updated values to match with custom covariance function

tests/test_stats.py

@@ -169,7 +169,7 @@ class TestBeta:
         sts = pft.TimeSeries(stock_data, "D")
         mts = pft.TimeSeries(market_data, "D")
         beta = pft.beta(sts, mts, frequency="D", return_period_unit="days", return_period_value=1)
-        assert round(beta, 4) == 1.6001
+        assert round(beta, 4) == 1.5997
 
     def test_beta_daily_freq_daily_returns(self, create_test_data):
         market_data = create_test_data(num=3600, frequency=pft.AllFrequencies.D)
@@ -177,7 +177,7 @@ class TestBeta:
         sts = pft.TimeSeries(stock_data, "D")
         mts = pft.TimeSeries(market_data, "D")
         beta = pft.beta(sts, mts)
-        assert round(beta, 4) == 1.6292
+        assert round(beta, 4) == 1.6287
 
     def test_beta_monthly_freq(self, create_test_data):
         market_data = create_test_data(num=3600, frequency=pft.AllFrequencies.D)
@@ -185,7 +185,7 @@ class TestBeta:
         sts = pft.TimeSeries(stock_data, "D")
         mts = pft.TimeSeries(market_data, "D")
         beta = pft.beta(sts, mts, frequency="M")
-        assert round(beta, 4) == 1.629
+        assert round(beta, 4) == 1.6137
 
     def test_beta_monthly_freq_monthly_returns(self, create_test_data):
         market_data = create_test_data(num=3600, frequency=pft.AllFrequencies.D)
@@ -193,4 +193,4 @@ class TestBeta:
         sts = pft.TimeSeries(stock_data, "D")
         mts = pft.TimeSeries(market_data, "D")
         beta = pft.beta(sts, mts, frequency="M", return_period_unit="months", return_period_value=1)
-        assert round(beta, 4) == 1.6023
+        assert round(beta, 4) == 1.5887