finmlkit.feature.core.structural_break.cusum module

Implementation of Chu-Stinchcombe-White CUSUM Test on Levels based on Homm and Breitung (2011).

finmlkit.feature.core.structural_break.cusum._comp_max_s_nt(y: ndarray[tuple[int, ...], dtype[_ScalarType_co]], t: int, sigma_sq_t: float) → Tuple[float, float, float, float][source]

Compute the maximum S_n values and critical values for upward and downward movements.

Parameters:

  • y – Array of log price series.

  • t – Current time index.

  • sigma_sq_t – Estimated variance at time t.

Returns:

A tuple containing: - max_s_n_value_up: Maximum test statistic for upward movements. - max_s_n_value_down: Maximum test statistic for downward movements. - max_s_n_critical_value_up: Critical value for the upward test statistic. - max_s_n_critical_value_down: Critical value for the downward test statistic.

finmlkit.feature.core.structural_break.cusum.cusum_test_developing(y: ndarray[tuple[int, ...], dtype[_ScalarType_co]], warmup_period: int = 30) → Tuple[ndarray[tuple[int, ...], dtype[float64]], ndarray[tuple[int, ...], dtype[float64]], ndarray[tuple[int, ...], dtype[float64]], ndarray[tuple[int, ...], dtype[float64]]][source]

Perform the Chu-Stinchcombe-White CUSUM Test on Levels.

This implementation follows Homm and Breitung (2011), but the one-sided tests are used to detect upward or downward structural breaks, providing directionality.

Parameters:

  • y – Array of price series (log prices).

  • warmup_period – Number of initial observations kept for std warmup, by default 30.

Returns:

A tuple containing: - s_n_t_values_up: Test statistic values for upward movements (one-sided test). - s_n_t_values_down: Test statistic values for downward movements (one-sided test). - critical_values_up: Critical values for the upward test statistics. - critical_values_down: Critical values for the downward test statistics.

Note

The test detects deviations from the random walk hypothesis in the time series. A significant test statistic indicates a structural break in the time series.

finmlkit.feature.core.structural_break.cusum.cusum_test_last(y: ndarray[tuple[int, ...], dtype[_ScalarType_co]]) → Tuple[float, float, float, float][source]

Perform the Chu-Stinchcombe-White CUSUM Test on Levels for the last observation.

This implementation follows Homm and Breitung (2011), but the one-sided tests are used to detect upward or downward structural breaks, providing directionality.

Parameters:

y – Array of price series (log prices).

Returns:

A tuple containing: - max_s_n_value_up: Test statistic value for upward movement (one-sided test) at the last observation. - max_s_n_value_down: Test statistic value for downward movement (one-sided test) at the last observation. - max_s_n_critical_value_up: Critical value for the upward test statistic at the last observation. - max_s_n_critical_value_down: Critical value for the downward test statistic at the last observation.

Note

A significant test statistic at the last observation indicates a recent structural break in the time series.

finmlkit.feature.core.structural_break.cusum.cusum_test_rolling(close_prices: ndarray[tuple[int, ...], dtype[_ScalarType_co]], window_size: int = 1000, warmup_period: int = 30) → Tuple[ndarray[tuple[int, ...], dtype[float64]], ndarray[tuple[int, ...], dtype[float64]], ndarray[tuple[int, ...], dtype[float64]], ndarray[tuple[int, ...], dtype[float64]]][source]

Perform the Chu-Stinchcombe-White CUSUM Test on Levels over a rolling window.

This implementation follows Homm and Breitung (2011), where the one-sided tests are used to detect upward or downward structural breaks, providing directionality.

Parameters:

  • close_prices – Array of close price series.

  • window_size – Size of the rolling window, by default 1000.

  • warmup_period – Minimum number of observations before the first statistic is calculated, by default 30.

Returns:

A tuple containing: - snt_up: Test statistic values for upward movements (one-sided test) over the rolling window. - snt_down: Test statistic values for downward movements (one-sided test) over the rolling window. - critical_values_up: Critical values for the upward test statistics over the rolling window. - critical_values_down: Critical values for the downward test statistics over the rolling window.

Note

This test detects deviations from the random walk hypothesis in the time series over a moving window. It helps identify periods of structural breaks in the time series.