Vix Blues, Large Close to Close Declines in Vix

This monday, we were witnesses to a rather large decline in Vix. Taking a quick look at how often drops like this happen and how has Vix behaved after large single day drops

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import scipy as sp
import seaborn as sns
import quandl

%matplotlib inline

vix = quandl.get("YAHOO/INDEX_VIX", authtoken="YOUR-KEY")
vix.index = pd.to_datetime(vix.index)
vix.drop({"Open", "High", "Low", "Close", "Volume"}, inplace=True, axis=1)
vix.columns = ["close"]
vix["pct"] = vix["close"].pct_change()

There havent been that many instances of Vix gapping down more than 20%. Mondays declide has made it into the top three of the hall of shame, only topped by august 2011 and october 2008

heat = vix[vix["pct"] <= -0.2].transpose().iloc[1:]
heat.columns = x: str(x)[:10])

cmap = sns.dark_palette("red", as_cmap=True)

fig, ax = plt.subplots(1, figsize=(16, 9))
ax = sns.heatmap(heat, square=True, cmap=cmap, linewidths=1,
annot=True, cbar=False, annot_kws={"size":21})
plt.title("Hall of shame - Top 10 close to close pct declines in Vix")


20%+ declines are indeed rare while 20%+ spikes are not (comparatively speaking)

ecd = np.arange(1, len(vix)+1) / len(vix)

plt.figure(figsize=(11, 11))
plt.plot(np.sort(vix["pct"]), ecd, linestyle="none", marker=".", alpha=0.55, color="#555555")
plt.axvline(-0.26, linestyle="--", color="crimson", label="26% Decline on Monday 24’th of April 2017")
plt.xlabel("Vix single day pct change")
plt.legend(loc="center right")


Not much really to look at on the scatter, since the sample size is very small on the 20%+ declines

def rets(df, shift):
out = (df.shift(-shift) / df) - 1
return out

rets_10 = rets(vix["close"], 21).where(vix["pct"] <= -0.1).dropna()
vix_10 = vix["pct"][vix["pct"] <= -0.1].iloc[:-1]
rets_20 = rets(vix["close"], 21).where(vix["pct"] <= -0.2)

slope, intercept, r_val, p_val, std_err = sp.stats.linregress(vix_10, rets_10)
rets_10_pred = intercept + slope * vix_10

plt.figure(figsize=(16, 9))
plt.plot(vix_10, rets_10_pred, linestyle="-", label="Linreg")
plt.scatter(vix_10, rets_10, color="#333333", alpha=0.55, s=21, label="Vix declines >= 10%")
plt.scatter(vix["pct"], rets_20, color="crimson", alpha=0.89, s=42, label="Vix declines >= 20%")
plt.title("VIx returns 21 days after large single day declines")
plt.ylabel("Vix % return 21 days later")
plt.xlabel("Vix single day decline pct (from close to close)")
plt.axhline(linestyle="--", linewidth=1, color="#333333")
plt.xticks(np.arange(-0.3, -0.09, 0.01))
plt.legend(loc="upper left")


According to past instances, Vix should head south again after gathering itself

def getRets(df, days, pct, pct_to):
df = df.reset_index()
df_out = pd.DataFrame()
for index, row in df.iterrows():
if row["pct"] <= pct and row["pct"] > pct_to and df2["pct"].iloc[index-1] > pct:
ret = df2["close"].iloc[index:index+days]
ret = np.log(ret).diff()
ret.iloc[:1] = 0
ret.reset_index(drop=True, inplace=True)
df_out[index] = ret

return df_out

vix_21rets_10 = getRets(vix, 55, -0.1, -0.2).mean(axis=1).cumsum()
vix_21rets_20 = getRets(vix, 55, -0.2, -1).mean(axis=1).cumsum()

plt.figure(figsize=(16, 9))
plt.plot(vix_21rets_10, color="#555555", label="Vix single day declines <= 10% and > 20%")
plt.plot(vix_21rets_20, color="crimson", label="Vix single day declines > 20%")
plt.title("Vix returns after large single day declines")
plt.ylabel("Vix % return")
plt.xlabel("Days from large single day decline")
plt.axhline(linestyle="--", linewidth=1, color="#333333")
plt.xticks(np.arange(0, 56, 5))
plt.legend(loc="upper right")


Thanks for your time


8 thoughts on “Vix Blues, Large Close to Close Declines in Vix

  1. Quantocracy's Daily Wrap for 04/26/2017 | Quantocracy

  2. Just try to following your Python codes, I find it is not easy if codes shown in the this web don’t show the indentation level of your statements.
    Some are easy to guest, some are not obvious.

  3. I think it is very difficult to come out with reliable rules as if the large move was random, when it was likely to be driven by the outcome of a specific event (French elections)

  4. I agree, its a neverending mexican standoff between quatitative and fundamental points of view. I did not mean to forecast anything, the sample size on large Vix declines is so miniscule that its not possible to conclude anything. The point is that Vix can go either way, but we have some historical instances to compare against, which is helpful in my opinion.

    Check out the fed rate decisions post to see what i mean, it suggested a rise in Vix after hike and a significant drop in Vix 30-40 trading days after that – again the sample size is small, but the information provided by past instnces at least give a ballpark of what could happen.

    Thanks for you comment

  5. I for one prefer to read the code along with the text as you have done here. Nice job, thank you.

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