[Highfrequency-commits] r82 - pkg/highfrequency/man

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

Author: kboudt
Date: 2014-08-29 09:52:32 +0200 (Fri, 29 Aug 2014)
New Revision: 82

Added:
   pkg/highfrequency/man/spotvolatility.Rd
Log:


Added: pkg/highfrequency/man/spotvolatility.Rd
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--- pkg/highfrequency/man/spotvolatility.Rd	                        (rev 0)
+++ pkg/highfrequency/man/spotvolatility.Rd	2014-08-29 07:52:32 UTC (rev 82)
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+% 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.
+}
+