tech.nmfin.portfoliooptimization.lai2010.fit

Class SimpleAR1Fit

java.lang.Object

dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel

dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAModel

dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel

dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARModel

tech.nmfin.portfoliooptimization.lai2010.fit.SimpleAR1Fit

All Implemented Interfaces:

ResamplerModel

public class SimpleAR1Fit
extends VARModel
implements ResamplerModel

This class does a quick AR(1) fitting to the time series, essentially treating the returns as independent. A better way to fit the data is to use VARFit.

Constructor Summary

Constructors

Constructor and Description
SimpleAR1Fit(Matrix mts)

Method Summary

Modifier and Type	Method and Description
Matrix	fittedValues() Gets the fitted observations from the model.
Matrix	sigma2() Gets the conditional variances of residuals over time.
Matrix	standarizedInnovations() Gets the standarized innovations (normalized by the conditional standard deviation at the time) of the time series.

Methods inherited from class dev.nm.stat.timeseries.linear.multivariate.stationaryprocess.arma.VARMAModel

conditionalMean, getDemeanedModel, unconditionalMean

Methods inherited from class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAModel

getVARMA

Methods inherited from class dev.nm.stat.timeseries.linear.multivariate.arima.VARIMAXModel

AR, d, dimension, getVARMAX, MA, maxPQ, mu, p, phi, psi, q, sigma, theta

Methods inherited from class java.lang.Object

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

Methods inherited from interface tech.nmfin.portfoliooptimization.lai2010.fit.ResamplerModel

fittedValues

Constructor Detail

SimpleAR1Fit

public SimpleAR1Fit(Matrix mts)

Method Detail

fittedValues

public Matrix fittedValues()

Description copied from interface: ResamplerModel

Gets the fitted observations from the model.

Specified by:

fittedValues in interface ResamplerModel

Returns:

the matrix of fitted observations

standarizedInnovations

public Matrix standarizedInnovations()

Description copied from interface: ResamplerModel

Gets the standarized innovations (normalized by the conditional standard deviation at the time) of the time series. Note (haksunli): we should not use instead the non-standarized innovations (have non-unit variances) because when they are scaled by the time dependent standard deviations/variances. We cannot simply shuffle them to get a new time series of innovations.

Specified by:

standarizedInnovations in interface ResamplerModel

Returns:

the matrix of standardized innovations

sigma2

public Matrix sigma2()

Description copied from interface: ResamplerModel

Gets the conditional variances of residuals over time.

Specified by:

sigma2 in interface ResamplerModel

Returns:

the matrix of conditional residual variances