我有一个时间序列,看起来像这样(一个切片):
Date 3 7 10
2015-02-13 0.00021 -0.00078927 0.00407473
2015-02-16 0.0 -0.00343163 0.0
2015-02-17 0.0 0.0049406 0.00159753
2015-02-18 0.00117 -0.00123565 -0.00031423
2015-02-19 0.00091 -0.00253578 -0.00106207
2015-02-20 0.00086 0.00113476 0.00612649
2015-02-23 -0.0011 -0.00403307 -0.00030327
2015-02-24 -0.00179 0.00043229 0.00275874
2015-02-25 0.00035 0.00186069 -0.00076578
2015-02-26 -0.00032 -0.01435613 -0.00147597
2015-02-27 -0.00288 -0.0001786 -0.00295631
为了计算EWMA波动率,我实现了以下函数:
def CalculateEWMAVol (ReturnSeries, Lambda):
SampleSize = len(ReturnSeries)
Average = ReturnSeries.mean()
e = np.arange(SampleSize-1,-1,-1)
r = np.repeat(Lambda,SampleSize)
vecLambda = np.power(r,e)
sxxewm = (np.power(ReturnSeries-Average,2)*vecLambda).sum()
Vart = sxxewm/vecLambda.sum()
EWMAVol = math.sqrt(Vart)
return (EWMAVol)
def CalculateVol (R, Lambda):
Vol = pd.Series(index=R.columns)
for facId in R.columns:
Vol[facId] = CalculateEWMAVol(R[facId], Lambda)
return (Vol)
该函数工作正常,但对于较大的时间序列,由于for循环,该过程会变慢。
是否有其他方法可以通过该系列调用此函数?
https://stackoverflow.com/questions/42305587
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