[Highfrequency-commits] r107 - pkg/highfrequency/man

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

Author: kboudt
Date: 2014-09-05 13:17:06 +0200 (Fri, 05 Sep 2014)
New Revision: 107

Removed:
   pkg/highfrequency/man/spotvolatility.Rd
Modified:
   pkg/highfrequency/man/spotvol.Rd
Log:


Modified: pkg/highfrequency/man/spotvol.Rd
===================================================================
--- pkg/highfrequency/man/spotvol.Rd	2014-09-03 15:40:07 UTC (rev 106)
+++ pkg/highfrequency/man/spotvol.Rd	2014-09-05 11:17:06 UTC (rev 107)
@@ -167,7 +167,7 @@
 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{\FKF} is used to apply the Kalman filter. In addition to
+\code{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"})}
@@ -267,7 +267,7 @@
 Parameters:
 \tabular{ll}{
 \code{model} \tab String specifying the type of test to be used. Options
-include \code{"sGARCH", "eGARCH"}. See \code{\ugarchspec} in the
+include \code{"sGARCH", "eGARCH"}. See \code{ugarchspec} in the
 \code{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

Deleted: pkg/highfrequency/man/spotvolatility.Rd
===================================================================
--- pkg/highfrequency/man/spotvolatility.Rd	2014-09-03 15:40:07 UTC (rev 106)
+++ pkg/highfrequency/man/spotvolatility.Rd	2014-09-05 11:17:06 UTC (rev 107)
@@ -1,78 +0,0 @@
-% Generated by roxygen2 (4.0.1): do not edit by hand
-\docType{package}
-\name{spotvolatility}
-\alias{spotvolatility}
-\alias{spotvolatility-package}
-\title{Spot volatility estimation}
-\description{
-The \code{spotvolatility} package offers several methods to estimate spot
-volatility and its intraday seasonality, using high-frequency data.
-}
-\details{
-The following spot volatility estimation methods have been implemented:
-}
-\section{Deterministic periodicity}{
-
-The spot volatility is decomposed into a a deterministic periodic factor
-f_{i} (identical for every day in the sample) and a daily factor 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.
-}
-
-\section{Stochastic periodicity}{
-
-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.
-}
-
-\section{Nonparametric filtering}{
-
-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.
-}
-
-\section{Piecewise constant volatility}{
-
-Another nonparametric method is that of Fried (2012), which 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.
-}
-
-\section{GARCH models with intraday seasonality}{
-
-The package also includes an option to apply GARCH models, implemented by
-the \code{\link{rugarch}} package, to estimate spot volatility from intraday
-data. This is done by including external regressors in the model. These
-regressors are based on a flexible Fourier form, which was also used in the
-stochastic and deterministic periodicity estimation methods.
-}
-\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.
-}
-