Package 'RPESE'

Standard Error Estimate for Downside Sharpe Ratio (DSR) of Returns

Description

DSR.SE computes the standard error of the downside Sharpe ratio of the returns.

Usage

DSR.SE(
  data,
  rf = 0,
  se.method = c("IFiid", "IFcor", "IFcorAdapt", "IFcorPW", "BOOTiid", "BOOTcor")[c(1, 4)],
  cleanOutliers = FALSE,
  fitting.method = c("Exponential", "Gamma")[1],
  d.GLM.EN = 5,
  freq.include = c("All", "Decimate", "Truncate")[1],
  freq.par = 0.5,
  corOut = c("none", "retCor", "retIFCor", "retIFCorPW")[1],
  return.coef = FALSE,
  ...
)

Arguments

data	Data of returns for one or multiple assets or portfolios.
rf	Risk free rate.
se.method	A character string indicating which method should be used to compute the standard error of the estimated standard deviation. One or a combination of: "IFiid" (default), "IFcor" (default), "IFcorPW", "IFcorAdapt", "BOOTiid" or "BOOTcor".
cleanOutliers	Boolean variable to indicate whether the pre-whitenning of the influence functions TS should be done through a robust filter. Default if FALSE.
fitting.method	Distribution used in the standard errors computation. Should be one of "Exponential" (default) or "Gamma".
d.GLM.EN	Order of the polynomial for the Exponential or Gamma fitting. Default polynomial order of 5.
freq.include	Frequency domain inclusion criteria. Must be one of "All" (default), "Decimate" or "Truncate."
freq.par	Percentage of the frequency used if "freq.include" is "Decimate" or "Truncate." Default is 0.5.
corOut	Return correlation of the returns or the influence function transformed returns. Must be one of "retCor", "retIFCor" or "none" (default).
return.coef	Boolean variable to indicate whether the coefficients of the penalized GLM fit are returned. Default if FALSE.
...	Additional parameters.

Value

A vector or a list depending on se.method.

Author(s)

Anthony-Alexander Christidis, [email protected]

Examples

# Loading data
data(edhec, package = "PerformanceAnalytics")
# Changing the data colnames
names(edhec)  =  c("CA", "CTA", "DIS", "EM", "EMN",
                   "ED", "FIA", "GM", "LS", "MA",
                   "RV", "SS", "FOF")
# Computing the standard errors for
# the two influence functions based approaches
DSR.SE(edhec, se.method = c("IFiid","IFcor"),
       cleanOutliers = FALSE,
       fitting.method = c("Exponential", "Gamma")[1])

---

Standard Error Estimate for Expected Shortfall (ES) of Returns

Description

ES.SE computes the standard error of the expected shortfall of the returns.

Usage

ES.SE(
  data,
  p = 0.95,
  se.method = c("IFiid", "IFcor", "IFcorAdapt", "IFcorPW", "BOOTiid", "BOOTcor")[1:2],
  cleanOutliers = FALSE,
  fitting.method = c("Exponential", "Gamma")[1],
  d.GLM.EN = 5,
  freq.include = c("All", "Decimate", "Truncate")[1],
  freq.par = 0.5,
  corOut = c("none", "retCor", "retIFCor", "retIFCorPW")[1],
  return.coef = FALSE,
  ...
)

Arguments

data	Data of returns for one or multiple assets or portfolios.
p	Confidence level for calculation. Default value is p = 0.95.
se.method	A character string indicating which method should be used to compute the standard error of the estimated standard deviation. One or a combination of: "IFiid" (default), "IFcor" (default), "IFcorPW", "IFcorAdapt", "BOOTiid" or "BOOTcor".
cleanOutliers	Boolean variable to indicate whether the pre-whitenning of the influence functions TS should be done through a robust filter. Default if FALSE.
fitting.method	Distribution used in the standard errors computation. Should be one of "Exponential" (default) or "Gamma".
d.GLM.EN	Order of the polynomial for the Exponential or Gamma fitting. Default polynomial order of 5.
freq.include	Frequency domain inclusion criteria. Must be one of "All" (default), "Decimate" or "Truncate."
freq.par	Percentage of the frequency used if "freq.include" is "Decimate" or "Truncate." Default is 0.5.
corOut	Return correlation of the returns or the influence function transformed returns. Must be one of "retCor", "retIFCor" or "none" (default).
return.coef	Boolean variable to indicate whether the coefficients of the penalized GLM fit are returned. Default if FALSE.
...	Additional parameters.

Value

A vector or a list depending on se.method.

Author(s)

Xin Chen, [email protected]

Anthony-Alexander Christidis, [email protected]

Examples

# Loading data
data(edhec, package = "PerformanceAnalytics")
# Changing the data colnames
names(edhec)  =  c("CA", "CTA", "DIS", "EM", "EMN",
                 "ED", "FIA", "GM", "LS", "MA",
                 "RV", "SS", "FOF")
# Computing the standard errors for
# the two influence functions based approaches
ES.SE(edhec, se.method = c("IFiid","IFcor"),
      cleanOutliers = FALSE,
      fitting.method = c("Exponential", "Gamma")[1])

---

Standard Error Estimate for Expected Shortfall Ratio (ESratio) of Returns

Description

ESratio.SE computes the standard error of the expected shortfall ratio of the returns.

Usage

ESratio.SE(
  data,
  alpha = 0.1,
  rf = 0,
  se.method = c("IFiid", "IFcor", "IFcorAdapt", "IFcorPW", "BOOTiid", "BOOTcor")[c(1, 4)],
  cleanOutliers = FALSE,
  fitting.method = c("Exponential", "Gamma")[1],
  d.GLM.EN = 5,
  freq.include = c("All", "Decimate", "Truncate")[1],
  freq.par = 0.5,
  corOut = c("none", "retCor", "retIFCor", "retIFCorPW")[1],
  return.coef = FALSE,
  ...
)

Arguments

data	Data of returns for one or multiple assets or portfolios.
alpha	Lower tail probability.
rf	Risk-free interest rate.
se.method	A character string indicating which method should be used to compute the standard error of the estimated standard deviation. One or a combination of: "IFiid" (default), "IFcor", "IFcorPW", "IFcorAdapt" (default), "BOOTiid" or "BOOTcor".
cleanOutliers	Boolean variable to indicate whether the pre-whitenning of the influence functions TS should be done through a robust filter. Default if FALSE.
fitting.method	Distribution used in the standard errors computation. Should be one of "Exponential" (default) or "Gamma".
d.GLM.EN	Order of the polynomial for the Exponential or Gamma fitting. Default polynomial order of 5.
freq.include	Frequency domain inclusion criteria. Must be one of "All" (default), "Decimate" or "Truncate."
freq.par	Percentage of the frequency used if "freq.include" is "Decimate" or "Truncate." Default is 0.5.
corOut	Return correlation of the returns or the influence function transformed returns. Must be one of "retCor", "retIFCor" or "none" (default).
return.coef	Boolean variable to indicate whether the coefficients of the penalized GLM fit are returned. Default if FALSE.
...	Additional parameters.

Value

A vector or a list depending on se.method.

Author(s)

Anthony-Alexander Christidis, [email protected]

Examples

# Loading data
data(edhec, package = "PerformanceAnalytics")
# Changing the data colnames
names(edhec) = c("CA", "CTA", "DIS", "EM", "EMN",
                 "ED", "FIA", "GM", "LS", "MA",
                 "RV", "SS", "FOF")
# Computing the standard errors for
# the two influence functions based approaches
ESratio.SE(edhec, se.method=c("IFiid","IFcorAdapt"),
           cleanOutliers=FALSE,
           fitting.method=c("Exponential", "Gamma")[1])

---

Wrapper Function for Standard Errors Estimates Functions

Description

EstimatorSE computes the standard error for specified risk and performance measures.

Usage

EstimatorSE(
  data,
  estimator.fun = c("DSR", "ES", "ESratio", "LPM", "Mean", "OmegaRatio", "RachevRatio",
    "robMean", "SD", "SemiSD", "SR", "SoR", "VaR", "VaRratio")[1],
  se.method = c("IFiid", "IFcor", "IFcorAdapt", "IFcorPW", "BOOTiid", "BOOTcor")[1],
  cleanOutliers = FALSE,
  fitting.method = c("Exponential", "Gamma")[1],
  d.GLM.EN = 5,
  freq.include = c("All", "Decimate", "Truncate")[1],
  freq.par = 0.5,
  a = 0.3,
  b = 0.7,
  return.coef = FALSE,
  ...
)

Arguments

data	Data of returns for one or multiple assets or portfolios.
estimator.fun	Risk or performance measure to compute estimates of standard errors.
se.method	A character string indicating which method should be used to compute the standard error of the estimated standard deviation. One of: "IFiid", "IFcor", "IFcorAdapt", "IFcorPW", "BOOTiid", "BOOTcor", or "none".
cleanOutliers	Boolean variable to indicate whether the pre-whitenning of the influence functions TS should be done through a robust filter. Default if FALSE.
fitting.method	Distribution used in the standard errors computation. Should be one of "Exponential" (default) or "Gamma".
d.GLM.EN	Order of the polynomial for the Exponential or Gamma fitting. Default polynomial order of 5.
freq.include	Frequency domain inclusion criteria. Must be one of "All" (default), "Decimate" or "Truncate."
freq.par	Percentage of the frequency used if "freq.include" is "Decimate" or "Truncate." Default is 0.5.
a	First adaptive method parameter.
b	Second adaptive method parameter.
return.coef	Boolean variable to indicate whether the coefficients of the Exponential or Gamma fit are returned. Default is FALSE.
...	Additional parameters.

Value

A vector standard error estimates.

Author(s)

Xin Chen, [email protected]

Anthony-Alexander Christidis, [email protected]

Examples

# Loading data
data(edhec, package = "PerformanceAnalytics")
# Changing the data colnames
names(edhec) = c("CA", "CTA", "DIS", "EM", "EMN",
                 "ED", "FIA", "GM", "LS", "MA",
                 "RV", "SS", "FOF")
# Computing the standard errors for
# the three influence functions based approaches
EstimatorSE(edhec[,"CA"], se.method = c("IFcor"),
            cleanOutliers = FALSE,
            fitting.method = c("Exponential", "Gamma")[1])

---

Standard Error Estimate for Lower Partial Moment (LPM) of Returns

Description

LPM.SE computes the standard error of the LPM of the returns.

Usage

LPM.SE(
  data,
  const = 0,
  order = 1,
  se.method = c("IFiid", "IFcor", "IFcorAdapt", "IFcorPW", "BOOTiid", "BOOTcor")[1:2],
  cleanOutliers = FALSE,
  fitting.method = c("Exponential", "Gamma")[1],
  d.GLM.EN = 5,
  freq.include = c("All", "Decimate", "Truncate")[1],
  freq.par = 0.5,
  corOut = c("none", "retCor", "retIFCor", "retIFCorPW")[1],
  return.coef = FALSE,
  ...
)

Arguments

data	Data of returns for one or multiple assets or portfolios.
const	Constant threshold.
order	Order for the lower partial moment (should be 1 or 2).
se.method	A character string indicating which method should be used to compute the standard error of the estimated standard deviation. One or a combination of: "IFiid" (default), "IFcor" (default), "IFcorPW", "IFcorAdapt", "BOOTiid" or "BOOTcor".
cleanOutliers	Boolean variable to indicate whether the pre-whitenning of the influence functions TS should be done through a robust filter. Default if FALSE.
fitting.method	Distribution used in the standard errors computation. Should be one of "Exponential" (default) or "Gamma".
d.GLM.EN	Order of the polynomial for the Exponential or Gamma fitting. Default polynomial order of 5.
freq.include	Frequency domain inclusion criteria. Must be one of "All" (default), "Decimate" or "Truncate."
freq.par	Percentage of the frequency used if "freq.include" is "Decimate" or "Truncate." Default is 0.5.
corOut	Return correlation of the returns or the influence function transformed returns. Must be one of "retCor", "retIFCor" or "none" (default).
return.coef	Boolean variable to indicate whether the coefficients of the penalized GLM fit are returned. Default if FALSE.
...	Additional parameters.

Value

A vector or a list depending on se.method.

Author(s)

Xin Chen, [email protected]

Anthony-Alexander Christidis, [email protected]

Examples

# Loading data
data(edhec, package = "PerformanceAnalytics")
# Changing the data colnames
names(edhec) = c("CA", "CTA", "DIS", "EM", "EMN",
                 "ED", "FIA", "GM", "LS", "MA",
                 "RV", "SS", "FOF")
# Computing the standard errors for
# the two influence functions based approaches
LPM.SE(edhec, se.method = c("IFiid","IFcor"),
       cleanOutliers = FALSE,
       fitting.method = c("Exponential", "Gamma")[1])

---

Standard Error Estimate for Mean of Returns

Description

Mean.SE computes the standard error of the mean of the returns.

Usage

Mean.SE(
  data,
  se.method = c("IFiid", "IFcor", "IFcorAdapt", "IFcorPW", "BOOTiid", "BOOTcor")[c(1, 4)],
  cleanOutliers = FALSE,
  fitting.method = c("Exponential", "Gamma")[1],
  d.GLM.EN = 5,
  freq.include = c("All", "Decimate", "Truncate")[1],
  freq.par = 0.5,
  corOut = c("none", "retCor", "retIFCor", "retIFCorPW")[1],
  return.coef = FALSE,
  ...
)

Arguments

data	Data of returns for one or multiple assets or portfolios.
se.method	A character string indicating which method should be used to compute the standard error of the estimated standard deviation. One or a combination of: "IFiid" (default), "IFcor", "IFcorPW", "IFcorAdapt" (default), "BOOTiid" or "BOOTcor".
cleanOutliers	Boolean variable to indicate whether the pre-whitenning of the influence functions TS should be done through a robust filter. Default if FALSE.
fitting.method	Distribution used in the standard errors computation. Should be one of "Exponential" (default) or "Gamma".
d.GLM.EN	Order of the polynomial for the Exponential or Gamma fitting. Default polynomial order of 5.
freq.include	Frequency domain inclusion criteria. Must be one of "All" (default), "Decimate" or "Truncate."
freq.par	Percentage of the frequency used if "freq.include" is "Decimate" or "Truncate." Default is 0.5.
corOut	Return correlation of the returns or the influence function transformed returns. Must be one of "retCor", "retIFCor" or "none" (default).
return.coef	Boolean variable to indicate whether the coefficients of the penalized GLM fit are returned. Default if FALSE.
...	Additional parameters.

Value

A vector or a list depending on se.method

Author(s)

Anthony-Alexander Christidis, [email protected]

Examples

# Loading data
data(edhec, package = "PerformanceAnalytics")
# Changing the data colnames
names(edhec) = c("CA", "CTA", "DIS", "EM", "EMN",
                 "ED", "FIA", "GM", "LS", "MA",
                 "RV", "SS", "FOF")
# Computing the standard errors for
# the two influence functions based approaches
Mean.SE(edhec, se.method = c("IFiid","IFcorAdapt"),
        cleanOutliers = FALSE,
        fitting.method = c("Exponential", "Gamma")[1])

---

Standard Error Estimate for Omega Ratio of Returns

Description

OmegaRatio.SE computes the standard error of the Omega ratio of the returns.

Usage

OmegaRatio.SE(
  data,
  const = 0,
  se.method = c("IFiid", "IFcor", "IFcorAdapt", "IFcorPW", "BOOTiid", "BOOTcor")[c(1, 4)],
  cleanOutliers = FALSE,
  fitting.method = c("Exponential", "Gamma")[1],
  d.GLM.EN = 5,
  freq.include = c("All", "Decimate", "Truncate")[1],
  freq.par = 0.5,
  corOut = c("none", "retCor", "retIFCor", "retIFCorPW")[1],
  return.coef = FALSE,
  ...
)

Arguments

data	Data of returns for one or multiple assets or portfolios.
const	Constant threshold.
se.method	A character string indicating which method should be used to compute the standard error of the estimated standard deviation. One or a combination of: "IFiid" (default), "IFcor", "IFcorPW", "IFcorAdapt" (default), "BOOTiid", "BOOTcor".
cleanOutliers	Boolean variable to indicate whether the pre-whitenning of the influence functions TS should be done through a robust filter. Default if FALSE.
fitting.method	Distribution used in the standard errors computation. Should be one of "Exponential" (default) or "Gamma".
d.GLM.EN	Order of the polynomial for the Exponential or Gamma fitting. Default polynomial order of 5.
freq.include	Frequency domain inclusion criteria. Must be one of "All" (default), "Decimate" or "Truncate."
freq.par	Percentage of the frequency used if "freq.include" is "Decimate" or "Truncate." Default is 0.5.
corOut	Return correlation of the returns or the influence function transformed returns. Must be one of "retCor", "retIFCor" or "none" (default).
return.coef	Boolean variable to indicate whether the coefficients of the penalized GLM fit are returned. Default if FALSE.
...	Additional parameters.

Value

A vector or a list depending on se.method.

Author(s)

Anthony-Alexander Christidis, [email protected]

Examples

# Loading data
data(edhec, package = "PerformanceAnalytics")
# Changing the data colnames
names(edhec)  =  c("CA", "CTA", "DIS", "EM", "EMN",
                 "ED", "FIA", "GM", "LS", "MA",
                 "RV", "SS", "FOF")
# Computing the standard errors for
# the two influence functions based approaches
OmegaRatio.SE(edhec, se.method = c("IFiid","IFcorAdapt")[1],
              cleanOutliers = FALSE,
              fitting.method = c("Exponential", "Gamma")[1])

---

Formatted Output for Standard Errors Functions in RPESE

Description

printSE returns a formatted output from standard error functions from RPESE.

Usage

printSE(SE.data, round.digit = 3, round.out = TRUE)

Arguments

SE.data	Standard error estimates output from RPESE functions.
round.digit	Number of digits for rounding.
round.out	Round data (TRUE) with round.digit number of digits. Default is TRUE.

Value

A data frame with formatted output from standard error functions from RPESE.

Author(s)

Xin Chen, [email protected]

Anthony-Alexander Christidis, [email protected]

Examples

# Loading data
data(edhec, package = "PerformanceAnalytics")
# Changing the data colnames
names(edhec) = c("CA", "CTA", "DIS", "EM", "EMN",
                 "ED", "FIA", "GM", "LS", "MA",
                 "RV", "SS", "FOF")
# Computing the standard errors for
# the two influence functions based approaches
ES.out <- ES.SE(edhec, se.method = c("IFiid","IFcor"),
                cleanOutliers = FALSE,
                fitting.method = c("Exponential", "Gamma")[1])
# Print the output
printSE(ES.out)

---

Standard Error Estimate for Rachev Ratio of Returns

Description

RachevRatio.SE computes the standard error of the Rachev ratio of the returns.

Usage

RachevRatio.SE(
  data,
  alpha = 0.1,
  beta = 0.1,
  se.method = c("IFiid", "IFcor", "IFcorAdapt", "IFcorPW", "BOOTiid", "BOOTcor")[c(1, 4)],
  cleanOutliers = FALSE,
  fitting.method = c("Exponential", "Gamma")[1],
  d.GLM.EN = 5,
  freq.include = c("All", "Decimate", "Truncate")[1],
  freq.par = 0.5,
  corOut = c("none", "retCor", "retIFCor", "retIFCorPW")[1],
  return.coef = FALSE,
  ...
)

Arguments

data	Data of returns for one or multiple assets or portfolios.
alpha	Lower tail probability.
beta	Upper tail probability.
se.method	A character string indicating which method should be used to compute the standard error of the estimated standard deviation. One or a combination of: "IFiid" (default), "IFcor", "IFcorPW", "IFcorAdapt" (default), "BOOTiid" or "BOOTcor".
cleanOutliers	Boolean variable to indicate whether the pre-whitenning of the influence functions TS should be done through a robust filter. Default if FALSE.
fitting.method	Distribution used in the standard errors computation. Should be one of "Exponential" (default) or "Gamma".
d.GLM.EN	Order of the polynomial for the Exponential or Gamma fitting. Default polynomial order of 5.
freq.include	Frequency domain inclusion criteria. Must be one of "All" (default), "Decimate" or "Truncate."
freq.par	Percentage of the frequency used if "freq.include" is "Decimate" or "Truncate." Default is 0.5.
corOut	Return correlation of the returns or the influence function transformed returns. Must be one of "retCor", "retIFCor" or "none" (default).
return.coef	Boolean variable to indicate whether the coefficients of the penalized GLM fit are returned. Default if FALSE.
...	Additional parameters.

Value

A vector or a list depending on se.method.

Author(s)

Anthony-Alexander Christidis, [email protected]

Examples

# Loading data
data(edhec, package = "PerformanceAnalytics")
# Changing the data colnames
names(edhec)  =  c("CA", "CTA", "DIS", "EM", "EMN",
                 "ED", "FIA", "GM", "LS", "MA",
                 "RV", "SS", "FOF")
# Computing the standard errors for
# the two influence functions based approaches
RachevRatio.SE(edhec, se.method = c("IFiid","IFcorAdapt"),
               cleanOutliers = FALSE,
               fitting.method = c("Exponential", "Gamma")[1])

---

Standard Error Estimate for Robust Location (Mean) M-Estimator of Returns

Description

robMean.SE computes the standard error of the robust location (mean) M-estimator of the returns.

Usage

robMean.SE(
  data,
  family = c("mopt", "opt", "bisquare")[1],
  eff = 0.95,
  se.method = c("IFiid", "IFcor", "IFcorAdapt", "IFcorPW", "BOOTiid", "BOOTcor")[c(1, 4)],
  cleanOutliers = FALSE,
  fitting.method = c("Exponential", "Gamma")[1],
  d.GLM.EN = 5,
  freq.include = c("All", "Decimate", "Truncate")[1],
  freq.par = 0.5,
  corOut = c("none", "retCor", "retIFCor", "retIFCorPW")[1],
  return.coef = FALSE,
  ...
)

Arguments

data	Data of returns for one or multiple assets or portfolios.
family	Family for robust m-estimator of location. Must be one of "mopt" (default), "opt" or "bisquare".
eff	Tuning parameter for the normal distribution efficiency. Default is 0.99.
se.method	A character string indicating which method should be used to compute the standard error of the estimated standard deviation. One or a combination of: "IFiid" (default), "IFcor", "IFcorPW", "IFcorAdapt" (default), "BOOTiid" or "BOOTcor".
cleanOutliers	Boolean variable to indicate whether the pre-whitenning of the influence functions TS should be done through a robust filter. Default if FALSE.
fitting.method	Distribution used in the standard errors computation. Should be one of "Exponential" (default) or "Gamma".
d.GLM.EN	Order of the polynomial for the Exponential or Gamma fitting. Default polynomial order of 5.
freq.include	Frequency domain inclusion criteria. Must be one of "All" (default), "Decimate" or "Truncate."
freq.par	Percentage of the frequency used if "freq.include" is "Decimate" or "Truncate." Default is 0.5.
corOut	Return correlation of the returns or the influence function transformed returns. Must be one of "retCor", "retIFCor" or "none" (default).
return.coef	Boolean variable to indicate whether the coefficients of the penalized GLM fit are returned. Default if FALSE.
...	Additional parameters.

Value

A vector or a list depending on se.method.

Author(s)

Anthony-Alexander Christidis, [email protected]

Examples

# Loading data
data(edhec, package = "PerformanceAnalytics")
# Changing the data colnames
names(edhec)  =  c("CA", "CTA", "DIS", "EM", "EMN",
                 "ED", "FIA", "GM", "LS", "MA",
                 "RV", "SS", "FOF")
# Computing the standard errors for
# the two influence functions based approaches
robMean.SE(edhec, se.method = c("IFiid","IFcorAdapt"),
           fitting.method = c("Exponential", "Gamma")[1],
           family = "mopt", eff = 0.95)

---

Standard Error Estimate for Standard Deviation (SD) of Returns

Description

SD.SE computes the standard error of the standard deviation of the returns.

Usage

SD.SE(
  data,
  se.method = c("IFiid", "IFcor", "IFcorAdapt", "IFcorPW", "BOOTiid", "BOOTcor")[1:2],
  cleanOutliers = FALSE,
  fitting.method = c("Exponential", "Gamma")[1],
  d.GLM.EN = 5,
  freq.include = c("All", "Decimate", "Truncate")[1],
  freq.par = 0.5,
  corOut = c("none", "retCor", "retIFCor", "retIFCorPW")[1],
  return.coef = FALSE,
  ...
)

Arguments

data	Data of returns for one or multiple assets or portfolios.
se.method	A character string indicating which method should be used to compute the standard error of the estimated standard deviation. One or a combination of: "IFiid" (default), "IFcor" (default), "IFcorPW", "IFcorAdapt" (default), "BOOTiid" or "BOOTcor".
cleanOutliers	Boolean variable to indicate whether the pre-whitenning of the influence functions TS should be done through a robust filter. Default if FALSE.
fitting.method	Distribution used in the standard errors computation. Should be one of "Exponential" (default) or "Gamma".
d.GLM.EN	Order of the polynomial for the Exponential or Gamma fitting. Default polynomial order of 5.
freq.include	Frequency domain inclusion criteria. Must be one of "All" (default), "Decimate" or "Truncate."
freq.par	Percentage of the frequency used if "freq.include" is "Decimate" or "Truncate." Default is 0.5.
corOut	Return correlation of the returns or the influence function transformed returns. Must be one of "retCor", "retIFCor" or "none" (default).
return.coef	Boolean variable to indicate whether the coefficients of the penalized GLM fit are returned. Default if FALSE.
...	Additional parameters.

Value

A vector or a list depending on se.method.

Author(s)

Anthony-Alexander Christidis, [email protected]

Examples

# Loading data
data(edhec, package = "PerformanceAnalytics")
# Changing the data colnames
names(edhec) = c("CA", "CTA", "DIS", "EM", "EMN",
                 "ED", "FIA", "GM", "LS", "MA",
                 "RV", "SS", "FOF")
# Computing the standard errors for
# the two influence functions based approaches
SD.SE(edhec, se.method = c("IFiid","IFcor","IFcorAdapt"),
      cleanOutliers = FALSE,
      fitting.method = c("Exponential", "Gamma")[1])

---

Standard Error Estimate for Semi-Standared Deviation (SemiSD) of Returns

Description

SemiSD.SE computes the standard error of the SSD of the returns.

Usage

SemiSD.SE(
  data,
  se.method = c("IFiid", "IFcor", "IFcorAdapt", "IFcorPW", "BOOTiid", "BOOTcor")[1:2],
  cleanOutliers = FALSE,
  fitting.method = c("Exponential", "Gamma")[1],
  d.GLM.EN = 5,
  freq.include = c("All", "Decimate", "Truncate")[1],
  freq.par = 0.5,
  corOut = c("none", "retCor", "retIFCor", "retIFCorPW")[1],
  return.coef = FALSE,
  ...
)

Arguments

data	Data of returns for one or multiple assets or portfolios.
se.method	A character string indicating which method should be used to compute the standard error of the estimated standard deviation. One or a combination of: "IFiid" (default), "IFcor" (default), "IFcorPW", "IFcorAdapt", "BOOTiid", "BOOTcor", or "none".
cleanOutliers	Boolean variable to indicate whether the pre-whitenning of the influence functions TS should be done through a robust filter. Default if FALSE.
fitting.method	Distribution used in the standard errors computation. Should be one of "Exponential" (default) or "Gamma".
d.GLM.EN	Order of the polynomial for the Exponential or Gamma fitting. Default polynomial order of 5.
freq.include	Frequency domain inclusion criteria. Must be one of "All" (default), "Decimate" or "Truncate."
freq.par	Percentage of the frequency used if "freq.include" is "Decimate" or "Truncate." Default is 0.5.
corOut	Return correlation of the returns or the influence function transformed returns. Must be one of "retCor", "retIFCor" or "none" (default).
return.coef	Boolean variable to indicate whether the coefficients of the penalized GLM fit are returned. Default if FALSE.
...	Additional parameters.

Value

A vector or a list depending on se.method.

Author(s)

Anthony-Alexander Christidis, [email protected]

Examples

# Loading data
data(edhec, package = "PerformanceAnalytics")
# Changing the data colnames
names(edhec) = c("CA", "CTA", "DIS", "EM", "EMN",
                 "ED", "FIA", "GM", "LS", "MA",
                 "RV", "SS", "FOF")
# Computing the standard errors for
# the two influence functions based approaches
SemiSD.SE(edhec, se.method = c("IFiid","IFcor"),
          cleanOutliers = FALSE,
          fitting.method = c("Exponential", "Gamma")[1])

---

Standard Error Estimate for Sortino Ratio (SoR) of Returns

Description

SoR.SE computes the standard error of the Sortino ratio of the returns.

Usage

SoR.SE(
  data,
  const = 0,
  threshold = c("mean", "const")[1],
  se.method = c("IFiid", "IFcor", "IFcorAdapt", "IFcorPW", "BOOTiid", "BOOTcor")[c(1, 4)],
  cleanOutliers = FALSE,
  fitting.method = c("Exponential", "Gamma")[1],
  d.GLM.EN = 5,
  freq.include = c("All", "Decimate", "Truncate")[1],
  freq.par = 0.5,
  corOut = c("none", "retCor", "retIFCor")[1],
  return.coef = FALSE,
  ...
)

Arguments

data	Data of returns for one or multiple assets or portfolios.
const	Minimum acceptable return for threshold.
threshold	Parameter to determine whether we use a "mean" or "const" threshold.
se.method	A character string indicating which method should be used to compute the standard error of the estimated standard deviation. One or a combination of: "IFiid" (default), "IFcor", "IFcorPW", "IFcorAdapt" (default), "BOOTiid" or "BOOTcor".
cleanOutliers	Boolean variable to indicate whether the pre-whitenning of the influence functions TS should be done through a robust filter. Default if FALSE.
fitting.method	Distribution used in the standard errors computation. Should be one of "Exponential" (default) or "Gamma".
d.GLM.EN	Order of the polynomial for the Exponential or Gamma fitting. Default polynomial order of 5.
freq.include	Frequency domain inclusion criteria. Must be one of "All" (default), "Decimate" or "Truncate."
freq.par	Percentage of the frequency used if "freq.include" is "Decimate" or "Truncate." Default is 0.5.
corOut	Return correlation of the returns or the influence function transformed returns. Must be one of "retCor", "retIFCor" or "none" (default).
return.coef	Boolean variable to indicate whether the coefficients of the penalized GLM fit are returned. Default if FALSE.
...	Additional parameters.

Value

A vector or a list depending on se.method.

Author(s)

Anthony-Alexander Christidis, [email protected]

Examples

# Loading data
data(edhec, package = "PerformanceAnalytics")
# Changing the data colnames
names(edhec) = c("CA", "CTA", "DIS", "EM", "EMN",
                 "ED", "FIA", "GM", "LS", "MA",
                 "RV", "SS", "FOF")
# Computing the standard errors for
# the two influence functions based approaches
SoR.SE(edhec, se.method = c("IFiid","IFcorAdapt"),
       cleanOutliers = FALSE,
       fitting.method = c("Exponential", "Gamma")[1])

---

Standard Error Estimate for Sharpe Ratio (SR) of Returns

Description

SR.SE computes the standard error of the Sharpe ratio of the returns.

Usage

SR.SE(
  data,
  rf = 0,
  se.method = c("IFiid", "IFcor", "IFcorAdapt", "IFcorPW", "BOOTiid", "BOOTcor")[c(1, 4)],
  cleanOutliers = FALSE,
  fitting.method = c("Exponential", "Gamma")[1],
  d.GLM.EN = 5,
  freq.include = c("All", "Decimate", "Truncate")[1],
  freq.par = 0.5,
  corOut = c("none", "retCor", "retIFCor", "retIFCorPW")[1],
  return.coef = FALSE,
  ...
)

Arguments

data	Data of returns for one or multiple assets or portfolios.
rf	Risk free rate.
se.method	A character string indicating which method should be used to compute the standard error of the estimated standard deviation. One or a combination of: "IFiid" (default), "IFcor", "IFcorPW", "IFcorAdapt" (default), "BOOTiid" or "BOOTcor".
cleanOutliers	Boolean variable to indicate whether the pre-whitenning of the influence functions TS should be done through a robust filter. Default if FALSE.
fitting.method	Distribution used in the standard errors computation. Should be one of "Exponential" (default) or "Gamma".
d.GLM.EN	Order of the polynomial for the Exponential or Gamma fitting. Default polynomial order of 5.
freq.include	Frequency domain inclusion criteria. Must be one of "All" (default), "Decimate" or "Truncate."
freq.par	Percentage of the frequency used if "freq.include" is "Decimate" or "Truncate." Default is 0.5.
corOut	Return correlation of the returns or the influence function transformed returns. Must be one of "retCor", "retIFCor" or "none" (default).
return.coef	Boolean variable to indicate whether the coefficients of the penalized GLM fit are returned. Default if FALSE.
...	Additional parameters.

Value

A vector or a list depending on se.method.

Author(s)

Anthony-Alexander Christidis, [email protected]

Examples

# Loading data
data(edhec, package = "PerformanceAnalytics")
# Changing the data colnames
names(edhec) = c("CA", "CTA", "DIS", "EM", "EMN",
                 "ED", "FIA", "GM", "LS", "MA",
                 "RV", "SS", "FOF")
# Computing the standard errors for
# the two influence functions based approaches
SR.SE(edhec, se.method = c("IFiid","IFcorAdapt"),
      cleanOutliers = FALSE,
      fitting.method = c("Exponential", "Gamma")[1])

---

Standard Error Estimate for Value-at-Risk (VaR) of Returns

Description

VaR.SE computes the standard error of the value-at-risk of the returns.

Usage

VaR.SE(
  data = NULL,
  alpha = 0.95,
  se.method = c("IFiid", "IFcor", "IFcorAdapt", "IFcorPW", "BOOTiid", "BOOTcor")[1:2],
  cleanOutliers = FALSE,
  fitting.method = c("Exponential", "Gamma")[1],
  d.GLM.EN = 5,
  freq.include = c("All", "Decimate", "Truncate")[1],
  freq.par = 0.5,
  corOut = c("none", "retCor", "retIFCor", "retIFCorPW")[1],
  return.coef = FALSE,
  ...
)

Arguments

data	Data of returns for one or multiple assets or portfolios.
alpha	Confidence level for calculation. Default is alpha=0.95.
se.method	A character string indicating which method should be used to compute the standard error of the estimated standard deviation. One or a combination of: "IFiid" (default), "IFcor" (default), "IFcorPW", "IFcorAdapt", "BOOTiid" or "BOOTcor".
cleanOutliers	Boolean variable to indicate whether the pre-whitenning of the influence functions TS should be done through a robust filter. Default if FALSE.
fitting.method	Distribution used in the standard errors computation. Should be one of "Exponential" (default) or "Gamma".
d.GLM.EN	Order of the polynomial for the Exponential or Gamma fitting. Default polynomial order of 5.
freq.include	Frequency domain inclusion criteria. Must be one of "All" (default), "Decimate" or "Truncate."
freq.par	Percentage of the frequency used if "freq.include" is "Decimate" or "Truncate." Default is 0.5.
corOut	Return correlation of the returns or the influence function transformed returns. Must be one of "retCor", "retIFCor" or "none" (default).
return.coef	Boolean variable to indicate whether the coefficients of the penalized GLM fit are returned. Default if FALSE.
...	Additional parameters.

Value

A vector or a list depending on se.method.

Author(s)

Anthony-Alexander Christidis, [email protected]

Examples

# Loading data
data(edhec, package = "PerformanceAnalytics")
# Changing the data colnames
names(edhec) = c("CA", "CTA", "DIS", "EM", "EMN",
                 "ED", "FIA", "GM", "LS", "MA",
                 "RV", "SS", "FOF")
# Computing the standard errors for
# the two influence functions based approaches
VaR.SE(edhec, se.method = c("IFiid","IFcor"),
       cleanOutliers = FALSE,
       fitting.method = c("Exponential", "Gamma")[1])

---

Standard Error Estimate for Value-at-Risk Ratio (VaRratio) of Returns

Description

VaRratio.SE computes the standard error of the value-at-risk ratio of the returns.

Usage

VaRratio.SE(
  data,
  alpha = 0.1,
  rf = 0,
  se.method = c("IFiid", "IFcor", "IFcorAdapt", "IFcorPW", "BOOTiid", "BOOTcor")[c(1, 4)],
  cleanOutliers = FALSE,
  fitting.method = c("Exponential", "Gamma")[1],
  d.GLM.EN = 5,
  freq.include = c("All", "Decimate", "Truncate")[1],
  freq.par = 0.5,
  corOut = c("none", "retCor", "retIFCor", "retIFCorPW")[1],
  return.coef = FALSE,
  ...
)

Arguments

data	Data of returns for one or multiple assets or portfolios.
alpha	The tail probability of interest.
rf	Risk-free interest rate.
se.method	A character string indicating which method should be used to compute the standard error of the estimated standard deviation. One or a combination of: "IFiid" (default), "IFcor", "IFcorPW", "IFcorAdapt" (default), "BOOTiid" or "BOOTcor".
cleanOutliers	Boolean variable to indicate whether the pre-whitenning of the influence functions TS should be done through a robust filter. Default if FALSE.
fitting.method	Distribution used in the standard errors computation. Should be one of "Exponential" (default) or "Gamma".
d.GLM.EN	Order of the polynomial for the Exponential or Gamma fitting. Default polynomial order of 5.
freq.include	Frequency domain inclusion criteria. Must be one of "All" (default), "Decimate" or "Truncate."
freq.par	Percentage of the frequency used if "freq.include" is "Decimate" or "Truncate." Default is 0.5.
corOut	Return correlation of the returns or the influence function transformed returns. Must be one of "retCor", "retIFCor" or "none" (default).
return.coef	Boolean variable to indicate whether the coefficients of the penalized GLM fit are returned. Default if FALSE.
...	Additional parameters.

Value

A vector or a list depending on se.method.

Author(s)

Anthony-Alexander Christidis, [email protected]

Examples

# Loading data
data(edhec, package = "PerformanceAnalytics")
# Changing the data colnames
names(edhec) = c("CA", "CTA", "DIS", "EM", "EMN",
                 "ED", "FIA", "GM", "LS", "MA",
                 "RV", "SS", "FOF")
# Computing the standard errors for
# the two influence functions based approaches
VaRratio.SE(edhec, se.method = c("IFiid","IFcorAdapt"),
            cleanOutliers = FALSE,
            fitting.method = c("Exponential", "Gamma")[1])

---