Equity · Europe · futures · Daily · Europe Free

Dividend Risk Premium Strategy

Academic paper

Paper linkhttps://quantpedia.com/www/Credit_Suisse_Dividend_Risk_Premium.pdf

Strategy in a nutshell

This strategy maintains constant exposure to implied dividends by rolling positions between front- and back-month dividend futures, going long on 1-year constant maturity dividend futures while hedging equity exposure via short SX5E positions. The daily hedge ratio is calculated using regression over the prior two weeks to manage risk effectively.

Economic rationale

A dividend risk premium exists due to overestimation of single-stock dividend cuts, underestimation of diversification benefits, liquidity constraints, and systematic mispricing from large positions held by banks. These factors create a persistent opportunity in dividend futures markets.

Backtest performance

Annualised return12.9%

Volatility11.6%

Beta0.227

Sharpe ratio1.11

Maximum drawdown-28.8%

Win rate55%

Full Python code

1from AlgorithmImports import *
2from scipy import stats
3import numpy as np
4#endregion
5class DividendRiskPremiumStrategy(QCAlgorithm):
6def Initialize(self):
7self.SetStartDate(2010, 1, 1)
8self.SetCash(100000)
9
10self.dividend_futures = ['FEXD1', 'FEXD2', 'FEXD3']
11
12self.fexd1 = None
13for i, div_symbol in enumerate(self.dividend_futures):
14    data = self.AddData(QuantpediaFutures, div_symbol, Resolution.Daily)
15    data.SetLeverage(10)
16    data.SetFeeModel(CustomFeeModel())
17    
18    # store nearby contract
19    if i == 0:
20        self.fexd1 = data.Symbol
21
22# STOXX 50 future
23data = self.AddData(QuantpediaFutures, "EUREX_FSTX1", Resolution.Daily)
24data.SetLeverage(10)
25data.SetFeeModel(CustomFeeModel())
26self.SX5E = data.Symbol
27
28# daily price
29self.data = {}
30self.period = 10
31self.max_missing_days = 5
32for symbol in [self.fexd1, self.SX5E]:
33    self.data[symbol] = RollingWindow[float](self.period)
34
35self.selection_flag = False
36self.recent_month:int = -1
37
38def OnData(self, data):
39# store daily prices
40if self.fexd1 in data and data[self.fexd1] and self.SX5E in data and data[self.SX5E]:
41    self.data[self.fexd1].Add(data[self.fexd1].Value)
42    self.data[self.SX5E].Add(data[self.SX5E].Value)
43else:
44    if (self.Securities[self.fexd1].GetLastData() and (self.Time.date() - self.Securities[self.fexd1].GetLastData().Time.date()).days > self.max_missing_days) or \
45        (self.Securities[self.SX5E].GetLastData() and (self.Time.date() - self.Securities[self.SX5E].GetLastData().Time.date()).days > self.max_missing_days):
46        self.data[self.fexd1].Reset()
47        self.data[self.SX5E].Reset()
48        self.Liquidate()
49if self.recent_month == self.Time.month:
50    return
51self.recent_month = self.Time.month
52if self.Time.month in [7, 1]:
53    self.Liquidate()
54    self.weights = [1, 0]   # divident future #2 and #3 weights
55    self.selection_flag = True
56
57if self.Portfolio.Invested or self.selection_flag:
58    if self.data[self.fexd1].IsReady and self.data[self.SX5E].IsReady:
59        dividend_future_prices = np.array([x for x in self.data[self.fexd1]])
60        market_prices = np.array([x for x in self.data[self.SX5E]])
61        
62        dividend_future_returns = dividend_future_prices[:-1] / dividend_future_prices[1:] - 1
63        market_returns = market_prices[:-1] / market_prices[1:] - 1
64        if not all(x==0 for x in dividend_future_returns):
65            slope, intercept, r_value, p_value, std_err = stats.linregress(market_returns, dividend_future_returns)
66            
67            # dividend futures exposure
68            self.SetHoldings(self.dividend_futures[1], self.weights[0])
69            self.SetHoldings(self.dividend_futures[2], self.weights[1])
70            
71            # adjust weights every invested day
72            if self.weights[0] >= 0.008:
73                self.weights[0] -= 0.008
74            
75            if self.weights[0] <= 0.992:
76                self.weights[1] += 0.008
77            
78            # hedge
79            self.SetHoldings(self.SX5E, -abs(slope))
80            
81            self.selection_flag = False
82        else:
83            self.Liquidate()
84# Quantpedia data.
85# NOTE: IMPORTANT: Data order must be ascending (datewise)
86class QuantpediaFutures(PythonData):
87def GetSource(self, config, date, isLiveMode):
88return SubscriptionDataSource("data.quantpedia.com/backtesting_data/futures/{0}.csv".format(config.Symbol.Value), SubscriptionTransportMedium.RemoteFile, FileFormat.Csv)
89def Reader(self, config, line, date, isLiveMode):
90data = QuantpediaFutures()
91data.Symbol = config.Symbol
92
93if not line[0].isdigit(): return None
94split = line.split(';')
95
96data.Time = datetime.strptime(split[0], "%d.%m.%Y") + timedelta(days=1)
97data['back_adjusted'] = float(split[1])
98data['spliced'] = float(split[2])
99data.Value = float(split[1])
100return data
101# Custom fee model.
102class CustomFeeModel(FeeModel):
103def GetOrderFee(self, parameters):
104fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
105return OrderFee(CashAmount(fee, "USD"))