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NumXL - List of Functions

Statistical Tests Functions

  • NormailityTest: a test to examine the normal distribution assumption of the data.
  • TEST_MEAN: p-value of testing mean against given value.
  • TEST_STDEVi:p-value of testing the standard deviation against given value.
  • TEST_SKEW: p-value of testing the presence of skewness
  • TEST_XKURT: p-value of testing the presence of excess kurtosis.

Utilities Functions

  • INTERPOLATE: a new data points within a range of discrete data set of known values. 
  • GEDi_XKURT: the excess-kurtosis for generalized error distribution (power exponential) given its shape factor (i.e. degrees of freedom).
  • TDIST_XKURT: the excess-kurtosis for Student t-distrubtion given number of degrees of freedom.
  • RMNA: remove all missing values (#NA) in a data set while preserving the original order of the observations.

Linear Time Series Functions

  • LAG: the lagging operator.
  • DIFF: the difference operator
  • WMAi: the weighted moving average operator.
  • EWMAi: the exponential weighted moving average operator (volatility)
  • XCF: the cross-correlation function (XCF)
  • ACFi: the auto-correlation function (ACF)
  • ACFCI: The upper/lower limits of confidence interval for estimated autocorrelation function (ACF).
  • ACFTest: the P-Value of a test hypothesis that ACF equal a given value (e.g. 0)
  • WNTest: P-Value of a white-noise test (data is serially correlated)
  • PACFi: the partial auto-correlation function
  • PACFCI: The upper/lower limits of the confidence interval for partial autocorrelation function (PACF).
  • ARMAi_AICi: the Akaike's information criterion (AIC) of an ARMA model.
  • ARMA_LLFi: the log-likelihood of an ARMA model.
  • ARMA_RESID: the standardized residuals of the ARMA model
  • ARMA_FORE: the forecasted conditional mean at T+step
  • ARMA_FORESD: the forecasted conditional volatility at T+step
  • ARMA_FORECI: the confidence interval limits of the forecasted value at T+step.
  

GARCHi Analysis Functions

  • ARCHTest: the ARCH effect test
  • GARCH_AIC: the Akaike's information criterion (AIC) of the GARCH.
  • GARCH_LLF: the log-likeyhood (goodness of fit) of the GARCH model.
  • GARCH_RESID: the standardized residuals of the GARCH model.
  • GARCH_FORE: the forecasted conditional mean of the GARCH model at T+step.
  • GARCH_FORESD: the forecasted conditional volatility of the GARCH model at T+step.
  • GARCH_FORECI: the upper/lower confidence limits of the forecasted return at T+step.
  • GARCH_MEAN: the array of fitted conditional mean.
  • GARCH_VOL: the array of fitted conditional volatility.
  • EGARCHi_AIC: the Akaike's information criterion (AIC) of the EGARCH.
  • EGARCH_LLF: the log-likeyhood (goodness of fit) of the GARCH model.
  • EGARCH_RESID: the standardized residuals of the EGARCH model.
  • EGARCH_FORE: the forecasted conditional mean of the EGARCH model at T+step.
  • EGARCH_FORESD: the forecasted conditional volatility of the EGARCH model at T+step.
  • EGARCH_FORECI: the upper/lower
  • confidence limits of the forecasted return at T+step.
  • EGARCH_MEAN: the array of fitted conditional mean.
  • EGARCH_VOL: the array of fitted conditional volatility.
  • GARCHM_AIC: the Akaike's information criterion (AIC) of the GARCH-Mi.
  • GARCHM_LLF: the log-likeyhood (goodness of fit) of the GARCH-M model.
  • GARCHM_RESID: the standardized residuals of the GARCH-M model.
  • GARCHM_FORE: the forecasted conditional mean of the GARCH-M model at T+step.
  • GARCHM_FORESD: the forecasted conditional volatility of the GARCH-M model at T+step.
  • GARCHM_FORECI: the upper/lower confidence limits of the forecasted return at T+step.
  • GARCHM_MEAN: the array of fitted conditional mean.
  • GARCHM_VOL: the array of fitted conditional volatility.
randomness