I’m using the SVR-GARCH model to predict conditional volatility, as described in the book Machine Learning for Financial Risk Management with Python: Algorithms for Modeling Risk by Abdullah Karasan.

I’ve encountered an issue where my code sometimes produces the same repeated value for the conditional volatility across the entire forecasting horizon. I understand that the initial parameter values are randomized, but I am confused about why, in most cases, the prediction results in a constant value throughout the forecast period.

import yfinance as yf
from datetime import datetime, timedelta
import pandas as pd
import numpy as np
from sklearn.svm import SVR
from sklearn.model_selection import RandomizedSearchCV
from scipy.stats import uniform as sp_rand
from sklearn.preprocessing import StandardScaler

# Select assets
stock_name = ['AAPL']
end_date = datetime.today()
start_date = end_date - timedelta(days = 365 * 25)

# Download the prices
prices = yf.download(
    stock_name,
    start = start_date,
    end = end_date,
    interval = '1d',
)['Adj Close']
prices = prices.dropna()
stock_name = ['Apple']
prices = prices.rename(stock_name[0], inplace = True)

# Log returns
returns = np.log(np.array(prices)[1:] / np.array(prices)[:-1])

# Forecasting horizon
H = 146

returns_series = pd.Series(returns)
realized_vol = returns_series.rolling(5).std()
realized_vol = pd.DataFrame(realized_vol)
realized_vol.reset_index(drop=True, inplace=True)

returns_svm = pd.DataFrame(returns ** 2)

X = pd.concat([realized_vol, returns_svm], axis=1, ignore_index=True)
X = X[4:].copy()
X = X.reset_index()
X.drop('index', axis=1, inplace=True)

realized_vol = realized_vol.dropna().reset_index()
realized_vol.drop('index', axis=1, inplace=True)

conditional_volatility = pd.DataFrame(index=prices.index[-H:], columns=['SVM Linear','SVM RBF','SVM Poly'])

para_grid = {'gamma': sp_rand(0.1, 1), 'C': sp_rand(0.1, 10), 'epsilon': sp_rand(0.1, 1)}

svr_lin = SVR(kernel='linear')
clf = RandomizedSearchCV(svr_lin, para_grid)
clf.fit(X[:-H], realized_vol.iloc[1:-(H-1)].values.reshape(-1,))
predict_svr_lin = clf.predict(X[-H:])
conditional_volatility['SVM Linear'] = predict_svr_lin

svr_rbf = SVR(kernel='rbf')
clf = RandomizedSearchCV(svr_rbf, para_grid)
clf.fit(X[:-H], realized_vol.iloc[1:-(H-1)].values.reshape(-1,))
predict_svr_rbf = clf.predict(X[-H:])
conditional_volatility['SVM RBF'] = predict_svr_rbf

svr_poly = SVR(kernel='poly')
clf = RandomizedSearchCV(svr_poly, para_grid)
clf.fit(X[:-H], realized_vol.iloc[1:-(H-1)].values.reshape(-1,))
predict_svr_poly = clf.predict(X[-H:])
conditional_volatility['SVM Poly'] = predict_svr_poly

print(conditional_volatility)

Could anyone help me understand why this might be happening and how to address it?