GARCHModel

Value Members

1. final def !=(arg0: AnyRef): Boolean

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2. final def !=(arg0: Any): Boolean

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3. final def ##(): Int

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4. final def ==(arg0: AnyRef): Boolean

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5. final def ==(arg0: Any): Boolean

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6. def addTimeDependentEffects(ts: Vector, dest: Vector): Vector

   Takes a series of i.

   Takes a series of i.i.d. observations and returns a time series based on it with the time-dependent effects of this model added.

   ts

   Time series of i.i.d. observations.

   dest

   Array to put the filtered series, can be the same as ts.

   returns

   The dest series, for convenience.

   Definition Classes

   GARCHModel → TimeSeriesModel

7. val alpha: Double

8. final def asInstanceOf[T0]: T0

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9. val beta: Double

10. def clone(): AnyRef

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11. final def eq(arg0: AnyRef): Boolean

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12. def equals(arg0: Any): Boolean

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13. def finalize(): Unit

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14. final def getClass(): Class[_]

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15. def hashCode(): Int

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16. final def isInstanceOf[T0]: Boolean

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17. def logLikelihood(ts: Vector): Double

    Returns the log likelihood of the parameters on the given time series.

    Returns the log likelihood of the parameters on the given time series.

    Based on http://www.unc.edu/~jbhill/Bollerslev_GARCH_1986.pdf

18. final def ne(arg0: AnyRef): Boolean

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19. final def notify(): Unit

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20. final def notifyAll(): Unit

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21. val omega: Double

22. def removeTimeDependentEffects(ts: Vector, dest: Vector): Vector

    Takes a time series that is assumed to have this model's characteristics and returns a time series with time-dependent effects of this model removed.

    Takes a time series that is assumed to have this model's characteristics and returns a time series with time-dependent effects of this model removed.

    This is the inverse of TimeSeriesModel#addTimeDependentEffects.

    ts

    Time series of observations with this model's characteristics.

    dest

    Array to put the filtered series, can be the same as ts.

    returns

    The dest series, for convenience.

    Definition Classes

    GARCHModel → TimeSeriesModel

23. def sample(n: Int, rand: RandomGenerator): Array[Double]

    Samples a random time series of a given length with the properties of the model.

    Samples a random time series of a given length with the properties of the model.

    n

    The length of the time series to sample.

    rand

    The random generator used to generate the observations.

    returns

    The samples time series.

24. final def synchronized[T0](arg0: ⇒ T0): T0

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25. def toString(): String

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26. final def wait(): Unit

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27. final def wait(arg0: Long, arg1: Int): Unit

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28. final def wait(arg0: Long): Unit

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