[Highfrequency-commits] r83 - pkg/highfrequency/man

[noreply at r-forge.r-project.org]

Author: kboudt
Date: 2014-08-29 09:52:49 +0200 (Fri, 29 Aug 2014)
New Revision: 83

Removed:
   pkg/highfrequency/man/spotVol.rd
Log:


Deleted: pkg/highfrequency/man/spotVol.rd
===================================================================
--- pkg/highfrequency/man/spotVol.rd	2014-08-29 07:52:32 UTC (rev 82)
+++ pkg/highfrequency/man/spotVol.rd	2014-08-29 07:52:49 UTC (rev 83)
@@ -1,361 +0,0 @@
-% Generated by roxygen2 (4.0.1): do not edit by hand
-\name{spotvol}
-\alias{spotvol}
-\title{Spot volatility estimation}
-\usage{
-spotvol(data, method = "detper", ..., on = "minutes", k = 5,
-  marketopen = "09:30:00", marketclose = "16:00:00", tz = "GMT")
-}
-\arguments{
-\item{data}{Either an \code{\link{xts}} object, containing price data, or a
-\code{matrix} containing returns. For price data, irregularly spaced
-observations are allowed. They will be aggregated to the level specified by
-parameters \code{on} and \code{k}. For return data, the observations are
-assumed to be equispaced, with the time between them specified by \code{on}
-and \code{k}. Return data should be in matrix form, where each row
-corresponds to a day, and each column to an intraday period. The output
-will be in the same form as the input (\code{xts} or
-\code{matrix}/\code{numeric}).}
-
-\item{method}{specifies which method will be used to estimate the spot
-volatility. Options include \code{"detper"} and \code{"stochper"}.
-See 'Details'.}
-
-\item{on}{string indicating the time scale in which \code{k} is expressed.
-Possible values are: \code{"secs", "seconds", "mins", "minutes", "hours"}.}
-
-\item{k}{positive integer, indicating the number of periods to aggregate
-over. E.g. to aggregate an \code{xts} object to the 5 minute frequency, set
-\code{k = 5} and \code{on = "minutes"}.}
-
-\item{marketopen}{the market opening time. This should be in the time zone
-specified by \code{tz}. By default, \code{marketopen = "09:30:00"}.}
-
-\item{marketclose}{the market closing time. This should be in the time zone
-specified by \code{tz}. By default, \code{marketclose = "16:00:00"}.}
-
-\item{tz}{string specifying the time zone to which the times in \code{data}
-and/or \code{marketopen}/ \code{marketclose} belong. Default = \code{"GMT"}.}
-
-\item{...}{method-specific parameters (see 'Details').}
-}
-\value{
-A \code{spotvol} object, which is a list containing one or more of the
-following outputs, depending on the method used:
-
-\code{spot}
-
-An \code{xts} or \code{matrix} object (depending on the input) containing
-spot volatility estimates \eqn{\sigma_{t,i}}, reported for each interval
-\eqn{i} between \code{marketopen} and \code{marketclose} for every day
-\eqn{t} in \code{data}. The length of the intervals is specifiedby \code{k}
-and \code{on}. Methods that provide this output: All.
-
-\code{daily}
-
-An \code{xts} or \code{numeric} object (depending on the input) containing
-estimates of the daily volatility levels for each day \eqn{t} in \code{data},
-if the used method decomposed spot volatility into a daily and an intraday
-component. Methods that provide this output: \code{"detper"}.
-
-\code{periodic}
-
-An \code{xts} or \code{numeric} object (depending on the input) containing
-estimates of the intraday periodicity factor for each day interval \eqn{i}
-between \code{marketopen} and \code{marketclose}, if the spot volatility was
-decomposed into a daily and an intraday component. If the output is in
-\code{xts} format, this periodicity factor will be dated to the first day of
-the input data, but it is identical for each day in the sample. Methods that
-provide this output: \code{"detper"}.
-
-\code{par}
-
-A named list containing parameter estimates, for methods that estimate one
-or more parameters. Methods that provide this output:
-\code{"stochper", "kernel"}.
-
-\code{cp}
-
-A vector containing the change points in the volatility, i.e. the observation
-indices after which the volatility level changed, according to the applied
-tests. The vector starts with a 0. Methods that provide this output:
-\code{"piecewise"}.
-
-\code{ugarchfit}
-
-A \code{\link{ugarchfit}} object, as used by the \code{\link{rugarch}}
-package, containing all output from fitting the GARCH model to the data.
-Methods that provide this output: \code{"garch"}.
-}
-\description{
-The \code{spotvol} function offers several methods to estimate spot
-volatility and its intraday seasonality, using high-frequency data. It
-returns an object of class \code{spotvol}, which can contain various outputs,
-depending on the method used. See 'Details' for a description of each method.
-In any case, the output will contain the spot volatility estimates.
-
-The input can consist of price data or return data, either tick by tick or
-sampled at set intervals. The data will be converted to equispaced
-high-frequency returns \eqn{r_{t,i}} (read: the \eqn{i}th return on day
-\eqn{t}).
-}
-\details{
-The following estimation methods can be specified in \code{method}:
-
-\strong{Deterministic periodicity method (\code{"detper"})}
-
-Parameters:
-\tabular{ll}{
-\code{dailyvol} \tab A string specifying the estimation method for the daily
-component \eqn{s_t}. Possible values are \code{"bipower", "rv", "medrv"}.
-Default = \code{"bipower"}. \cr
-\code{periodicvol} \tab A string specifying the estimation method for the
-component of intraday volatility, that depends in a deterministic way on the
-intraday time at which the return is observed. Possible values are
-\code{"TML", "SD", "WSD", "OLS"}. See Boudt et al. (2011) for details.
-Default = \code{"TML"}.\cr
-\code{P1} \tab A positive integer corresponding to the number of cosinus
-terms used in the flexible Fourier specification of the periodicity function,
-see Andersen et al. (1997) for details. Default = 5. \cr
-\code{P2} \tab Same as \code{P1}, but for the sinus terms. Default = 5.\cr
-\code{dummies} \tab Boolean: in case it is \code{TRUE}, the parametric
-estimator of periodic standard deviation specifies the periodicity function
-as the sum of dummy variables corresponding to each intraday period. If it
-is \code{FALSE}, the parametric estimator uses the flexible Fourier
-specification. Default = \code{FALSE}.
-}
-Outputs (see 'Value' for a full description of each component):
-\itemize{
-\item{\code{spot}}
-\item{\code{daily}}
-\item{\code{periodic}}
-}
-The spot volatility is decomposed into a deterministic periodic factor
-\eqn{f_{i}} (identical for every day in the sample) and a daily factor
-\eqn{s_{t}} (identical for all observations within a day). Both components
-are then estimated separately. For more details, see Taylor and Xu (1997)
-and Andersen and Bollerslev (1997). The jump robust versions by Boudt et al.
-(2011) have also been implemented.
-
-\strong{Stochastic periodicity method (\code{"stochper"})}
-
-Parameters:
-\tabular{ll}{
-\code{P1} \tab A positive integer corresponding to the number of cosinus
-terms used in the flexible Fourier specification of the periodicity function.
-Default = 5. \cr
-\code{P2} \tab Same as \code{P1}, but for the sinus terms. Default = 5.\cr
-\code{init} \tab A named list of initial values to be used in the
-optimization routine (\code{"BFGS"} in \code{\link{optim}}). Default =
-\code{list(sigma = 0.03, sigma_mu = 0.005, sigma_h = 0.005, sigma_k = 0.05,
-phi = 0.2, rho = 0.98, mu = c(2, -0.5), delta_c = rep(0, max(1,P1)),
-delta_s = rep(0, max(1,P2)))}. See Beltratti & Morana (2001) for a definition
-of each parameter. \code{init} can contain any number of these parameters.
-For parameters not specified in \code{init}, the default initial value will
-be used.\cr
-\code{control} \tab A list of options to be passed down to
-\code{\link{optim}}.
-}
-Outputs (see 'Value' for a full description of each component):
-\itemize{
-\item{\code{spot}}
-\item{\code{par}}
-}
-This method by Beltratti and Morana (2001) assumes the periodicity factor to
-be stochastic. The spot volatility estimation is split into four components:
-a random walk, an autoregressive process, a stochastic cyclical process and
-a deterministic cyclical process. The model is estimated using a
-quasi-maximum likelihood method based on the Kalman Filter. The package
-\code{\link[=fkf]{FKF}} is used to apply the Kalman filter. In addition to
-the spot volatility estimates, all parameter estimates are returned.
-
-\strong{Nonparametric filtering (\code{"kernel"})}
-
-Parameters:
-\tabular{ll}{
-\code{type} \tab String specifying the type of kernel to be used. Options
-include \code{"gaussian", "epanechnikov", "beta"}. Default =
-\code{"gaussian"}.\cr
-\code{h} \tab Scalar or vector specifying bandwidth(s) to be used in kernel.
-If \code{h} is a scalar, it will be assumed equal throughout the sample. If
-it is a vector, it should contain bandwidths for each day. If left empty,
-it will be estimated. Default = \code{NULL}. \cr
-\code{est} \tab String specifiying the bandwidth estimation method. Possible
-values include \code{"cv", "quarticity"}. Method \code{"cv"} equals
-cross-validation, which chooses the bandwidth that minimizes the Integrated
-Square Error. \code{"quarticity"} multiplies the simple plug-in estimator
-by a factor based on the daily quarticity of the returns. \code{est} is
-obsolete if \code{h} has already been specified by the user. Default =
-\code{"cv"}.\cr
-\code{lower} \tab Lower bound to be used in bandwidth optimization routine,
-when using cross-validation method. Default is \eqn{0.1n^{-0.2}}. \cr
-\code{upper} \tab Upper bound to be used in bandwidth optimization routine,
-when using cross-validation method. Default is \eqn{n^{-0.2}}. \cr
-}
-Outputs (see 'Value' for a full description of each component):
-\itemize{
-\item{\code{spot}}
-\item{\code{par}}
-}
-This method by Kristensen (2010) filters the spot volatility in a
-nonparametric way by applying kernel weights to the standard realized
-volatility estimator. Different kernels and bandwidths can
-be used to focus on specific characteristics of the volatility process.
-
-Estimation results heavily depend on the bandwidth parameter \eqn{h}, so it
-is important that this parameter is well chosen. However, it is difficult to
-come up with a method that determines the optimal bandwidth for any kind of
-data or kernel that can be used. Although some estimation methods are
-provided, it is advised that you specify \eqn{h} yourself, or make sure that
-the estimation results are appropiate.
-
-One way to estimate \eqn{h}, is by using cross-validation. For each day in
-the sample, \eqn{h} is chosen as to minimize the Integrated Square Error,
-which is a function of \eqn{h}. However, this function often has multiple
-local minima, or no minima at all (\eqn{h -> \Inf}). To ensure a reasonable
-optimum is reached, strict boundaries have to be imposed on \eqn{h}. These
-can be specified by \code{lower} and \code{upper}, which by default are
-\eqn{0.1n^{-0.2}} and \eqn{n^{-0.2}} respectively, where \eqn{n} is the
-number of observations in a day.
-
-When using the method \code{"kernel"}, in addition to the spot volatility
-estimates, all used values of the bandwidth \eqn{h} are returned.
-
-\strong{Piecewise constant volatility (\code{"piecewise"})}
-
-Parameters:
-\tabular{ll}{
-\code{type} \tab String specifying the type of test to be used. Options
-include \code{"MDa", "MDb", "DM"}. See Fried (2012) for details. Default =
-\code{"MDa"}.\cr
-\code{m} \tab Number of observations to include in reference window.
-Default = \code{40}. \cr
-\code{n} \tab Number of observations to include in test window.
-Default = \code{20}. \cr
-\code{alpha} \tab Significance level to be used in tests. Note that the test
-will be executed many times (roughly equal to the total number of
-observations), so it is advised to use a small value for \code{alpha}, to
-avoid a lot of false positives. Default = \code{0.005}. \cr
-\code{volest} \tab String specifying the realized volatility estimator to be
-used in local windows. Possible values are \code{"bipower", "rv", "medrv"}.
-Default = \code{"bipower"}. \cr
-\code{online} \tab Boolean indicating whether estimations at a certain point
-\eqn{t} should be done online (using only information available at
-\eqn{t-1}), or ex post (using all observations between two change points).
-Default = \code{TRUE}.  \cr
-}
-Outputs (see 'Value' for a full description of each component):
-\itemize{
-\item{\code{spot}}
-\item{\code{cp}}
-}
-
-This nonparametric method by Fried (2012) assumes the volatility to be
-piecewise constant over local windows. Robust two-sample tests are applied to
-detect changes in variability between subsequent windows. The spot volatility
-can then be estimated by evaluating regular realized volatility estimators
-within each local window.
-
-Along with the spot volatility estimates, this method will return the
-detected change points in the volatility level. When plotting a
-\code{spotvol} object containing \code{cp}, these change points will be
-visualized.
-
-\strong{GARCH models with intraday seasonality  (\code{"garch"})}
-
-Parameters:
-\tabular{ll}{
-\code{model} \tab String specifying the type of test to be used. Options
-include \code{"sGARCH", "eGARCH"}. See \code{\link{ugarchspec}} in the
-\code{\link{rugarch}} package. Default = \code{"eGARCH"}. \cr
-\code{garchorder} \tab Numeric value of length 2, containing the order of
-the GARCH model to be estimated. Default = \code{c(1,1)}. \cr
-\code{dist} \tab String specifying the distribution to be assumed on the
-innovations. See \code{distribution.model} in \code{\link{ugarchspec}} for
-possible options. Default = \code{"norm"}. \cr
-\code{solver.control} \tab List containing solver options.
-See \code{\link{ugarchfit}} for possible values. Default = \code{list()}. \cr
-\code{P1} \tab A positive integer corresponding to the number of cosinus
-terms used in the flexible Fourier specification of the periodicity function.
-Default = 5. \cr
-\code{P2} \tab Same as \code{P1}, but for the sinus terms. Default = 5.\cr
-}
-Outputs (see 'Value' for a full description of each component):
-\itemize{
-\item{\code{spot}}
-\item{\code{ugarchfit}}
-}
-This method generates the external regressors needed to model the intraday
-seasonality with a Flexible Fourier form. The \code{\link{rugarch}} package
-is then employed to estimate the specified GARCH(1,1) model.
-
-Along with the spot volatility estimates, this method will return the
-\code{\link{ugarchfit}} object used by the \code{\link{rugarch}} package.
-}
-\examples{
-data(sample_prices_5min)
-
-# default method, deterministic periodicity
-vol1 <- spotvol(sample_prices_5min)
-par.def <- par(no.readonly = TRUE)
-plot(vol1)
-par(par.def)
-
-# compare to stochastic periodicity
-init = list(sigma = 0.03, sigma_mu = 0.005, sigma_h = 0.007,
-            sigma_k = 0.06, phi = 0.194, rho = 0.986, mu = c(1.87,-0.42),
-            delta_c = c(0.25, -0.05, -0.2, 0.13, 0.02), delta_s = c(-1.2,
-            0.11, 0.26, -0.03, 0.08))
-# next method will take around 110 iterations
-vol2 <- spotvol(sample_prices_5min, method = "stochper", init = init)
-plot(as.numeric(vol1$spot[1:780]), type="l")
-lines(as.numeric(vol2$spot[1:780]), col="red")
-legend("topright", c("detper", "stochper"), col = c("black", "red"), lty=1)
-
-# various kernel estimates
-h1 = bw.nrd0((1:nrow(sample_returns_5min))*(5*60))
-vol3 <- spotvol(sample_returns_5min, method = "kernel", h = h1)
-vol4 <- spotvol(sample_returns_5min, method = "kernel", est = "quarticity")
-vol5 <- spotvol(sample_returns_5min, method = "kernel", est = "cv")
-plot(vol3, length = 2880)
-lines(as.numeric(t(vol4$spot))[1:2880], col="red")
-lines(as.numeric(t(vol5$spot))[1:2880], col="blue")
-legend("topright", c("h = simple estimate", "h = quarticity corrected",
-       "h = crossvalidated"), col = c("black", "red", "blue"), lty=1)
-par(par.def)
-
-# piecewise constant volatility, using an example from Fried (2012)
-simdata <- matrix(sqrt(5/3)*rt(3000, df = 5), ncol = 500, byrow = TRUE)
-simdata <- c(1, 1, 1.5, 1.5, 2, 1)*simdata
-# the volatility of the simulated now changes at 1000, 2000 and 2500
-vol6 <- spotvol(simdata, method = "piecewise", m = 200, n  = 100,
-                online = FALSE)
-plot(vol6)
-
-# compare regular GARCH(1,1) model to eGARCH, both with external regressors
-vol7 <- spotvol(sample_returns_5min, method = "garch", model = "sGARCH")
-vol8 <- spotvol(sample_returns_5min, method = "garch", model = "eGARCH")
-plot(as.numeric(t(vol7$spot)), type = "l")
-lines(as.numeric(t(vol8$spot)), col = "red")
-legend("topleft", c("GARCH", "eGARCH"), col = c("black", "red"), lty=1)
-}
-\references{
-Andersen, T. G. and T. Bollerslev (1997). Intraday periodicity and volatility
-persistence in financial markets. Journal of Empirical Finance 4, 115-158.
-
-Beltratti, A. and C. Morana (2001). Deterministic and stochastic methods for estimation
-of intraday seasonal components with high frequency data. Economic Notes 30, 205-234.
-
-Boudt K., Croux C. and Laurent S. (2011). Robust estimation of intraweek periodicity
-in volatility and jump detection. Journal of Empirical Finance 18, 353-367.
-
-Fried, Roland (2012). On the online estimation of local constant volatilities.
-Computational Statistics and Data Analysis 56, 3080-3090.
-
-Kristensen, Dennis (2010). Nonparametric filtering of the realized spot volatility:
-A kernel-based approach. Econometric Theory 26, 60-93.
-
-Taylor, S. J. and X. Xu (1997). The incremental volatility information in one million
-foreign exchange quotations. Journal of Empirical Finance 4, 317-340.
-}
-