Class CointegrationTest

java.lang.Object

com.quantfinlib.hedging.CointegrationTest

public final class CointegrationTest
extends Object

Engle-Granger two-step cointegration test: regress one price series on the other, then run an augmented Dickey-Fuller test (no constant) on the residual spread. This is the statistical justification PairsHedger needs before a pairs trade — a high return correlation is not enough; the spread must be stationary.

Critical values are the Engle-Granger two-variable, no-trend asymptotics: -3.90 (1%), -3.34 (5%), -3.04 (10%). More negative ADF t-statistics mean stronger evidence of cointegration.

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static final record	CointegrationTest.EngleGrangerResult

Field Summary

Fields

Modifier and Type	Field	Description
static final double	CRITICAL_10PCT
static final double	CRITICAL_1PCT
static final double	CRITICAL_5PCT

Method Summary

Modifier and Type	Method	Description
static double	adfTStatistic(double[] series)	Dickey-Fuller t-statistic (no constant, no lags — appropriate for mean-zero regression residuals): regress Δe_t = γ e_{t-1} + u_t and return t = γ̂ / se(γ̂).
static CointegrationTest.EngleGrangerResult	engleGranger(double[] pricesA, double[] pricesB)	Full Engle-Granger test of prices A against prices B.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Details

CRITICAL_1PCT

public static final double CRITICAL_1PCT

See Also:

• Constant Field Values

CRITICAL_5PCT

public static final double CRITICAL_5PCT

See Also:

• Constant Field Values

CRITICAL_10PCT

public static final double CRITICAL_10PCT

See Also:

• Constant Field Values

Method Details

engleGranger

public static CointegrationTest.EngleGrangerResult engleGranger(double[] pricesA,
 double[] pricesB)

Full Engle-Granger test of prices A against prices B.

adfTStatistic

public static double adfTStatistic(double[] series)

Dickey-Fuller t-statistic (no constant, no lags — appropriate for mean-zero regression residuals): regress Δe_t = γ e_{t-1} + u_t and return t = γ̂ / se(γ̂).