[Returnanalytics-commits] r2929 - in pkg/PerformanceAnalytics/sandbox/pulkit: R man vignettes
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Thu Aug 29 12:11:43 CEST 2013
Author: pulkit
Date: 2013-08-29 12:11:43 +0200 (Thu, 29 Aug 2013)
New Revision: 2929
Modified:
pkg/PerformanceAnalytics/sandbox/pulkit/R/DrawdownBeta.R
pkg/PerformanceAnalytics/sandbox/pulkit/R/DrawdownBetaMulti.R
pkg/PerformanceAnalytics/sandbox/pulkit/R/PSRopt.R
pkg/PerformanceAnalytics/sandbox/pulkit/R/ProbSharpeRatio.R
pkg/PerformanceAnalytics/sandbox/pulkit/man/BetaDrawdown.Rd
pkg/PerformanceAnalytics/sandbox/pulkit/man/MultiBetaDrawdown.Rd
pkg/PerformanceAnalytics/sandbox/pulkit/man/ProbSharpeRatio.Rd
pkg/PerformanceAnalytics/sandbox/pulkit/man/PsrPortfolio.Rd
pkg/PerformanceAnalytics/sandbox/pulkit/vignettes/ProbSharpe.pdf
Log:
changes during R CMD check
Modified: pkg/PerformanceAnalytics/sandbox/pulkit/R/DrawdownBeta.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/pulkit/R/DrawdownBeta.R 2013-08-29 10:03:24 UTC (rev 2928)
+++ pkg/PerformanceAnalytics/sandbox/pulkit/R/DrawdownBeta.R 2013-08-29 10:11:43 UTC (rev 2929)
@@ -4,21 +4,21 @@
#'@description
#'The drawdown beta is formulated as follows
#'
-#'\deqn{\beta_DD = \frac{{\sum_{t=1}^T}{q_t^\**}{(w_{k^{\**}(t)}-w_t)}}{D_{\alpha}(w^M)}}
+#'\deqn{\beta_DD = \frac{{\sum_{t=1}^T}{q_t^{*}}(w_{k^{*}(t)}-w_t)}{D_{\alpha}(w^M)}}
#' here \eqn{\beta_DD} is the drawdown beta of the instrument.
#'\eqn{k^{\**}(t)\in{argmax_{t_{\tau}{\le}k{\le}t}}w_k^M}
#'
-#'\eqn{q_t^\**=1/((1-\alpha)T)} if \eqn{d_t^M} is one of the
+#'\eqn{q_t^{*}=1/((1-\alpha)T)} if \eqn{d_t^M} is one of the
#'\eqn{(1-\alpha)T} largest drawdowns \eqn{d_1^{M} ,......d_t^M} of the
-#'optimal portfolio and \eqn{q_t^\** = 0} otherwise. It is assumed
-#'that \eqn{D_\alpha(w^M) {\neq} 0} and that \eqn{q_t^\**} and
-#'\eqn{k^{\**}(t)} are uniquely determined for all \eqn{t = 1....T}
+#'optimal portfolio and \eqn{q_t^{*} = 0} otherwise. It is assumed
+#'that \eqn{D_{\alpha}(w^M) \neq 0} and that \eqn{q_t^{*}} and
+#'\eqn{k^{*}(t)} are uniquely determined for all \eqn{t = 1....T}
#'
#'The numerator in \eqn{\beta_DD} is the average rate of return of the
#'instrument over time periods corresponding to the \eqn{(1-\alpha)T} largest
-#'drawdowns of the optimal portfolio, where \eqn{w_t - w_k^{\**}(t)}
+#'drawdowns of the optimal portfolio, where \eqn{w_t - w_k^{*}(t)}
#'is the cumulative rate of return of the instrument from the optimal portfolio
-#' peak time \eqn{k^\**(t)} to time t.
+#' peak time \eqn{k^{*}(t)} to time t.
#'
#'The difference in CDaR and standard betas can be explained by the
#'conceptual difference in beta definitions: the standard beta accounts for
Modified: pkg/PerformanceAnalytics/sandbox/pulkit/R/DrawdownBetaMulti.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/pulkit/R/DrawdownBetaMulti.R 2013-08-29 10:03:24 UTC (rev 2928)
+++ pkg/PerformanceAnalytics/sandbox/pulkit/R/DrawdownBetaMulti.R 2013-08-29 10:11:43 UTC (rev 2929)
@@ -4,15 +4,15 @@
#'@description
#'The drawdown beta is formulated as follows
#'
-#'\deqn{\beta_DD^i = \frac{{\sum_{s=1}^S}{\sum_{t=1}^T}p_s{q_t^\asterisk}{(w_{s,k^{\asterisk}(s,t)^i}-w_{st}^i)}}{D_{\alpha}(w^M)}}
+#'\deqn{\beta_DD^i = \frac{{\sum_{s=1}^S}{\sum_{t=1}^T}p_s{q_t^{*}}{(w_{s,k^{*}(s,t)^i}-w_{st}^i)}}{D_{\alpha}(w^M)}}
#' here \eqn{\beta_DD} is the drawdown beta of the instrument for multiple sample path.
-#'\eqn{k^{\asterisk}(s,t)\in{argmax_{t_{\tau}{\le}k{\le}t}}w_{sk}^p(x^\asterisk)}
+#'\eqn{k^{*}(s,t)\in{argmax_{t_{\tau}{\le}k{\le}t}}w_{sk}^p(x^{*})}
#'
#'The numerator in \eqn{\beta_DD} is the average rate of return of the
#'instrument over time periods corresponding to the \eqn{(1-\alpha)T} largest
-#'drawdowns of the optimal portfolio, where \eqn{w_t - w_k^{\asterisk}(t)}
+#'drawdowns of the optimal portfolio, where \eqn{w_t - w_k^{*}(t)}
#'is the cumulative rate of return of the instrument from the optimal portfolio
-#' peak time \eqn{k^\asterisk(t)} to time t.
+#' peak time \eqn{k^{*}(t)} to time t.
#'
#'The difference in CDaR and standard betas can be explained by the
#'conceptual difference in beta definitions: the standard beta accounts for
Modified: pkg/PerformanceAnalytics/sandbox/pulkit/R/PSRopt.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/pulkit/R/PSRopt.R 2013-08-29 10:03:24 UTC (rev 2928)
+++ pkg/PerformanceAnalytics/sandbox/pulkit/R/PSRopt.R 2013-08-29 10:11:43 UTC (rev 2929)
@@ -3,14 +3,14 @@
#'Maximizing for PSR leads to better diversified and more balanced hedge fund allocations compared to the concentrated
#'outcomes of Sharpe ratio maximization.We would like to find the vector of weights that maximize the expression
#'
-#'\deqn{\hat{PSR}(SR^\**) = Z\biggl[\frac{(\hat{SR}-SR^\**)\sqrt{n-1}}{\sqrt{1-\hat{\gamma_3}SR^\** + \frac{\hat{\gamma_4}-1}{4}\hat{SR^2}}}\biggr]}
+#'\deqn{\hat{PSR}(SR^{*}) = Z\bigg[\frac{(\hat{SR}-SR^{*})\sqrt{n-1}}{\sqrt{1-\hat{\gamma_3}SR^{*} + \frac{\hat{\gamma_4}-1}{4}\hat{SR^2}}}\bigg]}
#'
#'where \eqn{\sigma = \sqrt{E[(r-\mu)^2]}} ,its standard deviation.\eqn{\gamma_3=\frac{E\biggl[(r-\mu)^3\biggr]}{\sigma^3}} its skewness,
-#'\eqn{\gamma_4=\frac{E\biggl[(r-\mu)^4\biggr]}{\sigma^4}} its kurtosis and \eqn{SR = \frac{\mu}{\sigma}} its Sharpe Ratio.
-#'Because \eqn{\hat{PSR}(SR^\**)=Z[\hat{Z^\**}]} is a monotonic increasing function of
-#'\eqn{\hat{Z^\**}} ,it suffices to compute the vector that maximizes \eqn{\hat{Z^\**}}
+#'\eqn{\gamma_4=\frac{E\bigg[(r-\mu)^4\bigg]}{\sigma^4}} its kurtosis and \eqn{SR = \frac{\mu}{\sigma}} its Sharpe Ratio.
+#'Because \eqn{\hat{PSR}(SR^{*})=Z[\hat{Z^{*}}]} is a monotonic increasing function of
+#'\eqn{\hat{Z^{*}}} ,it suffices to compute the vector that maximizes \eqn{\hat{Z^{*}}}
#'
-#'This optimal vector is invariant of the value adopted by the parameter \eqn{SR^\**}.
+#'This optimal vector is invariant of the value adopted by the parameter \eqn{SR^{*}}.
#'Gradient Ascent Logic is used to compute the weights using the Function PsrPortfolio
#'@aliases PsrPortfolio
#'
Modified: pkg/PerformanceAnalytics/sandbox/pulkit/R/ProbSharpeRatio.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/pulkit/R/ProbSharpeRatio.R 2013-08-29 10:03:24 UTC (rev 2928)
+++ pkg/PerformanceAnalytics/sandbox/pulkit/R/ProbSharpeRatio.R 2013-08-29 10:11:43 UTC (rev 2929)
@@ -8,7 +8,7 @@
#' probability of skill. The reference Sharpe Ratio should be less than
#' the Observed Sharpe Ratio.
#'
-#' \deqn{\hat{PSR}(SR^\ast) = Z\biggl[\frac{(\hat{SR}-SR^\ast)\sqrt{n-1}}{\sqrt{1-\hat{\gamma_3}SR^\ast + \frac{\hat{\gamma_4}-1}{4}\hat{SR^2}}}\biggr]}
+#' \deqn{\hat{PSR}(SR^{*}) = Z\bigg[\frac{(\hat{SR}-SR^\ast)\sqrt{n-1}}{\sqrt{1-\hat{\gamma_3}SR^\ast + \frac{\hat{\gamma_4}-1}{4}\hat{SR^2}}}\bigg]}
#' Here \eqn{n} is the track record length or the number of data points. It can be daily,weekly or yearly depending on the input given
Modified: pkg/PerformanceAnalytics/sandbox/pulkit/man/BetaDrawdown.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/pulkit/man/BetaDrawdown.Rd 2013-08-29 10:03:24 UTC (rev 2928)
+++ pkg/PerformanceAnalytics/sandbox/pulkit/man/BetaDrawdown.Rd 2013-08-29 10:11:43 UTC (rev 2929)
@@ -35,24 +35,24 @@
The drawdown beta is formulated as follows
\deqn{\beta_DD =
- \frac{{\sum_{t=1}^T}{q_t^\**}{(w_{k^{\**}(t)}-w_t)}}{D_{\alpha}(w^M)}}
+ \frac{{\sum_{t=1}^T}{q_t^{*}}(w_{k^{*}(t)}-w_t)}{D_{\alpha}(w^M)}}
here \eqn{\beta_DD} is the drawdown beta of the
instrument.
\eqn{k^{\**}(t)\in{argmax_{t_{\tau}{\le}k{\le}t}}w_k^M}
- \eqn{q_t^\**=1/((1-\alpha)T)} if \eqn{d_t^M} is one of
+ \eqn{q_t^{*}=1/((1-\alpha)T)} if \eqn{d_t^M} is one of
the \eqn{(1-\alpha)T} largest drawdowns \eqn{d_1^{M}
- ,......d_t^M} of the optimal portfolio and \eqn{q_t^\** =
- 0} otherwise. It is assumed that \eqn{D_\alpha(w^M)
- {\neq} 0} and that \eqn{q_t^\**} and \eqn{k^{\**}(t)} are
+ ,......d_t^M} of the optimal portfolio and \eqn{q_t^{*} =
+ 0} otherwise. It is assumed that \eqn{D_{\alpha}(w^M)
+ \neq 0} and that \eqn{q_t^{*}} and \eqn{k^{*}(t)} are
uniquely determined for all \eqn{t = 1....T}
The numerator in \eqn{\beta_DD} is the average rate of
return of the instrument over time periods corresponding
to the \eqn{(1-\alpha)T} largest drawdowns of the optimal
- portfolio, where \eqn{w_t - w_k^{\**}(t)} is the
- cumulative rate of return of the instrument from the
- optimal portfolio peak time \eqn{k^\**(t)} to time t.
+ portfolio, where \eqn{w_t - w_k^{*}(t)} is the cumulative
+ rate of return of the instrument from the optimal
+ portfolio peak time \eqn{k^{*}(t)} to time t.
The difference in CDaR and standard betas can be
explained by the conceptual difference in beta
Modified: pkg/PerformanceAnalytics/sandbox/pulkit/man/MultiBetaDrawdown.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/pulkit/man/MultiBetaDrawdown.Rd 2013-08-29 10:03:24 UTC (rev 2928)
+++ pkg/PerformanceAnalytics/sandbox/pulkit/man/MultiBetaDrawdown.Rd 2013-08-29 10:11:43 UTC (rev 2929)
@@ -40,18 +40,17 @@
The drawdown beta is formulated as follows
\deqn{\beta_DD^i =
- \frac{{\sum_{s=1}^S}{\sum_{t=1}^T}p_s{q_t^\asterisk}{(w_{s,k^{\asterisk}(s,t)^i}-w_{st}^i)}}{D_{\alpha}(w^M)}}
+ \frac{{\sum_{s=1}^S}{\sum_{t=1}^T}p_s{q_t^{*}}{(w_{s,k^{*}(s,t)^i}-w_{st}^i)}}{D_{\alpha}(w^M)}}
here \eqn{\beta_DD} is the drawdown beta of the
instrument for multiple sample path.
- \eqn{k^{\asterisk}(s,t)\in{argmax_{t_{\tau}{\le}k{\le}t}}w_{sk}^p(x^\asterisk)}
+ \eqn{k^{*}(s,t)\in{argmax_{t_{\tau}{\le}k{\le}t}}w_{sk}^p(x^{*})}
The numerator in \eqn{\beta_DD} is the average rate of
return of the instrument over time periods corresponding
to the \eqn{(1-\alpha)T} largest drawdowns of the optimal
- portfolio, where \eqn{w_t - w_k^{\asterisk}(t)} is the
- cumulative rate of return of the instrument from the
- optimal portfolio peak time \eqn{k^\asterisk(t)} to time
- t.
+ portfolio, where \eqn{w_t - w_k^{*}(t)} is the cumulative
+ rate of return of the instrument from the optimal
+ portfolio peak time \eqn{k^{*}(t)} to time t.
The difference in CDaR and standard betas can be
explained by the conceptual difference in beta
Modified: pkg/PerformanceAnalytics/sandbox/pulkit/man/ProbSharpeRatio.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/pulkit/man/ProbSharpeRatio.Rd 2013-08-29 10:03:24 UTC (rev 2928)
+++ pkg/PerformanceAnalytics/sandbox/pulkit/man/ProbSharpeRatio.Rd 2013-08-29 10:11:43 UTC (rev 2929)
@@ -47,9 +47,9 @@
of skill. The reference Sharpe Ratio should be less than
the Observed Sharpe Ratio.
- \deqn{\hat{PSR}(SR^\ast) =
- Z\biggl[\frac{(\hat{SR}-SR^\ast)\sqrt{n-1}}{\sqrt{1-\hat{\gamma_3}SR^\ast
- + \frac{\hat{\gamma_4}-1}{4}\hat{SR^2}}}\biggr]} Here
+ \deqn{\hat{PSR}(SR^{*}) =
+ Z\bigg[\frac{(\hat{SR}-SR^\ast)\sqrt{n-1}}{\sqrt{1-\hat{\gamma_3}SR^\ast
+ + \frac{\hat{\gamma_4}-1}{4}\hat{SR^2}}}\bigg]} Here
\eqn{n} is the track record length or the number of data
points. It can be daily,weekly or yearly depending on the
input given \eqn{\hat{\gamma{_3}}} and
Modified: pkg/PerformanceAnalytics/sandbox/pulkit/man/PsrPortfolio.Rd
===================================================================
--- pkg/PerformanceAnalytics/sandbox/pulkit/man/PsrPortfolio.Rd 2013-08-29 10:03:24 UTC (rev 2928)
+++ pkg/PerformanceAnalytics/sandbox/pulkit/man/PsrPortfolio.Rd 2013-08-29 10:11:43 UTC (rev 2929)
@@ -23,22 +23,22 @@
would like to find the vector of weights that maximize
the expression
- \deqn{\hat{PSR}(SR^\**) =
- Z\biggl[\frac{(\hat{SR}-SR^\**)\sqrt{n-1}}{\sqrt{1-\hat{\gamma_3}SR^\**
- + \frac{\hat{\gamma_4}-1}{4}\hat{SR^2}}}\biggr]}
+ \deqn{\hat{PSR}(SR^{*}) =
+ Z\bigg[\frac{(\hat{SR}-SR^{*})\sqrt{n-1}}{\sqrt{1-\hat{\gamma_3}SR^{*}
+ + \frac{\hat{\gamma_4}-1}{4}\hat{SR^2}}}\bigg]}
where \eqn{\sigma = \sqrt{E[(r-\mu)^2]}} ,its standard
deviation.\eqn{\gamma_3=\frac{E\biggl[(r-\mu)^3\biggr]}{\sigma^3}}
its skewness,
- \eqn{\gamma_4=\frac{E\biggl[(r-\mu)^4\biggr]}{\sigma^4}}
+ \eqn{\gamma_4=\frac{E\bigg[(r-\mu)^4\bigg]}{\sigma^4}}
its kurtosis and \eqn{SR = \frac{\mu}{\sigma}} its Sharpe
- Ratio. Because \eqn{\hat{PSR}(SR^\**)=Z[\hat{Z^\**}]} is
- a monotonic increasing function of \eqn{\hat{Z^\**}} ,it
+ Ratio. Because \eqn{\hat{PSR}(SR^{*})=Z[\hat{Z^{*}}]} is
+ a monotonic increasing function of \eqn{\hat{Z^{*}}} ,it
suffices to compute the vector that maximizes
- \eqn{\hat{Z^\**}}
+ \eqn{\hat{Z^{*}}}
This optimal vector is invariant of the value adopted by
- the parameter \eqn{SR^\**}. Gradient Ascent Logic is used
+ the parameter \eqn{SR^{*}}. Gradient Ascent Logic is used
to compute the weights using the Function PsrPortfolio
}
\examples{
Modified: pkg/PerformanceAnalytics/sandbox/pulkit/vignettes/ProbSharpe.pdf
===================================================================
(Binary files differ)
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