手动计算波动率与内置函数不一样

问题描述

有人可以帮助我了解我错了吗？我不知道为什么每一栏的波动率都不同...

这是我的代码的示例：

from math import sqrt
from numpy import around
from numpy.random import uniform
from pandas import DataFrame
from statistics import stdev

data = around(a=uniform(low=1.0,high=50.0,size=(500,1)),decimals=3)
df = DataFrame(data=data,columns=['close'],dtype='float64')
df.loc[:,'delta'] = df.loc[:,'close'].pct_change().fillna(0).round(3)

volatility = []

for index in range(df.shape[0]):
    if index < 90:
        volatility.append(0)
    else:
        start = index - 90
        stop = index + 1
        volatility.append(stdev(df.loc[start:stop,'delta']) * sqrt(252))

df.loc[:,'volatility1'] = volatility
df.loc[:,'volatility2'] = df.loc[:,'delta'].rolling(window=90).std(ddof=0) * sqrt(252)

print(df)

      close   delta  volatility1  volatility2
0    10.099   0.000     0.000000          NaN
1    26.331   1.607     0.000000          NaN
2    32.361   0.229     0.000000          NaN
3     2.068  -0.936     0.000000          NaN
4    36.241  16.525     0.000000          NaN
..      ...     ...          ...          ...
495  48.015  -0.029    46.078037    46.132943
496   6.988  -0.854    46.036210    46.178820
497  23.331   2.339    46.003184    45.837245
498  25.551   0.095    45.608260    45.792188
499  46.248   0.810    45.793012    45.769787

[500 rows x 4 columns]

非常感谢您！

解决方法

需要进行三个小更改。内联添加评论。由于包含端点，因此需要89（与许多其他python东西不同）。需要ddof=1，因为stdev默认使用它。 This article讨论的是numpy std而不是stdev，但是ddof在做什么的理论仍然相同。

此外，将来尝试将大小更改为95。调试时不需要其他405行，很高兴看到从0 / NaN转换为实际波动率，看到需要89而不是90

0与NaN的差异仍然存在。这是因为您附加了0并滚动了默认行为。我不确定这是否是故意的，所以我离开了。

from math import sqrt
from numpy import around
from numpy.random import uniform
from pandas import DataFrame
from statistics import stdev

data = around(a=uniform(low=1.0,high=50.0,size=(500,1)),decimals=3)
df = DataFrame(data=data,columns=['close'],dtype='float64')
df['delta'] = df['close'].pct_change().fillna(0).round(3)

volatility = []

for index in range(df.shape[0]):
    if index < 89: #change to 89
        volatility.append(0)
    else:
        start = index - 89 #change to 89
        stop = index
        volatility.append(stdev(df.loc[start:stop,'delta']) * sqrt(252))

df['volatility1'] = volatility
df['volatility2'] = df.loc[:,'delta'].rolling(window=90).std(ddof=1) * sqrt(252) #change to ddof=1

print(df)