EGARCH(1,1) 使用 `arch` Package 預測波動率

Question

目的

我想通過EGARCH(1,1) model 使用arch package預測每日波動率。
預測間隔： 01-04-2015至12-06-2018 （mm-dd-yyyy 格式）

因此我應該從2013到2015獲取數據（例如）以適合 EGARCH(1,1) model ，然后預測01-04-2015到12-06-2018的每日波動率

---

代碼

所以我試着這樣寫：

# Packages That we need
from pandas_datareader import data as web
from arch import arch_model
import pandas as pd
#---------------------------------------

# grab Microsoft daily adjusted close price data from '01-03-2013' to '12-06-2018' and store it in DataFrame
df = pd.DataFrame(web.get_data_yahoo('MSFT' , start='01-03-2013' , end='12-06-2018')['Adj Close'])

#---------------------------------------

# calculate daily rate of return that is necessary for predicting daily Volatility by EGARCH
daily_rate_of_return_EGARCH = np.log(df.loc[ : '01-04-2015']/df.loc[ : '01-04-2015'].shift())
# drop NaN values
daily_rate_of_return_EGARCH = daily_rate_of_return_EGARCH.dropna()

#---------------------------------------

# Volatility Forecasting By EGARCH(1,1)
model_EGARCH = arch_model(daily_rate_of_return_EGARCH, vol='EGARCH' , p = 1 , o = 0 , q = 1)
fitted_EGARCH = model_EGARCH.fit(disp='off')

#---------------------------------------

# and finally, Forecasting step
# Note that as mentioned in `purpose` section, predict interval should be from '01-04-2015' to end of the data frame
horizon = len(df.loc['01-04-2015' : ])
volatility_FORECASTED = fitted_EGARCH.forecast(horizon = horizon , method='simulation')

錯誤

然后我得到了這個錯誤：

MemoryError                               Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_12900/1021856026.py in <module>
      1 horizon = len(df.loc['01-04-2015':])
----> 2 volatility_FORECASTED = fitted_EGARCH.forecast(horizon = horizon , method='simulation') 

MemoryError: Unable to allocate 3.71 GiB for an array with shape (503, 1000, 989) and data type float64

似乎 arch 將節省大量數據。

預期結果

我所期望的是一個簡單的pandas.Series ，其中包含從'01-04-2015'到'12-06-2018'的每日波動率預測。 正是我的意思是這樣的：
（注：日期格式 --> mm-dd-yyyy）

    (DATE)     (VOLATILITY)
'01-04-2015'      .....
'01-05-2015'      .....
'01-06-2015'      .....
.                   .
.                   .
.                   .
'12-06-2018'      .....

我怎樣才能做到這一點？

Answer 1

您只需要傳遞reindex=False關鍵字，memory 要求就會急劇下降。 您需要最新版本的 Arch package 才能使用此功能，該功能將預測的 output 形狀更改為僅包含預測值，因此 alignment 與歷史行為不同。

# Packages That we need
from pandas_datareader import data as web
from arch import arch_model
import pandas as pd
#---------------------------------------

# grab Microsoft daily adjusted close price data from '01-03-2013' to '12-06-2018' and store it in DataFrame
df = pd.DataFrame(web.get_data_yahoo('MSFT' , start='01-03-2013' , end='12-06-2018')['Adj Close'])

#---------------------------------------

# calculate daily rate of return that is necessary for predicting daily Volatility by EGARCH
daily_rate_of_return_EGARCH = np.log(df.loc[ : '01-04-2015']/df.loc[ : '01-04-2015'].shift())
# drop NaN values
daily_rate_of_return_EGARCH = daily_rate_of_return_EGARCH.dropna()

#---------------------------------------

# Volatility Forecasting By EGARCH(1,1)
model_EGARCH = arch_model(daily_rate_of_return_EGARCH, vol='EGARCH' , p = 1 , o = 0 , q = 1)
fitted_EGARCH = model_EGARCH.fit(disp='off')

#---------------------------------------

# and finally, Forecasting step
# Note that as mentioned in `purpose` section, predict interval should be from '01-04-2015' to end of the data frame
horizon = len(df.loc['01-04-2015' : ])
volatility_FORECASTED = fitted_EGARCH.forecast(horizon = horizon , method='simulation', reindex=False)