationship between carry trades and tail risk for a panel of commodity futures contracts. Unlike other asset classes, carry in commodities is highly volatile both in the time series and in the cross section. By using a panel quantile regression with commodity fixed effect, I document the tail-specific effect of carry on the conditional distribution of futures returns both in the short-term and in longer-term. The main empirical results show that carry has a significant effect on tail risk mostly in the short term and for the front end of the futures curve. In addition, the empirical evidence shows that such relationship can be explained by the role of non-natural hedgers, such as non-commercial traders, money managers and index traders, which tend to unwind their net-long futures positions when exposed to deteriorating aggregate financial conditions and increasing market uncertainty. This is consistent with existing theoretical models in which speculators and insurance providers are subject to limited risk capacity and financing liquidity constraints.
Keywords:Â Commodity Markets, Carry, Panel Quantile Regressions, Tail Risk, Empirical Asset Pricing, Risk Management, Bayesian Regressions.
Trading rules
- The investment universe is all commodity futures contracts.
- The approach involves buying the top quintile (20%) of commodities exhibiting the highest roll-returns.
- Simultaneously, sell short the bottom quintile (20%) of commodities showing the lowest roll-returns.
- Maintain the long-short positions for a duration of one month.
- Contracts in each quintile are equally-weighted.
Python code
Backtrader
import backtrader as bt
class RollRankStrategy(bt.Strategy):
def __init__(self):
# divide investment universe into quintiles
self.quintiles = 5
# get the number of contracts in each quintile
self.contracts_per_quintile = len(self.datas) // self.quintiles
# get the length of roll period
self.roll_period = 30 # assuming the trading period is 1 month
# calculate the number of contracts to trade long/short
self.num_longs_shorts = self.contracts_per_quintile // 5
def next(self):
# calculate roll-returns for all contracts
roll_returns = [d.close[-1] / d(-self.roll_period).close[0] for d in self.datas]
# sort contracts by roll-returns
ranks = sorted(range(len(roll_returns)), key=lambda x: roll_returns[x])
# go long on top 20% of contracts
for i in ranks[-self.num_longs_shorts:]:
self.buy(self.datas[i], size=1/self.num_longs_shorts)
# go short on bottom 20% of contracts
for i in ranks[:self.num_longs_shorts]:
self.sell(self.datas[i], size=1/self.num_longs_shorts)Note: This is just an example, and the code may need to be adjusted based on the specifics of the commodity futures contracts being traded.