Using S&P 500 data, this strategy calculates NEVI from VIX and GARCH forecasts, trading long below the 2nd decile and short above the 8th, holding positions for 30 days.
ASSET CLASS: CFDs, ETFs, funds, futures | REGION: United States | FREQUENCY:
Daily | MARKET: equities | KEYWORD: Net, Emotional. Volatility, Index
I. STRATEGY IN A NUTSHELL
Use S&P 500 data to compute GARCH(1,1) forecasts and derive the Net Emotional Volatility Index (NEVI = VIX − AGF). Go long when NEVI is very low, short when very high, holding positions 30 days.
II. ECONOMIC RATIONALE
NEVI isolates emotional (sentiment-driven) volatility from total market volatility, offering a purer measure of investor sentiment for exploiting volatility-driven market momentum.
III. SOURCE PAPER
Volatility Based Sentiment Indicators for Timing the Markets [Click to Open PDF]
Cacia, Tzvetkov, School of Economics and Management, Lund University
<Abstract>
VIX, published by the Chicago Board Options Exchange, is a well known implied volatility estimator. In this paper we assess its capability to be used as a sentiment indicator, and to give signals for a short term investment strategy. It will be proved and discussed how VIX-based strategies – also known as “Contrarian” strategies – can be effective as they lead to higher returns than the market. We also propose a purer sentiment indicator derived from VIX that gives more accurate market timing signals. We call this indicator “Net Emotional Volatility Index” (NEVI). It proves to have interesting properties, a highly significant statistical relationship with the market return, and a considerable power to time the market. The results of our back-testing for the period 2001-2002 and 2006-2007 using the two indicators are presented, compared and discussed. Possible explanations of the information that NEVI brings and why its signals work are provided.
IV. BACKTEST PERFORMANCE
| Annualised Return | 12.65% |
| Volatility | 5.92% |
| Beta | -0.25 |
| Sharpe Ratio | 1.46 |
| Sortino Ratio | N/A |
| Maximum Drawdown | N/A |
| Win Rate | 57% |
V. FULL PYTHON CODE
from AlgorithmImports import *
#endregion
# https://quantpedia.com/strategies/net-emotional-volatility-index-effect/
#
# Historical S&P 500 index data are used to estimate the coefficients in the GARCH (1,1) model. Once the GARCH coefficients are obtained, for each day, the investor forecasts the volatility
# for the next 30 days, reiterating the forecast, which means that every forecast becomes the last observation for the next forecast. The average of these values is called the Average GARCH
# Forecast (AGF). The investor then calculates the Net Emotional Volatility Index (NEVI) as the difference between the VIX and AGF. The historical NEVI index is then sorted into deciles, and
# the current NEVI is used as a “Momentum” indicator – the investor opens long on a position when the indicator goes below the 2nd decile and short on positions when it goes above the 8th decile.
# The position is held 30 days from the day of the last long or short signal.
import numpy as np
from arch import arch_model
class NetEmotionalVolatilityIndexEffect(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2008, 1, 1)
self.SetCash(100000)
self.period:int = 21
self.holding_period:int = 30
self.SetWarmUp(self.period + self.holding_period)
self.market:Symbol = self.AddEquity("SPY", Resolution.Daily).Symbol
self.vix:Symbol = self.AddData(QuandlVix, "CBOE/VIX", Resolution.Daily).Symbol # starts in 2004
self.days_held:int = 0
self.market_data:RollingWindow = RollingWindow[float](self.period)
self.volatility:RollingWindow = RollingWindow[float](self.period)
self.nevi_index:list[float] = []
def OnData(self, data):
# market data are present in the algorithm
if self.market in data and data[self.market]:
market_price:float = data[self.market].Value
if market_price != 0:
# store market price data
self.market_data.Add(market_price)
if self.market_data.IsReady:
# calcualte market daily returns
market_prices:np.ndarray = np.array([x for x in self.market_data][::-1])
market_returns:np.ndarray = (market_prices[:-1] / market_prices[1:] - 1) * 100
# forecast market volatility
exp_market_volatility:float = self.garch_vol_predict_volatility(market_returns, 1, 1, 30)
self.volatility.Add(exp_market_volatility)
if self.volatility.IsReady:
agf:float = np.mean([x for x in self.volatility])
# vix data are present in the algorithm
if self.vix in data and data[self.vix]:
vix:float = data[self.vix].Value
self.Plot("Expected Market Volatility", "Expected Market Volatility", exp_market_volatility)
self.Plot("VIX", "VIX", vix)
if vix != 0:
nevi:float = vix - agf
self.nevi_index.append(nevi)
# wait until at least 30 data points is present for NEVI
if len(self.nevi_index) >= self.period and not self.Portfolio[self.market].Invested and not self.IsWarmingUp:
low_decile:float = np.percentile(self.nevi_index[:-1], 20)
high_decile:float = np.percentile(self.nevi_index[:-1], 80)
recent_nevi:float = self.nevi_index[-1]
if recent_nevi > high_decile:
self.SetHoldings(self.market, -1)
self.days_held = 0
elif recent_nevi < low_decile:
self.SetHoldings(self.market, 1)
self.days_held = 0
if self.Portfolio[self.market].Invested:
self.days_held += 1
if self.days_held == self.holding_period:
self.Liquidate(self.market)
self.days_held = 0
def garch_vol_predict_volatility(self, returns, p, q, h) -> float:
am = arch_model(returns, mean='Zero', vol='GARCH', p=p, q=q)
res = am.fit(disp='off')
forecasts = res.forecast(horizon=h)
return np.sqrt(forecasts.variance.values[-1,:][0] * 252)
class QuandlVix(PythonQuandl):
def __init__(self):
self.ValueColumnName = "close"