siruiji / ml4frm Goto Github PK

Thank you for your work. It would be nice if you could give the relevant data in the code

Hello,
Thanks for putting the code to the excellent book up in Github. I notice a very small error in the code - I don't know if it is local of whether the error is common to all of the AR model codes but I'm considering the file: Volatility_prediction_GARCH.py:

Do let me know if you disagree with my reasoning regarding the square root!

1. the plots should read:
   plt.plot(np.sqrt(forecast.variance.iloc[-len(split_date):]), label='Volatility Prediction-GARCH')
   where one should take the square root of the arch_model forecat since this model actually forecasts variance, not volatility in order to plot it against the realised volatility.

My revised code:
def load_raw_data(ticker, start_date, end_date):
price = yf.download(ticker, start_date, end_date)['Adj Close']
ret = price.pct_change()[1:] # take the scaling out here
ret.dropna(inplace=True)
return ret

def model_train(ret):
global garch, q, best_param
bic_garch = []
for p in range(1, 5):
for q in range(1, 5):
garch = arch_model(ret, mean='zero', vol='GARCH', p=p, o=0, q=q, rescale=True).fit(disp='off') # add scaling here by adding rescale=True
bic_garch.append(garch.bic)
if garch.bic == np.min(bic_garch):
best_param = p, q
garch = arch_model(ret, mean='zero', vol='GARCH', p=best_param[0], o=0, q=best_param[1], rescale=True).fit(disp='off')
scale = garch.scale # take scale value here
print(garch.summary())

realized_vol = ret.rolling(5).std()
n = 252
split_date = ret.iloc[-n:].index
forecast = garch.forecast(start=split_date[0])
plt.figure(figsize=(10, 6))
plt.plot(realized_vol, label='Realized Volatility')
plt.plot(np.sqrt(forecast.variance.iloc[-len(split_date):] / np.power(scale, 2)), label='Volatility Prediction-GARCH') # divide by scaling squared hereand take the square root
plt.title('Volatility Prediction with GARCH')
plt.legend()
plt.savefig('corrected_plot.png')
plt.show()