\documentclass[10pt,a4paper,english]{article}

% now ignored, I think:
%\VignetteEngine{knitr::knitr}
%\VignetteIndexEntry{Asymptotic Distribution of the Markowitz Portfolio}
%\VignetteKeyword{Finance}
%\VignetteKeyword{Markowitz}
%\VignettePackage{MarkowitzR}

% front matter%FOLDUP
\usepackage[hyphens]{url}
\usepackage{amsmath}
\usepackage{amsfonts}
% for therefore
\usepackage{amssymb}
% for theorems?
\usepackage{amsthm}

\theoremstyle{plain}
\newtheorem{theorem}{Theorem}[section]
\newtheorem{lemma}[theorem]{Lemma}
\newtheorem{corollary}[theorem]{Corollary}

\theoremstyle{definition}
\newtheorem{definition}[theorem]{Definition}
\newtheorem{conjecture}[theorem]{Conjecture}
\newtheorem{assumption}[theorem]{Assumption}
\newtheorem{example}{Example}[section]

\theoremstyle{remark}
\newtheorem*{remark}{Remark}
\newtheorem*{caution}{Caution}
\newtheorem*{note}{Note}

% see http://tex.stackexchange.com/a/3034/2530
\PassOptionsToPackage{hyphens}{url}\usepackage{hyperref}
\usepackage{hyperref}
\usepackage[square,numbers]{natbib}
%\usepackage[authoryear]{natbib}
%\usepackage[iso]{datetime}
%\usepackage{datetime}

%%http://choorucode.com/2010/05/05/how-to-add-draft-watermark-in-latex/
%\usepackage{draftwatermark}
%\providecommand{\versnum}{V4}
%\SetWatermarkText{DRAFT \versnum}
%\SetWatermarkLightness{0.87}
%\SetWatermarkScale{4.5}

%\usepackage{fancyhdr}
%\pagestyle{fancy}
%\chead{}
%\rhead{}
%\lhead{}
%\rhead{\sc draft \versnum; do not distribute}
%\rfoot{}

%compactitem and such:
\usepackage[newitem,newenum,increaseonly]{paralist}

\makeatletter
\makeatother

%\input{sr_defs.tex}
%\usepackage[notheorems]{SharpeR}

%FOLDUP


% packages%FOLDUP
\RequirePackage{url}
\RequirePackage{amsmath}
\RequirePackage{amsfonts}
\RequirePackage{hyperref}
\RequirePackage{ifthen}

% deal with options to the 'package'. arg.
% for therefore
\RequirePackage{amssymb}
% for theorems?
\RequirePackage{amsthm}

\RequirePackage{xspace}
%UNFOLD

\providecommand{\sidenote}[2][{}]{\footnotemark\marginpar[#1]{\footnotesize\thefootnote{}.{} {#2}}}

% macros%FOLDUP

% ack.
%\providecommand{\figref}[1]{Figure~\ref{fig:#1}}
\providecommand{\figref}[1]{Figure\nobreakspace\ref{fig:#1}}
\providecommand{\eqnref}[1]{Equation\nobreakspace\ref{eqn:#1}}
\providecommand{\secref}[1]{Section\nobreakspace\ref{sec:#1}}
\providecommand{\subsecref}[1]{Section\nobreakspace\ref{subsec:#1}}
\providecommand{\tabref}[1]{Table\nobreakspace\ref{tab:#1}}
\providecommand{\lemmaref}[1]{Lemma\nobreakspace\ref{lemma:#1}}
\providecommand{\theoremref}[1]{Theorem\nobreakspace\ref{theorem:#1}}
\providecommand{\definitionref}[1]{Definition\nobreakspace\ref{definition:#1}}
\providecommand{\corollaryref}[1]{Corollary\nobreakspace\ref{corollary:#1}}
\providecommand{\conjectureref}[1]{Conjecture\nobreakspace\ref{conjecture:#1}}
\providecommand{\apxref}[1]{Approximation\nobreakspace\ref{apx:#1}}
\providecommand{\exampleref}[1]{Example\nobreakspace\ref{example:#1}}
\providecommand{\exerciseref}[1]{Exercise\nobreakspace\ref{exercise:#1}}
\providecommand{\chapterref}[1]{Chapter\nobreakspace\ref{chapter:#1}}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% meta meta commands
% emptyP if 1 is empty give 2 else give 3
%\providecommand{\mtP}[3]{\ifx\@empty#1\@empty#2\else#3\fi}
%\def\mtP#1#2#3{\ifx\@empty#1\@empty#2\else#3\fi}
%\def\mtP#1#2#3{\ifx\@empty\detokenize{#1}\@empty#2\else#3\fi}
\def\mtP#1#2#3{\if\relax\detokenize{#1}\relax#2\else#3\fi}
% listmore if 1 is empty give 1 else give `,1'
%\def\lMr#1{\ifx\@empty#1\@empty\relax\else{,#1}\fi}
\def\lMr#1{\ifx\@empty\detokenize{#1}\@empty\relax\else{,#1}\fi}

\providecommand{\MATHIT}[1]{\ensuremath{#1}\xspace}
\providecommand{\neUL}[3]{\mtP{#2}{\mtP{#3}{#1}{{#1}_{#3}}}{\mtP{#3}{{#1}^{#2}}{{#1}^{#2}_{#3}}}}
\providecommand{\neSUP}[2]{\mtP{#2}{#1}{{{#1}^{#2}}}}
\providecommand{\neSUB}[2]{\mtP{#2}{#1}{{{#1}_{#2}}}}
\providecommand{\mathSUB}[2]{\MATHIT{\neSUB{#1}{#2}}}

\providecommand{\wrapParens}[1]{\left(#1\right)}
\providecommand{\wrapBraces}[1]{\left\{#1\right\}}
\providecommand{\wrapBracks}[1]{\left[#1\right]}

%\providecommand{\wrapNeParens}[1]{\if\relax\detokenize{#1}\relax\else\wrapParens{#1}\fi}

\providecommand{\wrapNeParens}[1]{\mtP{#1}{}{\wrapParens{#1}}}
\providecommand{\wrapNeBraces}[1]{\mtP{#1}{}{\wrapBraces{#1}}}
\providecommand{\wrapNeBracks}[1]{\mtP{#1}{}{\wrapBracks{#1}}}

\providecommand{\abs}[1]{\MATHIT{\left| #1 \right|}}
\providecommand{\ceil}[1]{\MATHIT{\left\lceil #1 \right\rceil}}
\providecommand{\floor}[1]{\MATHIT{\left\lfloor #1 \right\rfloor}}

\providecommand{\mathSUB}[2]{\MATHIT{\neSUB{#1}{#2}}}
\renewcommand{\Pr}[1]{\MATHIT{\operatorname{Pr}\left\{#1\right\}}}
\providecommand{\vect}[1]{\MATHIT{\boldsymbol{#1}}}
%\providecommand{\eye}{\MATHIT{I}}
\providecommand{\Mtx}[1]{\MATHIT{\mathsf{#1}}}
\providecommand{\MtxUL}[3]{\mathUL{\Mtx{#1}}{#2}{#3}}
\providecommand{\nePAIR}[2]{#1\lMr{#2}}
\providecommand{\mathUL}[3]{\MATHIT{\neUL{#1}{#2}{#3}}}
\providecommand{\mathSUP}[2]{\MATHIT{\neSUP{#1}{#2}}}
\providecommand{\vectUL}[3]{\mathUL{\vect{#1}}{#2}{#3}}
\providecommand{\SEPbbb}[1]{\mathbb{#1}}
\providecommand{\reals}[1]{\MATHIT{\SEPbbb{R}^{#1}}}

\providecommand{\oneby}[1]{\MATHIT{\frac{1}{#1}}}

% yes, I am lazy.
\providecommand{\txtSR}{Sharpe ratio\xspace}
\providecommand{\txtSRs}{Sharpe ratios\xspace}
\providecommand{\txtSNR}{signal-noise ratio\xspace}
\providecommand{\txtSNRs}{signal-noise ratios\xspace}

\providecommand{\txtFSR}{ex-factor Sharpe ratio\xspace}
\providecommand{\txtFSNR}{ex-factor signal-noise ratio\xspace}
\providecommand{\txtFSNRs}{ex-factor signal-noise ratios\xspace}

\providecommand{\txtMwtz}{Markowitz\xspace}
\providecommand{\txtMP}{Markowitz portfolio\xspace}
\providecommand{\txtCR}{Cram\'{e}r-Rao\xspace}
% I have yet to decide:
\providecommand{\txtLP}{lambda prime\xspace}
\providecommand{\txtMLP}{multiple \txtLP}
\providecommand{\txtUPD}{upsilon\xspace}

% abbreviations
\providecommand{\myabrvc}[1]{\emph{#1},\xspace}
\providecommand{\myabrv}[1]{\emph{#1}\xspace}

\providecommand{\eg}{\myabrvc{e.g.}}
\providecommand{\ie}{\myabrvc{i.e.}}
\providecommand{\nb}{\myabrvc{n.b.}}

\providecommand{\iid}{\myabrv{i.i.d.}}
\providecommand{\viz}{\myabrv{viz.}}
\providecommand{\etc}{\myabrv{etc.}}
\providecommand{\etal}{\myabrv{et al.}}
\providecommand{\cf}{\myabrv{cf.}}
\providecommand{\WLOG}{without loss of generality, }
\providecommand{\cetpar}{\myabrv{ceterus paribus}}


\providecommand{\setwo}[2]{\MATHIT{\left\{ #1 \left|\; {#2} \right.\right\}}}

\providecommand{\defeq}{=_{\operatorname{df}}}
\providecommand{\eqdef}{=:}
\providecommand{\kth}[1]{\MATHIT{#1^{\text{th}}}}

% convergence limits, etc.
% convergence in distribution
\providecommand{\convd}{\MATHIT{\overset{d}{\rightarrow}}}
% convergence in probability
\providecommand{\convp}{\MATHIT{\overset{p}{\rightarrow}}}
% convergence almost surely
\providecommand{\convas}{\MATHIT{\overset{a.s.}{\rightarrow}}}


% utilities%FOLDUP
% convert something into a function
\providecommand{\funcitUL}[4]{\MATHIT{\mathUL{#1}{#2}{#3}\wrapNeParens{#4}}}
\providecommand{\funcitL}[3]{\funcitUL{#1}{}{#2}{#3}}
\providecommand{\funcit}[2]{\funcitUL{#1}{}{}{#2}}
%compact fraction;
\providecommand{\fracc}[2]{\MATHIT{#1 / #2}}
% with paren wrap
\providecommand{\fraccp}[2]{\MATHIT{\wrapNeParens{#1}/\wrapNeParens{#2}}}
%small 'mbox'
%http://stackoverflow.com/questions/1239786/latex-math-mode-and-mbox-mode
\providecommand{\smbox}[1]{\mbox{\scriptsize #1}}

%\providecommand{\argmax}{\MATHIT{\mbox{argmax}}}
\providecommand{\argmax}{\MATHIT{\mathop{\mathrm{argmax}}}}
\providecommand{\argmin}{\MATHIT{\mathop{\mathrm{argmin}}}}


%UNFOLD

% all commands%FOLDUP
% vector operator
\providecommand{\vecop}[1]{\funcit{\operatorname{vec}}{#1}}
%\providecommand{\vec}[1]{\funcit{\mbox{vec}}{#1}}
\providecommand{\vech}[1]{\funcit{\operatorname{vech}}{#1}}  % Magnus writes as v( )
\providecommand{\tril}[1]{\funcit{\operatorname{tril}}{#1}}  

\providecommand{\fvec}[1]{\vecop{#1}}
\providecommand{\fvech}[1]{\vech{#1}}
\providecommand{\ftril}[1]{\tril{#1}}
% inverses of these
\providecommand{\fivec}[1]{\funcitUL{{\operatorname{vec}}}{-1}{}{#1}}
\providecommand{\fivech}[1]{\funcitUL{{\operatorname{vech}}}{-1}{}{#1}}
\providecommand{\fitril}[1]{\funcitUL{{\operatorname{tril}}}{-1}{}{#1}}

\providecommand{\Elim}[1][{}]{\MtxUL{L}{}{#1}}
\providecommand{\Dupp}[1][{}]{\MtxUL{D}{}{#1}}
\providecommand{\Unun}[1][{-1}]{\MtxUL{U}{}{#1}}
\providecommand{\Komm}[1][{}]{\MtxUL{K}{}{#1}}
\providecommand{\kron}{\MATHIT{\otimes}}
\providecommand{\krov}{\MATHIT{\oslash}}

\providecommand{\qfElim}[1]{\qform{#1}{\Elim}}
\providecommand{\qoElim}[1]{\qoform{#1}{\Elim}}
\providecommand{\EXD}[1]{\MATHIT{\Elim {#1} \Dupp}}


% binary by: a x b
\providecommand{\bby}[2]{\MATHIT{{#1}\times{#2}}}
% k x k 
\providecommand{\sbby}[1]{\bby{#1}{#1}}

\providecommand{\AkronA}[1]{\MATHIT{{#1} \kron {#1}}}
\providecommand{\AtkronA}[1]{\MATHIT{\tr{#1} \kron {#1}}}
\providecommand{\AkronAt}[1]{\MATHIT{{#1} \kron \tr{#1}}}

%\providecommand{\trace}[1]{\funcit{\mbox{tr}}{#1}}
\providecommand{\trace}[1]{\funcit{\operatorname{tr}}{#1}}
% is this replicated elsewhere?
\providecommand{\det}[1]{\abs{#1}}
\renewcommand{\det}[1]{\abs{#1}}
\providecommand{\detpow}[2]{\neUL{\det{#1}}{#2}{}}
% functional det
\providecommand{\fdet}[1]{\funcit{\operatorname{det}}{#1}}
\renewcommand{\fdet}[1]{\funcit{\operatorname{det}}{#1}}
\providecommand{\logdet}[1]{\MATHIT{\log\det{#1}}}
\providecommand{\sign}[1]{\funcit{\operatorname{sign}}{#1}}


\providecommand{\ccinterval}[2]{\wrapBracks{#1,#2}}
% as vector, a column.
\providecommand{\asrowvec}[1]{\wrapBracks{#1}}
\providecommand{\asvec}[1]{\tr{\asrowvec{#1}}}
\providecommand{\vcat}[2]{\asvec{\tr{#1},\tr{#2}}}

\providecommand{\diag}[1]{\funcit{\operatorname{diag}}{#1}}
\providecommand{\fsymd}[1]{\funcit{f_{\mbox{sym}}}{#1}}

\providecommand{\threebythree}[9]{\wrapBracks{\begin{array}{ccc}{#1}&{#2}&{#3}\\{#4}&{#5}&{#6}\\{#7}&{#8}&{#9}\end{array}}}
\providecommand{\twotwosys}[6]{\wrapBracks{\begin{array}{cc|c}{#1}&{#2}&{#3}\\{#4}&{#5}&{#6}\end{array}}}
\providecommand{\twobytwo}[4]{\wrapBracks{\begin{array}{cc}{#1}&{#2}\\{#3}&{#4}\end{array}}}
\providecommand{\twobytwosym}[3]{\twobytwo{#1}{#2}{\tr{#2}}{#3}}
\providecommand{\twobytwossym}[3]{\twobytwo{#1}{#2}{#2}{#3}}
\providecommand{\twobyone}[2]{\wrapBracks{\begin{array}{r}{#1}\\{#2}\end{array}}}
\providecommand{\twobythree}[6]{\wrapBracks{\begin{array}{ccc}{#1}&{#2}&{#3}\\{#4}&{#5}&{#6}\end{array}}}
\providecommand{\threebytwo}[6]{\wrapBracks{\begin{array}{cc}{#1}&{#2}\\{#3}&{#4}\\{#5}&{#6}\end{array}}}
\providecommand{\threebyone}[3]{\wrapBracks{\begin{array}{r}{#1}\\{#2}\\{#3}\end{array}}}
\providecommand{\onebytwo}[2]{\wrapBracks{\begin{array}{rr}{#1}&{#2}\end{array}}}

\providecommand{\threebythreesym}[6]{\threebythree{#1}{#2}{#3}{\tr{#2}}{#4}{#5}{\tr{#3}}{\tr{#5}}{#6}}
\providecommand{\threebythreessym}[6]{\threebythree{#1}{#2}{#3}{#2}{#4}{#5}{#3}{#5}{#6}}


% idealized and conditional SNR functions
\providecommand{\pSNRfoo}[2]{\funcit{\mathSUB{\mbox{SNR}}{#1}}{#2}}
\providecommand{\sSNRfoo}[2]{\funcit{\mathSUB{\hat{\mbox{SNR}}}{#1}}{#2}}

\providecommand{\pSNR}[1]{\pSNRfoo{}{#1}}
\providecommand{\sSNR}[1]{\sSNRfoo{}{#1}}

\providecommand{\SNRfunc}[1]{\pSNRfoo{}{#1}}
\providecommand{\SNRfunci}[1]{\pSNRfoo{i}{#1}}
\providecommand{\SNRfuncu}[1]{\pSNRfoo{u}{#1}}
\providecommand{\SNRfuncc}[1]{\pSNRfoo{c}{#1}}

% transpose and inverse
\providecommand{\trsym}[0]{\top}
\providecommand{\tr}[1]{\mathSUP{#1}{\trsym}}
\providecommand{\minv}[1]{\mathSUP{#1}{-1}}
\providecommand{\trminv}[1]{\mathSUP{#1}{-\trsym}}
\providecommand{\minvAB}[2]{\MATHIT{\minv{#1}{#2}}}
\providecommand{\minvParens}[1]{\minv{\wrapParens{#1}}}

% for scalars:
\providecommand{\sinv}[1]{\mathSUP{#1}{-1}}
\providecommand{\sinvParens}[1]{\sinv{\wrapParens{#1}}}

% Hermitian and pseudoinverse
\providecommand{\htr}[1]{\mathSUP{#1}{*}}
\providecommand{\pinvsym}[0]{+}
\providecommand{\pinv}[1]{\mathSUP{#1}{\pinvsym}}

% He and such
\providecommand{\He}[2][{}]{\funcit{\mathSUB{H\!e}{#1}}{#2}}
\providecommand{\Hh}[2][{}]{\funcit{\mathSUB{H\!h}{#1}}{#2}}

\providecommand{\bottom}{\bot}

% Complement
%\providecommand{\cmpl}[1]{\mathSUP{#1}{\mathcal{C}}}
\providecommand{\cmpl}[1]{\mathSUP{#1}{\bot}}

% cholesky 
\providecommand{\chol}[1]{\mathSUP{#1}{1/2}}
\providecommand{\trchol}[1]{\mathSUP{#1}{\trsym/2}}
\providecommand{\ichol}[1]{\mathSUP{#1}{-1/2}}
\providecommand{\trichol}[1]{\mathSUP{#1}{-\trsym/2}}
% null space
\providecommand{\mnull}[1]{\mathSUP{#1}{C}}

% singleentry matrix; see matrix cookbook 9.7.1
\providecommand{\Jsing}[2]{\MtxUL{J}{#1#2}{}}

% these are private to this file%FOLDUP
\providecommand{\prvsymi}{x}
\providecommand{\prvsymj}{y}
\providecommand{\prvsymk}{f}
\providecommand{\prvsyml}{z}
\providecommand{\prvsyme}{v}
\providecommand{\prvsymf}{f}
\providecommand{\prvsymv}{v}
\providecommand{\prvsymu}{u}
% prices:
\providecommand{\prvsymp}{p}
% arithmetic and geometric:
\providecommand{\prvsyma}{r}
\providecommand{\prvsymg}{l}

\providecommand{\prvsymPortfolio}{w}
\providecommand{\prvsymPASSTHROUGH}{W}

\providecommand{\prvsymWilk}{U}
\providecommand{\prvsymHLT}{T}
\providecommand{\prvsymPBT}{P}
\providecommand{\prvsymRLR}{R}
%UNFOLD

% prices/mtm
%\providecommand{\pryt}[1][t]{\mathSUB{p}{#1}}
\providecommand{\pryp}[1][]{\mathSUB{\prvsymp}{#1}}
%\providecommand{\grett}[1][t]{\mathSUB{l}{#1}}
%\providecommand{\arett}[1][t]{\mathSUB{r}{#1}}

\renewcommand{\exp}[1]{\MATHIT{e^{#1}}}
\providecommand{\longexp}[1]{\funcit{\operatorname{exp}}{#1}}

%scalar returns:
\providecommand{\reti}[1][]{\mathSUB{\prvsymi}{#1}}
\providecommand{\retj}[1][]{\mathSUB{\prvsymj}{#1}}
\providecommand{\retk}[1][]{\mathSUB{\prvsymk}{#1}}
\providecommand{\retl}[1][]{\mathSUB{\prvsyml}{#1}}
\providecommand{\retf}[1][]{\mathSUB{\prvsymf}{#1}}
\providecommand{\retv}[1][]{\mathSUB{\prvsymv}{#1}}
\providecommand{\reta}[1][]{\mathSUB{\prvsyma}{#1}}
\providecommand{\retg}[1][]{\mathSUB{\prvsymg}{#1}}

%vector returns:
\providecommand{\vreti}[1][]{\vectUL{\prvsymi}{}{#1}}
\providecommand{\vretj}[1][]{\vectUL{\prvsymj}{}{#1}}
\providecommand{\vretk}[1][]{\vectUL{\prvsymk}{}{#1}}
\providecommand{\vretl}[1][]{\vectUL{\prvsyml}{}{#1}}
\providecommand{\vretf}[1][]{\vectUL{\prvsymf}{}{#1}}
\providecommand{\vretv}[1][]{\vectUL{\prvsymv}{}{#1}}
\providecommand{\vreta}[1][]{\vectUL{\prvsyma}{}{#1}}
\providecommand{\vretg}[1][]{\vectUL{\prvsymg}{}{#1}}

% heteroskedasticity
\providecommand{\prvsymVOLA}{s}
\providecommand{\fvola}[1][]{\mathSUB{\prvsymVOLA}{#1}}
\providecommand{\fvvola}[1][t]{\vectUL{\prvsymVOLA}{}{#1}}
\providecommand{\volafoo}[2]{\mathUL{\gamma}{#1}{#2}}
\providecommand{\volavar}[1][]{\volafoo{2}{#1}}
\providecommand{\volaivar}[1][]{\volafoo{-2}{#1}}
% sked
\providecommand{\prvsymSKED}{s}
\providecommand{\fsked}[1][]{\mathSUB{\prvsymSKED}{#1}}
\providecommand{\fvsked}[1][t]{\vectUL{\prvsymSKED}{}{#1}}


% augmented
\providecommand{\avreti}[1][]{\vectUL{\tilde{\prvsymi}}{}{#1}}
\providecommand{\amreti}[1][]{\MtxUL{\tilde{\MakeUppercase{\prvsymi}}}{}{#1}}
\providecommand{\aavreti}[1][]{\vectUL{\tilde{\tilde{\prvsymi}}}{}{#1}}

% transpose of same
\providecommand{\trvreti}[1][]{\vectUL{\prvsymi}{\trsym}{#1}}
\providecommand{\trvretj}[1][]{\vectUL{\prvsymj}{\trsym}{#1}}
\providecommand{\trvretk}[1][]{\vectUL{\prvsymk}{\trsym}{#1}}
\providecommand{\trvretl}[1][]{\vectUL{\prvsyml}{\trsym}{#1}}
\providecommand{\trvretf}[1][]{\vectUL{\prvsymf}{\trsym}{#1}}
\providecommand{\trvretv}[1][]{\vectUL{\prvsymv}{\trsym}{#1}}
\providecommand{\trvreta}[1][]{\vectUL{\prvsyma}{\trsym}{#1}}
\providecommand{\trvretg}[1][]{\vectUL{\prvsymg}{\trsym}{#1}}

%matrix returns:
\providecommand{\mreti}[1][]{\MtxUL{\MakeUppercase{\prvsymi}}{}{#1}}
\providecommand{\mretj}[1][]{\MtxUL{\MakeUppercase{\prvsymj}}{}{#1}}
\providecommand{\mretk}[1][]{\MtxUL{\MakeUppercase{\prvsymk}}{}{#1}}
\providecommand{\mretl}[1][]{\MtxUL{\MakeUppercase{\prvsyml}}{}{#1}}
\providecommand{\mretf}[1][]{\MtxUL{\MakeUppercase{\prvsymf}}{}{#1}}
\providecommand{\mretv}[1][]{\MtxUL{\MakeUppercase{\prvsymv}}{}{#1}}
\providecommand{\mreta}[1][]{\MtxUL{\MakeUppercase{\prvsyma}}{}{#1}}
\providecommand{\mretg}[1][]{\MtxUL{\MakeUppercase{\prvsymg}}{}{#1}}

% the factor or signal
\providecommand{\sfactsym}[0]{\MATHIT{\prvsymf}}
\providecommand{\sfact}[1][t]{\mathSUB{\prvsymf}{#1}}
\providecommand{\vfact}[1][t]{\vectUL{\prvsymf}{}{#1}}
\providecommand{\trvfact}[1][t]{\vectUL{\prvsymf}{\trsym}{#1}}
\providecommand{\mfact}[1][]{\MtxUL{\MakeUppercase{\prvsymf}}{}{#1}}

% index. i or t?
\providecommand{\idx}{\MATHIT{t}}


% distribution of factor
\providecommand{\pfacmu}{\vectUL{\prvMean}{}{f}}
\providecommand{\trpfacmu}{\vectUL{\prvMean}{\trsym}{f}}
\providecommand{\pfacsig}{\MtxUL{\Gamma}{}{f}}
\providecommand{\pfacgram}[1][]{\MATHIT{\pfacsig + #1\pfacmu\trpfacmu}}
\providecommand{\pfaccov}{\MtxUL{\prvCov}{}{f}}

\providecommand{\sfacmu}{\vectUL{\hat{\prvMean}}{}{f}}
\providecommand{\trsfacmu}{\vectUL{\hat{\prvMean}}{\trsym}{f}}
\providecommand{\sfacsig}{\MtxUL{\hat{\Gamma}}{}{f}}

% the error or residual
\providecommand{\serrt}[1][t]{\mathSUB{\prvsymu}{#1}}
\providecommand{\verrt}[1][t]{\vect{\mathSUB{\prvsymu}{#1}}}
\providecommand{\merrt}[1][]{\MtxUL{\MakeUppercase{\prvsymu}}{}{#1}}

% population and sample versions
\providecommand{\pserrt}[1][t]{\mathSUB{\epsilon}{#1}}
\providecommand{\pverrt}[1][t]{\vect{\mathSUB{\epsilon}{#1}}}
\providecommand{\pmerrt}[1][]{\MtxUL{E}{}{#1}}
\providecommand{\sserrt}[1][t]{\mathSUB{\hat{\epsilon}}{#1}}
\providecommand{\sverrt}[1][t]{\vect{\mathSUB{\hat{\epsilon}}{#1}}}
\providecommand{\smerrt}[1][]{\MtxUL{\hat{E}}{}{#1}}

% population and sample (conditional) markowitz weights and passthrough
\providecommand{\pmarkow}[1][]{\mathSUB{w}{#1}}
\providecommand{\smarkow}[1][]{\mathSUB{\hat{w}}{#1}}
\providecommand{\ppasthru}[1][]{\mathSUB{\prvsymPASSTHROUGH}{#1}}
\providecommand{\spasthru}[1][]{\mathSUB{\hat{\prvsymPASSTHROUGH}}{#1}}
\providecommand{\trppasthru}[1][]{\mathUL{\prvsymPASSTHROUGH}{\trsym}{#1}}
\providecommand{\trspasthru}[1][]{\mathUL{\hat{\prvsymPASSTHROUGH}}{\trsym}{#1}}

\providecommand{\apasthru}[1][a]{\ppasthru[#1]}
\providecommand{\trapasthru}[1][a]{\trppasthru[#1]}

\providecommand{\ppasopt}[1][]{\ppasthru[\nePAIR{*}{#1}]}
\providecommand{\spasopt}[1][]{\spasthru[\nePAIR{*}{#1}]}

% just some eigenvalue, eigenvec
\providecommand{\eigval}[1][]{\mathUL{\lambda}{}{#1}}
\providecommand{\eigvec}[1][]{\vectUL{\nu}{}{#1}}

% HE eigenvalues, eigenvects
\providecommand{\pheeig}[1][]{\mathUL{\lambda}{}{#1}}
\providecommand{\sheeig}[1][]{\mathUL{\hat{\lambda}}{}{#1}}
\providecommand{\pheevc}[1][]{\vectUL{\nu}{}{#1}}
\providecommand{\sheevc}[1][]{\vectUL{\hat{\nu}}{}{#1}}

% some functions
\providecommand{\fmax}[1]{\funcit{max}{#1}}
\providecommand{\fmin}[1]{\funcit{min}{#1}}
\providecommand{\feig}[1]{\funcit{eig}{#1}}

% MGLH
\providecommand{\pWILK}[1][]{\mathUL{\prvsymWilk}{}{#1}}
\providecommand{\pHLT}[1][]{\mathUL{\prvsymHLT}{}{#1}}
\providecommand{\pPBT}[1][]{\mathUL{\prvsymPBT}{}{#1}}
\providecommand{\pRLR}[1][]{\mathUL{\prvsymRLR}{}{#1}}
\providecommand{\sWILK}[1][]{\mathUL{\hat{\prvsymWilk}}{}{#1}}
\providecommand{\sHLT}[1][]{\mathUL{\hat{\prvsymHLT}}{}{#1}}
\providecommand{\sPBT}[1][]{\mathUL{\hat{\prvsymPBT}}{}{#1}}
\providecommand{\sRLR}[1][]{\mathUL{\hat{\prvsymRLR}}{}{#1}}

\providecommand{\hejG}{\Mtx{G}}
\providecommand{\hejg}{\vect{g}}
\providecommand{\hejGt}{\MATHIT{\tilde{\hejG}}}
\providecommand{\Delhej}{\mathUL{\Delta}{}{\hejG}}
\providecommand{\Delzh}{\mathUL{\Delta}{}{\zerJ,\hejG}}
\providecommand{\nstrathej}{\nstrat[g]}
\providecommand{\nlatfhej}{\nlatf[g]}



\providecommand{\zerJ}{\Mtx{J}}
\providecommand{\zerJc}{\cmpl{\zerJ}}
\providecommand{\zerJt}{\MATHIT{\tilde{\zerJ}}}
\providecommand{\nlatfzer}{\nlatf[j]}

% unified second moment stuff.
\providecommand{\pvsm}[1][{}]{\mathSUB{\Mtx{\Theta}}{#1}}
\providecommand{\svsm}[1][{}]{\mathSUB{\Mtx{\hat{\Theta}}}{#1}}
% constrained sample version
\providecommand{\svsmc}[1][{}]{\mathSUB{\Mtx{\tilde{\Theta}}}{#1}}


% combinatorics
\providecommand{\nchoosek}[2]{\MATHIT{{#1 \choose #2}}}

%vector of all ones, zeros
\providecommand{\vone}[1][]{\vect{1_{#1}}}
\providecommand{\vzero}{\vect{0}}

% a vector
\providecommand{\avect}{\vect{v}}

%leverage variable;
\providecommand{\levi}[1][]{\mathSUB{l}{#1}}

%generic combination of matrices
\providecommand{\ABA}[2]{#1 #2 #1}
\providecommand{\ABCBA}[3]{{#1 #2 #3 #2 #1}}
\providecommand{\trAB}[2]{\MATHIT{\tr{#1}#2}}

%gram matrix
\providecommand{\gram}[1]{\trAB{#1}{#1}}
%outer gram
\providecommand{\ogram}[1]{\MATHIT{#1 \tr{#1}}}
%quadratic form
\providecommand{\qform}[2]{\MATHIT{\tr{#2} #1 #2}}
\providecommand{\qiform}[2]{\qform{\minv{#1}}{#2}}
%quadratic outer form
\providecommand{\qoform}[2]{\MATHIT{#2 #1 \tr{#2}}}
\providecommand{\qoiform}[2]{\qoform{\minv{#1}}{#2}}
\providecommand{\crossp}[1]{\gram{\wrapNeParens{#1}}}

\providecommand{\qqform}[3]{\qform{\qform{#1}{#2}}{#3}}
\providecommand{\qqqform}[4]{\qform{\qform{\qform{#1}{#2}}{#3}}{#4}}

% these should have the same semantics WRT which arg comes first;
% basically it is Sigma then H (or J)
\providecommand{\wrapProj}[2]{\qiform{\wrapParens{\qoform{#1}{#2}}}{#2}}
\providecommand{\proj}[2]{\funcitUL{\mathcal{P}}{}{#2}{#1}}
\providecommand{\fdinvwrap}[2]{\funcitUL{f}{}{\mathcal{P}}{#2;#1}}

% a conditional on b
%\providecommand{\acondb}[2]{\MATHIT{#1\,\left|\,#2\right.}}
\providecommand{\acondb}[2]{\MATHIT{#1\left|\,#2\right.}}
\providecommand{\Econd}[2]{\E{\acondb{#1}{#2}}}
\providecommand{\Varcond}[2]{\VAR{\acondb{#1}{#2}}}

\providecommand{\evalat}[2]{\MATHIT{\left.#1\right|_{#2}}}

% sample statistics%FOLDUP
\providecommand{\smean}[1]{\MATHIT{\bar{#1}}}
\providecommand{\sstd}[1]{\mathSUB{s}{#1}}

%\providecommand{\svmean}[1]{\smean{\vect{#1}}}
%\providecommand{\svstd}[1]{\MtxUL{S}{}{#1}}

% raw symbols for stuff
%\providecommand{\prvSNR}[0]{\psi}
\providecommand{\prvSNR}[0]{\zeta}
\providecommand{\prvMean}[0]{\mu}
\providecommand{\prvStd}[0]{\sigma}
\providecommand{\prvPrec}[0]{\lambda}
\providecommand{\prvCov}[0]{\Sigma}
\providecommand{\prviCov}[0]{\Lambda}


% moments. what a totaly shit show.
% we have: raw, 'zero-centered' moments
% mean-centered moments (the first of which makes no sense)
% standardized moments
% cumulants.

% raw, zero-centered moment; mu' is standard here, btw
\providecommand{\prvRawMom}[0]{\alpha}
% centered moment.
\providecommand{\prvCenMom}[0]{\mu}
% standardized moment.
\providecommand{\prvStdMom}[0]{\gamma}
% cumulant
\providecommand{\prvCumulant}[0]{\kappa}
% standardized cumulant
\providecommand{\prvStdCumulant}[0]{\gamma}


%sample statistics.
%\providecommand{\smu}[1][]{\smean{\reti[#1]}}
%\providecommand{\ssig}[1][]{\MATHIT{s_{#1}}}
%\providecommand{\smu}[1][]{\smean{\reti[#1]}}
\providecommand{\smu}[1][]{\mathSUB{\hat{\prvMean}}{#1}}
\providecommand{\ssig}[1][]{\mathSUB{\hat{\prvStd}}{#1}}
\providecommand{\ssigsq}[1][]{\mathUL{\hat{\prvStd}}{2}{#1}}
\providecommand{\ssigu}[1][]{\mathSUB{\tilde{\prvStd}}{#1}}
\providecommand{\ssds}[1][]{\MATHIT{{s_N}_{#1}}}
\providecommand{\smom}[1][{}]{\mathSUB{\hat{\prvRawMom}}{#1}}

% risk free
\providecommand{\rfr}[1][0]{\mathSUB{r}{#1}}
% risk budget
\providecommand{\Rbuj}[1][]{\mathSUB{R}{#1}}
\providecommand{\rdrag}[1][0]{\MATHIT{\frac{\rfr[#1]}{\Rbuj}}}



% sample sharpe ratio
\providecommand{\ssrUL}[2]{\mathUL{\hat{\prvSNR}}{#1}{#2}}
%\providecommand{\ssrUL}[2]{\mathUL{r}{#1}{#2}}
\providecommand{\ssr}[1][]{\ssrUL{}{#1}}
\providecommand{\ssrsq}[1][]{\ssrUL{2}{#1}}
\providecommand{\ssropt}{\ssr[*]}
\providecommand{\ssroptG}[1]{\ssr[*,#1]}
\providecommand{\ssrsqopt}{\ssrsq[*]}
\providecommand{\ssrsqoptG}[1]{\ssrsq[*,#1]}

\providecommand{\ssrg}[1][g]{\ssr[{#1}]}

\providecommand{\svsigma}[1][]{\vectUL{\hat{\prvStd}}{}{#1}}
\providecommand{\svsr}[1][]{\vectUL{\hat{\prvSNR}}{}{#1}}

% using the unbiased sigma: 
\providecommand{\ussrUL}[2]{\mathUL{\tilde{\prvSNR}}{#1}{#2}}
\providecommand{\ussr}[1][]{\ussrUL{}{#1}}
\providecommand{\ussrsq}[1][]{\ussrUL{2}{#1}}
\providecommand{\ussropt}{\ussr[*]}
\providecommand{\ussrsqopt}{\ussrsq[*]}


\providecommand{\svmu}[1][]{\vectUL{\hat{\prvMean}}{}{#1}}
\providecommand{\svsig}[1][]{\MtxUL{\hat{\prvCov}}{}{#1}}
\providecommand{\svvar}[1][]{\MtxUL{\hat{\Omega}}{}{#1}}
\providecommand{\svmom}[1][{}]{\vectUL{\hat{\prvRawMom}}{}{#1}}

\providecommand{\usvsig}[1][]{\MtxUL{\tilde{\prvCov}}{}{#1}}

\providecommand{\sfvmu}[1][]{\smean{\vreti[#1]^{*}}}
\providecommand{\sfse}{\kappa}

%unbiased estimator of Sharpe ratio
\providecommand{\susr}[1][]{\MATHIT{\tilde{\prvSNR}_{#1}}}
\providecommand{\sgossz}[1][]{\mathSUB{z}{#1}}
%UNFOLD
%population parameters%FOLDUP
\providecommand{\pmu}[1][]{\mathSUB{\prvMean}{#1}}
\providecommand{\psig}[1][]{\mathSUB{\prvStd}{#1}}
\providecommand{\pprec}[1][]{\mathSUB{\prvPrec}{#1}}
\providecommand{\psigma}[1][]{\mathSUB{\prvStd}{#1}}
\providecommand{\psigsq}[1][]{\mathUL{\prvStd}{2}{#1}}
\providecommand{\pmom}[1][{}]{\mathSUB{\prvRawMom}{#1}}
\providecommand{\pmomx}[2][{}]{\funcit{\pmom[#1]}{#2}}
\providecommand{\pmomssr}[1][{}]{\pmomx[#1]{\ssr}}

\providecommand{\psrUL}[2]{\mathUL{\prvSNR}{#1}{#2}}

\providecommand{\psr}[1][]{\psrUL{}{#1}}
\providecommand{\psnr}[1][]{\psrUL{}{#1}}
\providecommand{\psrsq}[1][]{\psrUL{2}{#1}}
\providecommand{\psnrsq}[1][]{\psrUL{2}{#1}}
\providecommand{\psnrg}[1][g]{\psnr[{#1}]}
\providecommand{\psnrh}[1][h]{\psnr[{#1}]}

% optimum SNR in the population
\providecommand{\psnropt}{\psnr[*]}
\providecommand{\psnroptG}[1]{\psnr[*,#1]}
\providecommand{\psnrpopt}{\psnr[*]}
\providecommand{\psnrsqopt}{\psnrsq[*]}
\providecommand{\psnrsqoptG}[1]{\psnrsq[*,#1]}
\providecommand{\psnrsqpopt}{\psnrsq[*]}

% population SNR of the *sample optimal* portfolio. bleah
\providecommand{\psnrsopt}{\psnr[s,*]}

% delta SNR squared in the population
\providecommand{\Delpsnrsqopt}[1]{\mathUL{\Delta}{}{#1}\psnrsqopt}

%population vector mean and covariance
\providecommand{\pvmu}[1][]{\vectUL{\prvMean}{}{#1}}
\providecommand{\pvmom}[1][{}]{\vectUL{\prvRawMom}{}{#1}}
\providecommand{\pvsig}[1][]{\MtxUL{\prvCov}{}{#1}}
\providecommand{\pvschol}[1][]{\MtxUL{C}{}{#1}}

\providecommand{\pvsigma}[1][]{\vectUL{\prvStd}{}{#1}}
\providecommand{\pvsnr}[1][]{\vectUL{\prvSNR}{}{#1}}
\providecommand{\pvvar}[1][]{\MtxUL{\Omega}{}{#1}}
%UNFOLD

% haircut
\providecommand{\hcut}[1][]{\mathSUB{h}{#1}}
\providecommand{\Pmat}{\Mtx{P}}

\providecommand{\smahalo}[1][]{\mathSUB{\hat{d}}{#1}}


% positively proportional to
\providecommand{\ppropto}{\mathSUB{\propto}{+}}

% MGLH
\providecommand{\MGLHA}[1][]{\MtxUL{A}{}{#1}}
\providecommand{\MGLHC}[1][]{\MtxUL{C}{}{#1}}
\providecommand{\MGLHT}[1][]{\MtxUL{\Theta}{}{#1}}
\providecommand{\MGLHrank}{\MATHIT{r}}
\providecommand{\MGLHa}{\MATHIT{a}}
\providecommand{\MGLHc}{\MATHIT{c}}

\providecommand{\MGLHH}[1][]{\MtxUL{H}{}{#1}}
\providecommand{\MGLHE}[1][]{\MtxUL{E}{}{#1}}
\providecommand{\pMGLHH}[1][]{\MtxUL{H}{}{#1}}
\providecommand{\pMGLHE}[1][]{\MtxUL{E}{}{#1}}
\providecommand{\sMGLHH}[1][]{\MtxUL{\hat{H}}{}{#1}}
\providecommand{\sMGLHE}[1][]{\MtxUL{\hat{E}}{}{#1}}

\providecommand{\mglhM}{\Mtx{M}}

\providecommand{\eye}[1][]{\MtxUL{I}{}{#1}}
\providecommand{\mzero}[1][]{\MtxUL{0}{}{#1}}


%CDF and quantile%FOLDUP

% make a letter into a distribution 'law'
\providecommand{\makelaw}[2]{\MATHIT{#1\wrapNeParens{#2}}}
\providecommand{\FOOpdf}[3]{\funcit{\mathSUB{f}{#1}}{#2;#3}}
\providecommand{\FOOlik}[3]{\funcit{\mathSUB{\mathcal{L}}{#1}}{\condtwo{#3}{#2}}}
\providecommand{\FOOcdf}[3]{\funcit{\mathSUB{F}{#1}}{#2;#3}}
\providecommand{\FOOqnt}[3]{\funcit{\mathSUB{#1}{#2}}{#3}}

% normal
\providecommand{\normpdf}[2]{\FOOpdf{\mathcal{N}}{#1}{#2}}
\providecommand{\normcdf}[2]{\FOOcdf{\mathcal{N}}{#1}{#2}}
\providecommand{\normqnt}[2]{\FOOqnt{\mathcal{N}}{#1}{#2}}
\providecommand{\normlaw}[1]{\makelaw{\mathcal{N}}{#1}}

% uniform
\providecommand{\unipdf}[2]{\FOOpdf{\mathcal{U}}{#1}{#2}}
\providecommand{\unicdf}[2]{\FOOcdf{\mathcal{U}}{#1}{#2}}
\providecommand{\uniqnt}[2]{\FOOqnt{\mathcal{U}}{#1}{#2}}
\providecommand{\unilaw}[1]{\makelaw{\mathcal{U}}{#1}}

% AKA:
%density, distribution, quantile of normal distribution
\providecommand{\dnorm}[1][x]{\funcit{\phi}{#1}}
\providecommand{\pnorm}[1][x]{\funcit{\Phi}{#1}}
\providecommand{\pinorm}[1][x]{\funcit{\mathUL{\Phi}{-1}{}}{#1}}
\providecommand{\qnorm}[1]{\mathSUB{z}{#1}}

\providecommand{\erf}[1]{\funcit{\mathUL{erf}{}{}}{#1}}
\providecommand{\erfinv}[1]{\funcit{\mathUL{erf}{-1}{}}{#1}}

%t
\providecommand{\tcdf}[2]{\FOOcdf{t}{#1}{#2}}
\providecommand{\tqnt}[2]{\FOOqnt{t}{#1}{#2}}
\providecommand{\tlaw}[1]{\makelaw{t}{#1}}

% noncentral t
% changing the cdf, pdf, qnt to be:
% *, nct param, df
\providecommand{\nctcdf}[3]{\FOOcdf{t}{#1}{#2,#3}}
\providecommand{\nctpdf}[3]{\FOOpdf{t}{#1}{#2,#3}}
\providecommand{\nctlik}[3]{\FOOlik{t}{#2,#3}{#1}}
\providecommand{\nctqnt}[3]{\FOOqnt{t}{#1}{#2,#3}}
\providecommand{\nctlaw}[1]{\makelaw{t}{#1}}
\providecommand{\nctvar}[1][]{\mathSUB{t}{#1}}
\providecommand{\nctdf}[1][]{\mathSUB{\nu}{#1}}

% SR
% the order is x, non-centrality, sample size
\providecommand{\srpdf}[3]{\FOOpdf{SR}{#1}{#2,#3}}
\providecommand{\srlik}[3]{\FOOlik{SR}{#2,#3}{#1}}
% the order is x, non-centrality, sample size
\providecommand{\srcdf}[3]{\FOOcdf{SR}{#1}{#2,#3}}
% the order is x, non-centrality, sample size
\providecommand{\srqnt}[3]{\FOOqnt{SR}{#1}{#2,#3}}
\providecommand{\srlaw}[1]{\makelaw{SR}{#1}}

% cdf of F, and non-central F:
\providecommand{\fcdf}[2]{\FOOcdf{f}{#1}{#2}}
\providecommand{\ncfcdf}[3]{\FOOcdf{f}{#1}{#2,#3}}
% quantiles (inverse cdf) of F, noncentral F
\providecommand{\fqnt}[2]{\FOOqnt{f}{#1}{#2}}
\providecommand{\ncfqnt}[3]{\FOOqnt{f}{#1}{#2,#3}}
% as a 'law'
\providecommand{\flaw}[1]{\makelaw{F}{#1}}
\providecommand{\ncflaw}[1]{\makelaw{F}{#1}}

%chisq
\providecommand{\prvchisq}{\chi^2}
\providecommand{\chisqpdf}[2]{\FOOpdf{\prvchisq}{#1}{#2}}
\providecommand{\chisqcdf}[2]{\FOOcdf{\prvchisq}{#1}{#2}}
\providecommand{\chisqqnt}[2]{\FOOqnt{\prvchisq}{#1}{#2}}
\providecommand{\chisqlaw}[1]{\makelaw{\prvchisq}{#1}}

% chi
\providecommand{\prvchi}{\chi}
\providecommand{\chipdf}[2]{\FOOpdf{\prvchi}{#1}{#2}}

% gamma distribution
\providecommand{\prvgamma}{\Gamma}
\providecommand{\gammacdf}[2]{\FOOcdf{\prvgamma}{#1}{#2}}
\providecommand{\gammaqnt}[2]{\FOOqnt{\prvgamma}{#1}{#2}}
\providecommand{\gammalaw}[1]{\makelaw{\prvgamma}{#1}}

% inverse gamma distribution
\providecommand{\igammacdf}[2]{\FOOcdf{\minv{\prvgamma}}{#1}{#2}}
\providecommand{\igammaqnt}[2]{\FOOqnt{\minv{\prvgamma}}{#1}{#2}}
\providecommand{\igammalaw}[1]{\makelaw{\minv{\prvgamma}}{#1}}

%beta
\providecommand{\prvbetacdf}[2]{\FOOcdf{\beta}{#1}{#2}}
\providecommand{\prvbetapdf}[2]{\FOOpdf{\beta}{#1}{#2}}
\providecommand{\prvbetaqnt}[2]{\FOOqnt{\beta}{#1}{#2}}
\providecommand{\prvbetalaw}[1]{\makelaw{\mathcal{B}}{#1}}

\providecommand{\betacdf}[3]{\prvbetacdf{#1}{#2,#3}}
\providecommand{\betapdf}[3]{\prvbetapdf{#1}{#2,#3}}
\providecommand{\betaqnt}[3]{\prvbetaqnt{#1}{#2,#3}}
\providecommand{\betalaw}[2]{\prvbetalaw{#1,#2}}

%non-central beta
\providecommand{\nctbetacdf}[4]{\prvbetacdf{#1}{#2,#3,#4}}
\providecommand{\nctbetapdf}[4]{\prvbetapdf{#1}{#2,#3,#4}}
\providecommand{\nctbetaqnt}[4]{\prvbetaqnt{#1}{#2,#3,#4}}
\providecommand{\nctbetalaw}[3]{\prvbetalaw{#1,#2,#3}}

% lambdaprime
\providecommand{\lampcdf}[3]{\FOOcdf{\lambda'}{#1}{#2,#3}}
\providecommand{\lamppdf}[3]{\FOOpdf{\lambda'}{#1}{#2,#3}}
\providecommand{\lampqnt}[3]{\FOOqnt{\lambda'}{#1}{#2,#3}}
\providecommand{\lamplaw}[1]{\makelaw{\lambda'}{#1}}
\providecommand{\lampdf}[1][]{\mathSUB{\nu}{#1}}

% upsilon
\providecommand{\upscdf}[3]{\FOOcdf{\Upsilon}{#1}{#2,#3}}
\providecommand{\upspdf}[3]{\FOOpdf{\Upsilon}{#1}{#2,#3}}
\providecommand{\upsqnt}[3]{\FOOqnt{\Upsilon}{#1}{#2,#3}}
\providecommand{\upslaw}[1]{\makelaw{\Upsilon}{#1}}

% wishart
\providecommand{\wishlaw}[1]{\makelaw{\mathcal{W}}{#1}}

% instantiations of laws with default parameters filled in?
% overkill?
\providecommand{\normdist}[1][0,1]{\normlaw{#1}}
\providecommand{\tdist}[1][n]{\MATHIT{\mathcal{t}\wrapNeParens{#1}}}
\providecommand{\hotdist}[1][n,p]{\MATHIT{\mathcal{T^2}\wrapNeParens{#1}}}

%UNFOLD

%statistical whatsits%FOLDUP
\providecommand{\typeI}{\MATHIT{\alpha}}
\providecommand{\typeII}{\MATHIT{\beta}}
\providecommand{\powr}{\MATHIT{1 - \typeII}}
\providecommand{\irate}{\MATHIT{c_0}}
%UNFOLD

% landau notation:
\providecommand{\bigo}[1]{\MATHIT{\mathcal{O}\wrapParens{#1}}}
\providecommand{\bigOmega}[1]{\MATHIT{\Omega\wrapParens{#1}}}
\providecommand{\half}[1][1]{\MATHIT{\frac{#1}{2}}}
\providecommand{\halff}[1][1]{\fracc{#1}{2}}

% time commands; change the default!?
\providecommand{\yrto}[1]{\mathSUP{\mbox{yr}}{#1}}
\providecommand{\moto}[1]{\mathSUP{\mbox{mo.}}{#1}}
\providecommand{\qto}[1]{\mathSUP{\mbox{Q}}{#1}}
\providecommand{\dayto}[1]{\mathSUP{\mbox{day}}{#1}}
\providecommand{\wkto}[1]{\mathSUP{\mbox{wk.}}{#1}}

\providecommand{\yrtomhalf}{\yrto{-\halff}}

%linear regression: population and sample%FOLDUP
\providecommand{\pregco}[1][]{\mathSUB{\beta}{#1}}
\providecommand{\pregvec}[1][]{\vectUL{\beta}{}{#1}}
\providecommand{\perr}[1][]{\mathSUB{\epsilon}{#1}}
\providecommand{\sregco}[1][]{\mathSUB{\hat{\beta}}{#1}}
\providecommand{\sregvec}[1][]{\vectUL{\hat{\beta}}{}{#1}}
\providecommand{\serr}[1][]{\mathSUB{\hat{\epsilon}}{#1}}

% multivariate regression;
\providecommand{\prvRegsym}{B}
\providecommand{\pRegco}[1][]{\MtxUL{\prvRegsym}{}{#1}}
\providecommand{\sRegco}[1][]{\MtxUL{\hat{\prvRegsym}}{}{#1}}

\providecommand{\pErr}[1][]{\mathSUB{E}{#1}}
\providecommand{\pErrt}[1][t]{\pErr[#1]}
\providecommand{\sErr}[1][]{\mathSUB{\hat{E}}{#1}}

%UNFOLD
%
%'contrast' vector and target
\providecommand{\convec}[1][]{\vect{\mathSUB{v}{#1}}}
\providecommand{\contar}[1][]{\MATHIT{c}}


%noncentrality parameters
\providecommand{\nctp}[1][]{\mathSUB{\delta}{#1}}
\providecommand{\ncfp}[1][]{\mathSUB{\delta}{#1}}

\providecommand{\tstat}[1][]{\mathSUB{t}{#1}}
\providecommand{\fstat}[1][]{\mathSUB{f}{#1}}
\providecommand{\Fstat}[1][]{\mathSUB{F}{#1}}
\providecommand{\Tstat}[1][]{\mathUL{T}{2}{#1}}
%\providecommand{\tbias}[1][\ssiz]{\MATHIT{\eta_{#1}}}
\providecommand{\tbias}[1][\ssiz]{\mathSUB{d}{#1}}

% delta hotelling cf. \Delpsnrsqopt
\providecommand{\DTstat}[1][]{\MATHIT{\Delta\Tstat[#1]}}

\providecommand{\median}[1]{\funcit{\mbox{median}}{#1}}
\providecommand{\Pr}[1]{\funcit{\mbox{\large P}}{#1}}
%\providecommand{\E}[1]{\MATHIT{\mbox{\large E}\wrapNeBracks{#1}}}
%%\providecommand{\E}[1]{\ensuremath{\operatorname{E}\left[#1\right]}}
\providecommand{\E}[1]{\MATHIT{\operatorname{E}\wrapNeBracks{#1}}}
\providecommand{\Eof}[2][{}]{\MATHIT{\mathSUB{\operatorname{E}}{#1}\wrapNeBracks{#2}}}
\providecommand{\VAR}[1]{\funcit{\operatorname{Var}}{#1}}
\providecommand{\GAM}[1]{\MATHIT{\Gamma\wrapNeParens{#1}}}
\providecommand{\skewness}[1]{\funcit{\mbox{skew}}{#1}}
\providecommand{\exkurt}[1]{\funcit{\mbox{ex\,kurtosis}}{#1}}

% incomplete gamma
\providecommand{\Incbeta}[3]{\funcit{\mathUL{\operatorname{I}}{}{#1}}{#2, #3}}

% incomplete gamma
\providecommand{\Incgamma}[2]{\GAM{#1,#2}}
\providecommand{\GAMrat}[2]{\frac{\GAM{#1}}{\GAM{#2}}}
\providecommand{\GAMhalfrat}[2]{\GAMrat{\half[{#1}]}{\half[{#2}]}}

\providecommand{\HyperF}[3]{\funcit{{}_{#1}F_{#2}}{#3}}
\providecommand{\semifact}[1]{\mathit{#1 !!}}


% hypotheses
\providecommand{\Hyp}[1][0]{\mathSUB{H}{#1}}

% population centered moment, adjusted?
\providecommand{\pcmom}[1][{}]{\mathSUB{\prvCenMom}{#1}}
\providecommand{\pvcmom}[1][{}]{\vectUL{\prvCenMom}{}{#1}}
\providecommand{\scmom}[1][{}]{\mathSUB{\hat{\prvCenMom}}{#1}}
\providecommand{\svcmom}[1][{}]{\vectUL{\hat{\prvCenMom}}{}{#1}}

% crap, I already used pcmom to mean something;
% use pmmom: 'm' for middle. but really a 'c'-score
% c-score of moments: E((x-mu)^p)
\providecommand{\pmmom}[1][{}]{\mathSUB{\prvCenMom}{#1}}
\providecommand{\pvmmom}[1][{}]{\vectUL{\prvCenMom}{}{#1}}
\providecommand{\smmom}[1][{}]{\mathSUB{\hat{\prvCenMom}}{#1}}
\providecommand{\svmmom}[1][{}]{\vectUL{\hat{\prvCenMom}}{}{#1}}

% z-score of moments: E((x-mu)^p) / sigma^p
\providecommand{\pzmom}[1][{}]{\mathSUB{\prvStdMom}{#1}}
\providecommand{\pvzmom}[1][{}]{\vectUL{\prvStdMom}{}{#1}}
\providecommand{\szmom}[1][{}]{\mathSUB{\hat{\prvStdMom}}{#1}}
\providecommand{\svzmom}[1][{}]{\vectUL{\hat{\prvStdMom}}{}{#1}}

% standardized cumulants
\providecommand{\pzkuml}[1][{}]{\mathSUB{\prvStdCumulant}{#1}}
\providecommand{\pvzkuml}[1][{}]{\vectUL{\prvStdCumulant}{}{#1}}
\providecommand{\szkuml}[1][{}]{\mathSUB{\hat{\prvStdCumulant}}{#1}}
\providecommand{\svzkuml}[1][{}]{\vectUL{\hat{\prvStdCumulant}}{}{#1}}

% cumulants
\providecommand{\pkuml}[1][{}]{\mathSUB{\prvCumulant}{#1}}
\providecommand{\pvkuml}[1][{}]{\vectUL{\prvCumulant}{}{#1}}
\providecommand{\skuml}[1][{}]{\mathSUB{\hat{\prvCumulant}}{#1}}
\providecommand{\svkuml}[1][{}]{\vectUL{\hat{\prvCumulant}}{}{#1}}

\providecommand{\sacor}[1][]{\mathSUB{\hat{\nu}}{#1}}
\providecommand{\pacor}[1][]{\mathSUB{\nu}{#1}}
\providecommand{\corcor}{\MATHIT{d}}

% sample size, # of strategies, number of 'latent factors', addon factors?
% bleah. the semantics of this are hosed.

% sample size; for scalar case
\providecommand{\ssiz}[1][]{\mathSUB{n}{#1}}
% number of 'strategies'; or returns. assets.
\providecommand{\nstrat}[1][]{\mathSUB{k}{#1}}

% number of observations. uhoh.
\providecommand{\nobs}[1][]{\mathSUB{n}{#1}}
% number of 'signals'. let's stick with that.
\providecommand{\nfac}[1][]{\mathSUB{f}{#1}}
\providecommand{\nvol}[1][]{\mathSUB{q}{#1}}

% this should probably change to \nstrat.
\providecommand{\nlatf}[1][]{\mathSUB{p}{#1}}
\providecommand{\nlatfmo}[1][]{\mathSUB{q}{#1}}
\providecommand{\nlatftot}{\MATHIT{\nlatf+\nlatfmo}}

% # of attribution factors
% this should probably change to \nfac.
\providecommand{\nattf}[1][]{\mathSUB{l}{#1}}

\providecommand{\df}[1][]{\mathSUB{v}{#1}}

%t-power law numerator constant.
%2FIX: this is a throwaway constant.
\providecommand{\tpowc}[1][]{\mathSUB{k}{#1}}

%aspect ratio
\providecommand{\arat}[1][a]{\mathSUB{c}{#1}}

% portfolios
\providecommand{\prvPortfolio}[0]{\nu}
% portfolio coefficient, or passthrough
\providecommand{\prvPortfolioPass}[0]{N}

\providecommand{\portw}[1][{}]{\vectUL{w}{}{#1}}

\providecommand{\pportw}[1][{}]{\vectUL{\prvPortfolio}{}{#1}}
\providecommand{\sportw}[1][{}]{\vectUL{\hat{\prvPortfolio}}{}{#1}}
\providecommand{\sportwoptFoo}[1]{\sportw[{#1}*]}
\providecommand{\pportwoptFoo}[1]{\pportw[{#1}*]}
\providecommand{\sportwopt}{\sportwoptFoo{}}
\providecommand{\pportwopt}{\pportwoptFoo{}}
\providecommand{\sportwoptR}{\sportwoptFoo{\Rbuj,}}
\providecommand{\pportwoptR}{\pportwoptFoo{\Rbuj,}}

\providecommand{\sportwoptG}[1]{\sportw[*,#1]}
\providecommand{\pportwoptG}[1]{\pportw[*,#1]}

\providecommand{\sportv}[1][{}]{\vectUL{\hat{v}}{}{#1}}
\providecommand{\sportvopt}{\sportv[*]}

\providecommand{\pportx}[1][{}]{\vectUL{\xi}{}{#1}}
\providecommand{\sportx}[1][{}]{\vectUL{\hat{\xi}}{}{#1}}

% ouch! should these use PASSTHROUGH instead? bleah.
%\providecommand{\sportW}[1][{}]{\vectUL{\hat{\uppercase{\prvPortfolio}}}{}{#1}}
\providecommand{\pportW}[1][{}]{\MtxUL{\prvPortfolioPass}{}{#1}}
\providecommand{\sportW}[1][{}]{\MtxUL{\hat{\prvPortfolioPass}}{}{#1}}
\providecommand{\sportWoptFoo}[1]{\sportW[{#1}*]}
\providecommand{\pportWoptFoo}[1]{\pportW[{#1}*]}
\providecommand{\sportWopt}{\sportWoptFoo{}}
\providecommand{\pportWopt}{\pportWoptFoo{}}

% basis vectors
\providecommand{\basev}[1][]{\vectUL{e}{}{#1}}

% functions
\providecommand{\farcsin}[1]{\funcit{\arcsin}{#1}}
\providecommand{\farctan}[1]{\funcit{\arctan}{#1}}

\providecommand{\ftan}[2][{}]{\funcitUL{\tan}{#1}{}{#2}}
\providecommand{\fsin}[2][{}]{\funcitUL{\sin}{#1}{}{#2}}
\providecommand{\fcos}[2][{}]{\funcitUL{\cos}{#1}{}{#2}}
\providecommand{\fcot}[2][{}]{\funcitUL{\cot}{#1}{}{#2}}

% named functions
\providecommand{\fntas}[1]{\funcit{\mathSUB{f}{\mbox{tas}}}{#1}}
\providecommand{\fnbtas}[1]{\funcit{\mathSUB{\bar{f}}{\mbox{tas}}}{#1}}
\providecommand{\fngom}[1]{\funcit{\mathSUB{f}{\mbox{log}}}{#1}}
\providecommand{\fnmog}[1]{\funcit{\mathSUB{f}{\mbox{exp}}}{#1}}

\providecommand{\flog}[1]{\funcit{\operatorname{log}}{#1}}


%UNFOLD
%

\providecommand{\stratrc}[1][]{\mathSUB{\theta}{#1}}

%\providecommand{\rcode}[1]{\texttt{\verb{#1}}}
\providecommand{\Rcode}[1]{{\texttt{#1}}}
% stolen from synapter vignette:
\providecommand{\Rfunction}[1]{{\texttt{#1}}}
\providecommand{\Robject}[1]{{\texttt{#1}}}
\providecommand{\Rpackage}[1]{{\mbox{\normalfont\textsf{#1}}}}
% http://tex.stackexchange.com/a/105562/2530
%\providecommand{\Rlang}{\textsf{R}\xspace}
\providecommand{\Rlang}{\textbf{\textsf{R}}\xspace}
\providecommand{\email}[1]{\href{mailto:#1}{\normalfont\texttt{#1}}}

\providecommand{\StockTicker}[1]{{\texttt{#1}}}
%UNFOLD

\providecommand{\DIFFERENTIAL}[1]{\mathrm{d}{#1}}
\providecommand{\PARTIAL}[1]{\partial {#1}}

\providecommand{\dx}[1][x]{\MATHIT{\,\DIFFERENTIAL{#1}}}
\providecommand{\px}[1][x]{\MATHIT{\PARTIAL{#1}}}
%deprecated:
\providecommand{\dpx}[1][x]{\MATHIT{\PARTIAL{#1}}}

%with differentials d^1 2 / (d 3)^1
\providecommand{\dbyd}[3][{}]{\MATHIT{\frac{\neUL{\DIFFERENTIAL{}}{#1}{} {#2}}{\neUL{\DIFFERENTIAL{#3}}{#1}{}}}}

% conditional on
\providecommand{\condl}[1]{\MATHIT{\left|{#1}\right.}}
\providecommand{\condtwo}[2]{\MATHIT{{#1}\condl{#2}}}

\providecommand{\NORMM}[2]{\ensuremath{\left\|{#2}\right\|_{#1}}\xspace}
\providecommand{\norm}[2][2]{\NORMM{#1}{#2}}



% R packages.
% see: https://stat.ethz.ch/pipermail/r-help/2007-November/144810.html
\newcommand{\pkg}[1]{{\normalfont\fontseries{b}\selectfont #1}}
\let\proglang=\textsf
\let\code=\texttt
\newcommand{\CRANpkg}[1]{\href{https://CRAN.R-project.org/package=#1}{\pkg{#1}}}

%for vim modeline: (do not edit)
% vim:fdm=marker:fmr=FOLDUP,UNFOLD:cms=%%s:syn=latex:ft=latex

%UNFOLD

\providecommand{\sideWarning}[1][0.5]{\marginpar{\hfill\includegraphics[width=#1\marginparwidth]{warning}}}

% knitr setup%FOLDUP

<<'preamble', include=FALSE, warning=FALSE, message=FALSE>>=
library(knitr)

# set the knitr options ... for everyone!
# if you unset this, then vignette build bonks. oh, joy.
#opts_knit$set(progress=TRUE)
opts_knit$set(eval.after='fig.cap')
# for a package vignette, you do want to echo.
# opts_chunk$set(echo=FALSE,warning=FALSE,message=FALSE)
opts_chunk$set(warning=FALSE,message=FALSE)
#opts_chunk$set(results="asis")
opts_chunk$set(cache=TRUE,cache.path="cache/rfin2016_")

#opts_chunk$set(fig.path="figure/",dev=c("pdf","cairo_ps"))
opts_chunk$set(fig.path="figure/rfin2016_",dev=c("pdf"))
opts_chunk$set(fig.width=5,fig.height=4,dpi=64)

# doing this means that png files are made of figures;
# the savings is small, and it looks like shit:
#opts_chunk$set(fig.path="figure/",dev=c("png","pdf","cairo_ps"))
#opts_chunk$set(fig.width=4,fig.height=4)
# for figures? this is sweave-specific?
#opts_knit$set(eps=TRUE)

# this would be for figures:
#opts_chunk$set(out.width='.8\\textwidth')
# for text wrapping:
options(width=64,digits=2)
opts_chunk$set(size="small")
opts_chunk$set(tidy=TRUE,tidy.opts=list(width.cutoff=50,keep.blank.line=TRUE))

compile.time <- Sys.time()

# from the environment

# only recompute if FORCE_RECOMPUTE=True w/out case match.
FORCE_RECOMPUTE <- 
	(toupper(Sys.getenv('FORCE_RECOMPUTE',unset='False')) == "TRUE")

# compiler flags!

# not used yet
LONG.FORM <- FALSE

mc.resolution <- ifelse(LONG.FORM,1000,200)
mc.resolution <- max(mc.resolution,100)

library(SharpeR)
library(madness)
library(dplyr)
library(lubridate)


ipdf <- data.frame(installed.packages(),stringsAsFactors=FALSE) 
madness_version <- ipdf[ipdf$Package=='madness','Version']
@
%UNFOLD
    
% SYMPY preamble%FOLDUP
    
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		\usepackage{fancyvrb} % verbatim replacement that allows latex
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    % Exact colors from NB
    \definecolor{incolor}{rgb}{0.0, 0.0, 0.5}
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    % Prevent overflowing lines due to hard-to-break entities
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    % Setup hyperref package
    \hypersetup{
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    %UNFOLD
%UNFOLD

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% commands specific to this paper:%FOLDUP
\newcommand{\madnesspack}{\CRANpkg{madness}\xspace}
\newcommand{\madness}{\Robject{madness}\xspace}
\newcommand{\madobj}{\Robject{madness}\xspace}

\newcommand{\feetv}[1][]{\vectUL{f}{}{#1}}
\newcommand{\feetm}[1][]{\mtxUL{F}{}{#1}}
%UNFOLD

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% document incantations%FOLDUP
\begin{document}

%\title{Portfolio \txtCR bounds}
%\subtitle{Why bad things happen to good quants}
\title{Madness: a package for Multivariate Automatic Differentiation}
\author{Steven E. Pav \thanks{\email{shabbychef@gmail.com}}}
%\date{\today, \currenttime}

\maketitle
%UNFOLD

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{abstract}%FOLDUP
The \madnesspack package provides a class for automatic differentiation of 
`multivariate' operations via forward accumulation. By `multivariate,'
we mean the class computes the derivative of a vector or matrix or
multidimensional array (or scalar) with respect to a scalar, vector, matrix,
or multidimensional array. The primary intended use of this class is to support
the multivariate delta method for performing inference on multidimensional
quantities. Another use case is the automatic computation of the gradient
in parameter optimization (\eg in the computation of an MLE). Examples
of the use of this package are given in the realm of quantitative
finance.
\end{abstract}%UNFOLD

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Introduction}%FOLDUP

The \madnesspack package \cite{madnessmadness-Manual} provides the ability to
automatically compute and accumulate the derivative of numerical quantities
on concrete data via 
forward accumulation. \cite{rall1981automatic,griewank2008evaluating} It can
compute the derivatives of multivariate functions--those producing multivariate
output--with respect to a multivarite independent variable. While the
derivatives are essentially computed symbolically, they are applied immediately
to concrete data. Unlike previous attempts at automatic differentiation in
\Rlang, \madness takes a `high level' approach.  %\cite{tada_package,radx_package}
That is, rather than provide methods for computing the derivatives of a few
basic operators like sum, product, exponent and some trigonometrics, which
would be applied at the lowest level of more complicated functions, the 
eponymous \madobj class supports functions like the Cholesky factor, 
the matrix square root, matrix inversion, computing eigenvalues
and so on. Because many of these linear algebra operations are typically computed at 
the lowest level in C code, a `low level' approach which infects basic
operations like sum and product could not be easily applied.

The target application is the multivariate delta method. Informally, the
multivariate delta method claims that a function commutes with a consistent
estimator of some population quantity, while the covariance gets `wrapped' with
the derivative of the applied function. That is, if $\beta$ is some population
quantity, and $B$ is some consistent estimator of $\beta$ with
$$
\sqrt{\ssiz}\wrapParens{B - \beta} \xrightarrow{D} \normlaw{\vzero,\Omega},
$$
based on $\ssiz$ independent observations, and $\funcit{f}{\cdot}$ is some function
which is continuous and non-zero at $\beta$, then
$$
\sqrt{\ssiz}\wrapParens{\funcit{f}{B} - \funcit{f}{\beta}} \xrightarrow{D}
\normlaw{\vzero,\evalat{\qform{\Omega}{\dbyd{\funcit{f}{x}}{x}}}{x=\beta}}.
$$
Practically speaking, this means that if you can compute a consistent estimator
(\eg by taking a simple mean and relying on the central limit theorem),
\emph{and you can compute derivatives}, you can estimate the
variance-covariance of some really weird estimators. The \madness package
aims to compute those derivatives for you.
% need ref for multivariate delta method.

\emph{Nota bene} The \madnesspack package is in a state of flux. This 
document describes version \Sexpr{madness_version} of the package, but
should be applicable for more recent versions.

\nocite{magnus1999matrix}

%UNFOLD

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Basic usage}%FOLDUP

The \madobj class is an \Robject{S4} class with the following slots:
\begin{compactitem}
\item The dependent variable, \Robject{val}, a multidimensional numeric.
\item The derivative of the dependent variable with respect to some implicit
independent variable, \Robject{dvdx}, a matrix. The matrix is stored in 
`numerator layout,' where the derivative of a scalar with respect to a 
vector is a \emph{row} vector. This is inconsistent with traditional 
representation of a gradient as a column, but notationally more convenient.
\item Optionally the `tag' of the value, \Robject{vtag} is stored. This
keeps track of the operations applied to the value, and is useful for
debugging.
\item Optionally the `tag' of the independent variable, \Robject{xtag} is
stored. While this tag is optional, it is important to note that two 
\madobj objects with \emph{different} \Robject{xtag} values cannot be used 
in the same computation. For example, attempting to add them results in an
error, since they are considered to track the derivatives with respect to
different independent variables.
\item Optionally the variance-covariance of the independent variable is stored
in \Robject{varx}. This is convenient for the multivariate delta method. One
can call the \Rfunction{vcov} method on a \madobj object with a non-null
\Robject{varx}, and the delta method will be applied.
\end{compactitem}

\subsection{Object construction}

One can get data into a \madobj object by calling the \Rfunction{madness}
function. The derivative \Robject{dvdx} will default to the identity matrix.
That is, the constructor assumes that the dependent variable \emph{is} the
independent variable. The constructor also guesses the tags for the independent
and dependent variables by the name of the input variable. The \Rfunction{show}
method shows a \madobj object, just showing the head of the value and
derivative:

<<'demo_1',echo=TRUE,cache=TRUE>>=
require(madness)
set.seed(1234)
X_NAMED <- array(rnorm(3),dim=c(3,1))
Xmad <- madness(X_NAMED)
show(Xmad)
@

One can get the value, the derivative, tags, and so on with eponymous getter
methods, \Rfunction{val}, \Rfunction{dvdx}, \Rfunction{xtag}, \Rfunction{vtag},
\Rfunction{varx}:

<<'demo_1more',echo=TRUE,cache=TRUE>>=
show(val(Xmad))
show(dvdx(Xmad))
@

One can also construct a \madobj object via the \Rfunction{as.madness} function
which calls the \Rfunction{coef} method and the \Rfunction{vcov} method on the
input. So, for example, one can easily convert an object of class
\Rfunction{lm} to a \madobj:

<<'demo_convert',echo=TRUE,cache=TRUE>>=
set.seed(456)
a_df <- data.frame(x=rnorm(1000),y=runif(1000),z=runif(1000))
a_df$v <- rowSums(a_df) + rnorm(nrow(a_df))
beta <- lm(v ~ x + y + z,data=a_df)
bmad <- as.madness(beta,vtag='beta')
show(bmad)
@

There are also two functions which construct a \madobj object from data: 
\begin{compactitem}
\item \Rfunction{twomoments}, which computes the sample mean and covariance
of \ssiz independent observations of a \nlatf vector given in a 
\bby{\ssiz}{\nlatf} matrix.
\item \Rfunction{theta}, which computes the uncentered second moment matrix
of \ssiz independent observations of a \nlatf vector given in a 
\bby{\ssiz}{\nlatf} matrix.
\end{compactitem}
Both methods allow one to feed in a more `exotic' variance-covariance estimator
than the default \Rfunction{stats::vcov}. More importantly, both methods
properly take into account the symmetry of the output. If one blindly stuffed a 
\eg covariance matrix into a \madobj object, one could easily overestimate the
variance of ones estimate by effectively ignoring that any estimate has to be
symmetric, and thus diagonal-mirrored elements do not vary independently.

<<'demo_theta',echo=TRUE,cache=TRUE>>=
set.seed(789)
X <- matrix(rnorm(1000*3),ncol=3)
# one of these apparently does not run under alternative BLAS, and 
# so CRAN precludes me from executing this code in the vignette,
# but I cannot tell which because I cannot use those alternative BLAS,
# so I am commenting out this code, which is absurd.
#Xmad <- theta(X)
#show(Xmad)

# more 'exotic' variance-covariance:
library(sandwich)
set.seed(1111)
X <- matrix(rnorm(100*2),ncol=2)

#twom <- twomoments(X,vcov=sandwich::vcovHAC)
#show(twom)
@

\subsection{Methods}

Obviously, to be of maximal use, the \madobj class should support any
method a reasonable user throws at it. Setting aside the definition of
`reasonable,' many methods have been implemented for the \madobj class:
unary minus; 
element-wise binary sum, product, difference, ratio, power; 
matrix product and Kronecker product; 
accumulating sum and product;
element-wise unary exponentiation, logarithm, and trigonometrics; 
\Rfunction{colSums}, \Rfunction{rowSums}, \Rfunction{colMeans}, \Rfunction{rowMeans}; 
matrix trace, determinant, matrix inverse, \Rfunction{solve}; 
Cholesky factor, symmetric square root, and \Rfunction{eigen};
matrix norms; 
\Rfunction{outer} with a limited set of functions; 
reshape operations; 
extracting lower, upper triangle, or diagonal;
\Rfunction{cbind}, \Rfunction{rbind} and concatenation; 
subselecting elements. 

Since not every conceivable function can be implemented, there is a 
method, \Rfunction{numderiv} which approximates derivatives numerically, 
producing a \madobj object. While symbolically computed derivatives are 
typically preferred, numerical approximations are preferred to an unusable
half-solution. Indeed, the numerical approximations are used in the unit tests
to ensure the derivatives are correctly computed. Moreover, the goal is to
simplify the computation and use of derivatives, which is not aided by a
dogmatic adherence to symbolic derivation.

Some example computations showing methods performed on \madobj objects:

<<'demo_2',echo=TRUE,cache=TRUE>>=
set.seed(2223)
X <- matrix(runif(5*3),ncol=3)
Y <- matrix(rnorm(length(X)),ncol=ncol(X))
Xmad <- madness(X,xtag='v')
Ymad <- madness(Y,xtag='v')

Zmad <- Xmad + Ymad
# hadamard product:
Zmad <- Xmad * Ymad
# matrix product:
Zmad <- t(Xmad) %*% Ymad
# equivalently
Zmad <- crossprod(Xmad,Ymad)

# can also interact with a scalar:
Zmad <- Xmad + Y
Zmad <- t(Xmad) %*% Y
# and so on.

# not sure _why_ you want to do these, but they can be done:
foo <- Xmad ^ Ymad
foo <- log(Xmad)
foo <- outer(Xmad,Y,'+')

# some sums and such:
cboth <- c(colSums(Xmad),colSums(Ymad))
xsum <- sum(Xmad)

# square matrix operations:
Zmad <- crossprod(Xmad,Ymad)
foo <- matrix.trace(Zmad)
foo <- det(Zmad)
invZ <- solve(Zmad)
invZ <- solve(Zmad,crossprod(Y,Y))

# and so on...

@

%require(madness)

%# the 'fit' is the Frobenius norm of Y - L*R
%# with a penalty for negative R.
%compute_fit <- function(R,L,Y) {
	%Rmad <- madness(R)
	%Err <- Y - L %*% Rmad
	%penalty <- sum(exp(-0.1 * Rmad))
	%fit <- norm(Err,'f') + penalty
%}

%set.seed(1234)
%R <- array(runif(5*20),dim=c(5,20))
%L <- array(runif(1000*5),dim=c(1000,5))
%Y <- array(runif(1000*20),dim=c(1000,20))
%ftv <- compute_fit(R,L,Y)
%show(ftv)
%show(val(ftv))
%show(dvdx(ftv))

%UNFOLD

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Examples}%FOLDUP

We further illustrate the use of the \madobj class with real examples.

\subsection{The \txtSR}%FOLDUP

<<'quandl_data',echo=FALSE,cache=TRUE>>=
data(wff3)
data(stock_returns)
@

The \txtSR is arguably the most popular metric for comparing the 
historical (or backtested) performance of assets. It is, however, a sample
statistic, and represents a noisy estimate of some population parameter, which
we will call the \emph{\txtSNR.} The asymptotic standard error of the \txtSR was given by 
Johnson and Welch, Jobson and Korkie, 
and others.  \cite{Johnson:1940,jobsonkorkie1981,lo2002} This statistic, and
its approximate standard error, can easily be computed with a \madobj object,
here applied to the Fama-French 3 factors weekly returns.
\cite{Fama_French_1992} The data were downloaded from French's website, and
comprise \Sexpr{nrow(wff3)} weeks of data, from 
\Sexpr{wff3$Date[1]} to
\Sexpr{wff3$Date[nrow(wff3)]}. 

<<'sr_demo_1',echo=TRUE,cache=TRUE>>=
data(wff3)
wff3$Mkt_RF <- wff3$Mkt - wff3$RF
ff3 <- wff3[,c('Mkt_RF','SMB','HML')]
# compute first and second moments:
# (beware: this method will not scale to larges numbers of assets!)
two <- twomoments(ff3,diag.only=TRUE)
# annualization factor:
ope <- 52
srs <- sqrt(ope) * two$mu / sqrt(two$sigmasq)

show(val(srs))
show(vcov(srs))

# for comparison:
library(SharpeR)
show(sr_vcov(as.matrix(ff3),ope=ope))
@

In fact, here we have illustrated the computation of the \txtSR of not a single
asset, but of three assets. We can perform tests of equality of the \txtSNR
of different assets. \cite{Leung2008,wright2014}

<<'sr_demo_2',echo=TRUE,cache=TRUE>>=
# test whether SMB has same signal-noise as HML:
testv <- t(srs) %*% array(c(0,-1,1),dim=c(3,1))
# now the Wald statistic:
wald <- as.numeric(val(testv)) / sqrt(diag(vcov(testv))) 
show(wald)
@

Here we demonstrate the computation of the Wald statistic: a quantity of
interest, typically assumed to be zero under the null hypothesis, divided by
its approximate standard error. In this case the Wald statistic is nowhere near
the `magical' value of 2, and we have little reason to doubt the null
hypothesis that \StockTicker{SMB} and \StockTicker{HML} have the same \txtSNR.

\subsubsection{Fighting overfit of the \txtSR}

The following recipe for quantitative strategy development is widely followed
in industry:
\begin{compactenum}
\item Write a piece of code which converts historical data to predicted returns
or target portfolio at point in time.
\item Backtest the code with all available historical data.
\item If the \txtSR of the backtested returns is not satisfactory, 
add more features to the trading strategy code, and repeat the backtest cycle.
\item When the backtested \txtSR is high enough, productionalize the model.
\end{compactenum}

When presented in this way, one suspects such a practice would yield
unsatisfactory results\footnote{Actually, this depends on the background rate
of profitable trading strategies. If one could randomly stumble upon 
strategies with high \txtSNR, this recipe might be fruitful. This is not
commonly experienced, however.}. Numerous tests have been devised to fight this
kind of `data-snooping' bias.  \cite{White:2000,Hsu2010471,Hansen:2005} 

Here we develop another approach to overfitting which models \txtSNR in terms
of the various attributes of the trading strategy being tested. Formally,
suppose that one records \nattf `features' about each strategy which has been
backtested. Let \feetv[i] be the vector of features pertaining to the \kth{i}
strategy, for $i=1,2,\ldots,\nstrat$. For example, suppose one is testing a
moving average crossover strategy. The features vector might be the lengths of
the two averaging windows. More elaborate strategies might have long feature
vectors, with information about lookback windows for features, which features
are included, how the predictive model was constructed, the form of the
covariance estimator, what instruments are hedged out, how portfolio
optimization is performed, and so on.

Letting \psnr[i] be the \txtSNR of this strategy, the simplest linear model
posits that $\psnr[i] = \trAB{\feetv[i]}{\vect{\beta}}.$ When testing this
model, one should take care to express the features in such a way that would
allow arbitrarily high \txtSNR by extrapolating away from the tested feature
set. This may require some imagination.

<<'overfit_0',echo=FALSE,cache=TRUE>>=
n_features <- 25
n_backtests <- 400
n_days <- 253 * 7
true_beta <- c(c(0.20,-0.10),rep(0,n_features-2))
set.seed(2356)
F_matrix <- matrix(runif(n_features*n_backtests),ncol=n_backtests)
# normalize:
F_matrix <- t(t(F_matrix) / sqrt(colSums(F_matrix^2)))
# latent returns, independent, identical variance, different means.
sigma <- 0.013
LRets <- matrix(rnorm(n_features*n_days,sd=sigma),nrow=n_days)
LRets <- t(t(LRets) + true_beta * sigma)
# manifest returns:
Rets <- LRets %*% F_matrix
# suppose only a subset of the features are actually measured though:
n_latent_feat <- 5
F_mat <- F_matrix[1:n_latent_feat,]
sub_beta <- true_beta[1:nrow(F_mat)]
@

One collects the backtested returns on the \nstrat strategies, then computes
the \txtSR of these, along with the variance-covariance matrix of these.
One can then use linear regression to estimate $\vect{\beta}$. By performing
this calculation with a \madobj object, one can compute the marginal Wald
statistics associated with each element of the feature vector. Here we present
a simple example using fake backtested returns. First imagine some 
process (hidden here) generates the returns on \Sexpr{n_days} of data
over \Sexpr{n_backtests}. Moreover, the returns are some linear combination of
\Sexpr{n_features} latent returns. The loadings on \Sexpr{n_latent_feat} of
these are observed as the features of the different strategies, including all
those with non-zero \txtSNR. The true \vect{\beta} in this case is
\asvec{\Sexpr{as.character(sub_beta)}}. Then proceed as follows:

<<'overfit_1',echo=TRUE,cache=TRUE>>=
show(dim(Rets))
show(dim(F_mat))
# use madness.
two <- twomoments(Rets,diag.only=TRUE)
srs <- two$mu / sqrt(two$sigmasq)

# the normal equations method. This is typically numerically unstable and
# not recommended, but I have not implemented QR factorization yet...
betahat <- solve(tcrossprod(F_mat,F_mat),F_mat %*% srs)
show(val(t(betahat)))
marginal_wald <- val(betahat) /sqrt(diag(vcov(betahat)))
show(t(marginal_wald))
@

%In this example, we have expressed the returns as the linear combination of
%\Sexpr{n_features} different returns streams, but only exposed the values of
%\Sexpr{5} of these features to the strawman quant, including the two which
%correspond to non-zero expected returns.
In this case, with \Sexpr{n_backtests} backtests of 
\Sexpr{n_days} days of returns, the marginal Wald statistics correctly identify
the first two features as significantly non-zero.

%UNFOLD

\subsection{The \txtFSR}%FOLDUP

Loosely, the \emph{information ratio} is the \txtSR of returns
\emph{in excess of some non-constant benchmark}. This assumes that
the proper `beta' of the investment with respect to the benchmark
is exactly one. A more pessimistic model of the returns of an asset is
essentially that of Arbitrage Pricing Theory, which expresses the
returns of an asset as the linear combination of the returns of some common
risk factors. For the purposes of estimating whether an investment strategy
has any idiosyncratic `alpha', this is equivalent to regressing the
historical returns against the historical returns of the risk factors, and
assessing whether the intercept term is significantly non-zero.

Rather than perform a hypothesis test, we can perform inference on the
intercept term divided by the volatility, here given the unfortunate name of 
\emph{\txtFSNR}. The model is as follows:
\begin{equation}
\reti[t] = \pregco[0] 1 + \sum_i^{\nattf - 1} \pregco[i] \retk[i,t] + \perr[t],
\label{eqn:factormodel}
\end{equation}
where \reti[t] is the return of the asset at time $t$,
$\retk[i,t]$ is the value of some \kth{i} `factor' at time
$t$, and the innovations, \perr, are assumed to be zero mean, and
have standard deviation \psig. Here we have forced the zeroth factor
to be the constant one, $\retk[0,t] = 1$.
\nocite{Ross_APT_1976} 

Given \ssiz observations, let \mretk be the \bby{\ssiz}{\nattf} matrix 
whose rows are the observations of the factors (including a column that
is the constant 1), and let \vreti be the \ssiz length column vector
of returns; then the multiple linear regression estimates are
\begin{equation}
\label{eqn:MLS_def}
\sregvec\defeq\minv{\wrapParens{\gram{\mretk}}}\trAB{\mretk}{\vreti},
\qquad
\ssig\defeq\sqrt{\frac{\gram{\wrapParens{\vreti -
\mretk\sregvec}}}{\ssiz-\nattf}}.
\end{equation}
We can then define a \emph{\txtFSR} as follows: let \convec be
some non-zero vector, and let \rfr be some risk-free, or disastrous,
rate of return. Then define
\begin{equation}
\label{eqn:gensr_def}
\ssrg \defeq \frac{\trAB{\sregvec}{\convec} -
\rfr}{\ssig}.
\end{equation}

The \txtFSNR appears in a transform of the `theta' matrix which 
encompasses both first and second moments of a 
distribution.  \cite{pav2013markowitz} Let 
$$
\avreti[i] \defeq \asvec{\reti[i],\tr{\vretk[i]}}.
$$
Define the second moment of this as
$$
\pvsm \defeq \E{\ogram{\avreti}}.
$$

First note that
\begin{equation}
\label{eqn:aug_secmom_isit}
\pvsm = \twobytwo{\psigsq +
\qform{\pfacsig}{\pregco}}{\trAB{\pregco}{\pfacsig}}{\pfacsig\pregco}{\pfacsig},
\end{equation}
where \pfacsig is the uncentered second moment of $\vretk$.
Simple matrix multiplication 
confirms that the inverse of \pvsm is 
\begin{equation}
\label{eqn:inv_aug_secmom_isit}
\minv{\pvsm} = \twobytwo{\psig^{-2}}{-\trAB{\pregco}\psig^{-2}}{%
-\pregco\psig^{-2}}{\minv{\pfacsig} + \psig^{-2}\ogram{\pregco}},
\end{equation}
and the Cholesky factor of that inverse is
\begin{equation}
\label{eqn:ichol_aug_secmom_isit}
\ichol{\pvsm} = \twobytwo{\psig^{-1}}{0}{%
-\pregco\psig^{-1}}{\ichol{\pfacsig}}.
\end{equation}
The \txtFSNR (\cf \eqnref{gensr_def}) can thus be expressed as
\begin{equation}
\label{eqn:gensnr_augform}
\psnrg = \frac{\trAB{\pregvec}{\convec} - \rfr}{\psig} = 
- {\asrowvec{\rfr,\tr{\convec}}\ichol{\pvsm}\basev[1]}.
\end{equation}
Up to scaling by some factor of \ssiz and \nattf, which becomes
immaterial for large \ssiz, the sample \txtFSR takes
the same form in the sample analogue.
%\begin{equation*}
%- \trace{\basev[1]\asrowvec{\rfr,\tr{\convec}}\ichol{\svsm}}
%\to \ssrg.
%\end{equation*}

We demonstrate this computation by grabbing the weekly simple returns of
\StockTicker{AAPL} and \StockTicker{IBM}, then attributing them to the Fama French three factor weekly returns.
We compute the \txtFSR to test for idiosyncratic alpha by computing the
intercept term divided by the volatility. Because we estimate the 
variance-covariance of the combined vector of returns, we can estimate the 
variance-covariance of our estimates of the \txtFSNRs together. Again we stress
that the hard work is in gathering the data together, putting them in the right
form, and sanely computing the estimate. The \madobj class automatically computes the
derivatives and the marginal Wald statistics are trivial to compute. 
Here we apply this analysis to the weekly returns of
\StockTicker{\Sexpr{colnames(stock_returns)[2]}} and of
\StockTicker{\Sexpr{colnames(stock_returns)[3]}}, collected over
\Sexpr{nrow(stock_returns)} weeks from 
\Sexpr{stock_returns[1,]$Date} to 
\Sexpr{stock_returns[nrow(stock_returns),]$Date}, as downloaded from Quandl. \cite{Quandl}
We will perform attribution against the Fama-French factor weekly returns
considered earlier. The tail of the data looks as follows:

<<'fsr_show',echo=TRUE,cache=TRUE>>=
data(wff3)
data(stock_returns)
allweekly <- stock_returns %>%   
	mutate(AAPL=100*AAPL,IBM=100*IBM) %>%  
	left_join(wff3,by='Date') %>%
	mutate(Mkt_RF=Mkt - RF) %>% 
	dplyr::select(-Mkt)

tail(allweekly,6) %>% dplyr::select(-RF) %>% knitr::kable(row.names=FALSE)
@
<<'fsr_noshow',echo=FALSE,cache=TRUE>>=
usenames <- gsub('_','\\_',colnames(allweekly))
@


\hfill\break
We now perform the attributions and test them for significance:
<<'fsr_demo_1',echo=TRUE,cache=TRUE>>=
tht <- theta(allweekly %>% dplyr::select(AAPL,IBM,Mkt_RF,SMB,HML) %>% mutate(one=1.0),xtag='stocks')

thinv_aapl <- chol(solve(tht[c(1,3,4,5,6),c(1,3,4,5,6)]))
thinv_ibm <- chol(solve(tht[c(2,3,4,5,6),c(2,3,4,5,6)]))
r0 <- 1e-4
v <- c(0,0,0,1)
r0v <- array(c(r0,v),dim=c(5,1))

exfacsr_aapl <- -(t(r0v) %*% t(thinv_aapl))[1,1] 
exfacsr_ibm <- -(t(r0v) %*% t(thinv_ibm))[1,1] 
exfacsr <- c(exfacsr_aapl,exfacsr_ibm)

show(cov2cor(vcov(exfacsr)))

waldboth <- val(exfacsr) / sqrt(diag(vcov(exfacsr)))
show(waldboth)
@

Here we conclude that the \txtFSNR of \StockTicker{AAPL} is greater than the hurdle rate of 1
bp per week, but that of \StockTicker{IBM} is not. The correlation of the errors of our
estimates is estimated to be fairly small. We can also perform a paired test
for whether the \txtFSNR of \StockTicker{AAPL} is greater than that of
\StockTicker{IBM} by taking the
difference in our estimates, and trivially computing the Wald statistic. In
this case, the evidence does not strongly support that \StockTicker{AAPL} has higher
idiosyncratic alpha than \StockTicker{IBM}:

<<'fsr_demo_2',echo=TRUE,cache=TRUE>>=
isbigger <- array(c(1,-1),dim=c(1,2)) %*% exfacsr 
show(val(isbigger) / sqrt(diag(vcov(isbigger))))
@
%UNFOLD

\subsection{The \txtMP}%FOLDUP

The Markowitz portfolio is the unconstrained portfolio that maximizes the
\txtSNR. For a vector of returns of \nlatf assets, if the unconditional
expected return is \pvmu, and the covariance of returns is \pvsig, then
the \txtMP is
\begin{equation}
\pportwopt \defeq \lambda \minvAB{\pvsig}{\pvmu},
\end{equation}
where $\lambda$ is some positive constant chosen to respect a cap on 
portfolio volatility (or leverage).

Since the population parameters \pvmu and \pvsig are unknown, they must be
estimated from the data. The noisy estimates may be unreliable, and one may
wish to check the standard error around the portfolio weights. This can be
found under assumptions of normality, or by using the `theta' matrix, but
computing directly via a \madobj object.  
\cite{BrittenJones1999,pav2013markowitz}
Here we compute the \txtMP on the 
\Sexpr{nrow(allweekly)} weeks of weekly returns, from
\Sexpr{allweekly[1,]$Date} to 
\Sexpr{allweekly[nrow(allweekly),]$Date}, of 
the Fama-French three factor data and of
\StockTicker{\Sexpr{usenames[2]}} and 
\StockTicker{\Sexpr{usenames[3]}} discussed above\footnote{It should be
recognized that one can \emph{not} trade on the Fama French factors directly,
that there is a selection bias in our choice of stocks, and so on. This is just
an example.}.

<<'the_mp_1',echo=TRUE,cache=TRUE>>=
library(sandwich)
twom <- twomoments(allweekly %>% select(AAPL,IBM,Mkt_RF,SMB,HML),vcov=sandwich::vcovHAC,diag.only=FALSE)
the_mp <- solve(twom$Sigma,twom$mu)
show(val(t(the_mp)))
show(vcov(the_mp))
# let's normalize to unit gross leverage:
mp_norm <- outer(the_mp,norm(the_mp,'1'),'/')
dim(mp_norm) <- dim(the_mp)

show(val(t(mp_norm)))
show(cov2cor(vcov(mp_norm)))
@

More elaborate inference on the \txtMP is possible via the `theta' matrix. 
Computation of theta requires one to choose `features' for prediction of
returns--either constant one for the unconditional model, or some time varying 
state variables for the linear conditional expectation model.  \cite{pav2013markowitz}
Using the \Rfunction{theta} method requires one to bind the features to the
returns. Here we perform this computation on the two stocks and the Fama French
weekly returns.

<<'the_mp_2',echo=TRUE,cache=TRUE>>=
library(sandwich)
tht <- theta(allweekly %>% mutate(one=1.0) %>% select(one,AAPL,IBM,Mkt_RF,SMB,HML),
	xtag='all5',vcov=sandwich::vcovHAC)
@

%We can then perform inference on the \txtMP subject to hedging constraints. 
%\cite{pav2013markowitz}

Suppose that \pvsm is somehow known to be reduced rank.  We can perform
inference on the \txtMP by computing the pseudoinverse of \svsm and computing
the sample \txtMP, performing inference on its elements.  \cite{pav2013markowitz}
Here we show this calculation assuming that \pvsm is of rank 2. 
%\cite{Izenman1975248}

<<'the_mp_3',echo=TRUE,cache=TRUE>>=
rnk <- 2
ev <- eigen(tht,symmetric=TRUE)
evals <- ev$values[,1:rnk]
evecs <- ev$vectors[,1:rnk]
thtinv <- evecs %*% todiag(evals^-1) %*% t(evecs)

the_mp2 <- - thtinv[2:nrow(thtinv),1]
show(val(t(the_mp2)))
show(vcov(the_mp2))
@

Comparing the \txtMP computed here to the one computed previously, we see that
the weights for the Fama French factors are much smaller in magnitude, while
the weight for \StockTicker{AAPL} is relatively unchanged.

%UNFOLD

\subsection{Correlation matrix}%FOLDUP

<<'correlation_show_0',echo=FALSE,cache=TRUE>>=
data(wff3)
wff3$Mkt_RF <- wff3$Mkt - wff3$RF
ff3 <- wff3[,c('Mkt_RF','SMB','HML')]
#usenames <- gsub('_','\\_',colnames(ff3))
usenames <- gsub('_','',colnames(ff3))
@

We can trivially use the covariance computed by \Rfunction{twomoments} to
compute a correlation matrix. Here we demonstrate this use on the Fama
French three factor weekly returns. We compute the Wald statistics
of the three off-diagonal correlations, finding that the correlation of weekly
returns between \StockTicker{\Sexpr{usenames[1]}} and 
\StockTicker{\Sexpr{usenames[2]}}, and between
returns between \StockTicker{\Sexpr{usenames[1]}} and 
\StockTicker{\Sexpr{usenames[3]}} is likely to be significantly non-zero,
while the correlation between 
\StockTicker{\Sexpr{usenames[2]}} and 
\StockTicker{\Sexpr{usenames[3]}} is apparently very close to zero:


<<'correlation_show_1',echo=TRUE,cache=TRUE>>=
library(sandwich)
data(wff3)
wff3$Mkt_RF <- wff3$Mkt - wff3$RF
ff3 <- wff3[,c('Mkt_RF','SMB','HML')]
# compute first and second moments:
two <- twomoments(ff3,vcov=sandwich::vcovHAC)
# basically cov2cor:
fcorr <- two$Sigma / tcrossprod(sqrt(diag(two$Sigma)))  
show(val(fcorr))
# compute the Wald statistic of the off-diagonal correlations:
odiag <- vech(fcorr,-1)
wald <- val(odiag) / sqrt(diag(vcov(odiag)))
show(wald)
@

%UNFOLD

\subsection{As an objective function}%FOLDUP

The \madobj class can be of some limited use when writing
objective functions\footnote{Automatic computation of the Hessian matrix
would improve this area of functionality, but it is not clear how this
would interoperate with support for computing derivatives of multivariate-valued 
functions.}. 
For this purpose,
the \Rfunction{to\_objective} method converts a \madobj object representing a
scalar into a numerical value with a \Robject{gradient} attribute. Consider
this artificial example of a matrix factorization objective with a penalty
for highly negative elements:

<<'as_objective_1',echo=TRUE,cache=TRUE>>=
fitfun <- function(R,L,Y,nu=-0.1) {
	Rmad <- madness(R)
	dim(Rmad) <- c(ncol(L),ncol(Y))
	Err <- Y - L %*% Rmad
	penalty <- sum(exp(nu * Rmad))
	fit <- norm(Err,'f') + penalty
	# convert to an objective:
	to_objective(fit)
}
set.seed(1234)
L <- array(runif(30*5),dim=c(30,5)) 
Y <- array(runif(nrow(L)*20),dim=c(nrow(L),20))
R0 <- array(runif(ncol(L)*ncol(Y)),dim=c(ncol(L),ncol(Y)))
Rk <- nlm(fitfun, R0, L, Y, iterlim=30)

show(c(fitfun(R0,L,Y)))
show(c(fitfun(Rk$estimate,L,Y)))
@

%UNFOLD

%UNFOLD

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Future Directions}%FOLDUP

To make this package more useful for the computation of objective functions,
the second derivative should also be computed and maintained during
operations. Moreover, use of higher-order derivatives could also be useful for
application of the delta method when the sample size is so small that
estimators are seriously biased.
It is challenging to add this feature while keeping the
`high-level' approach to automatic differentiation, since the second derivative
of matrix-to-matrix operations like the Cholesky factorization are hard to
code. 

%UNFOLD

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% bibliography%FOLDUP
%\nocite{markowitz1952portfolio,markowitz1999early,markowitz2012foundations}
%\bibliographystyle{jss}
%\bibliographystyle{siam}
%\bibliographystyle{ieeetr}
\bibliographystyle{plainnat}
%\bibliographystyle{acm}
\bibliography{common}
%\bibliography{AsymptoticMarkowitz}
<<gen_bibliography,echo=FALSE>>=
# generate the bibliography#FOLDUP
#see also
#http://r.789695.n4.nabble.com/Automating-citations-in-Sweave-td872079.html

#FID <- file("rauto.bib", "w")  # open an output file connection

cite.by.name <- function(x){ 
	res <- toBibtex(citation(x)) 
	if (is.list(res)) res <- res[[1]] 
	#2FIX: multiple citations; bleah;
	tofix <- grep("^@.+",res)
	fidx <- tofix[1]
	res[fidx] <- sub("{",paste("{",x,sep=''),res[fidx],fixed=TRUE) 

	if (length(tofix) > 1) {
		for (fidx in tofix[2:length(tofix)]) {
			res[fidx] <- sub("{",paste("{",x,"_",fidx,sep=''),res[fidx],fixed=TRUE) 
		}
	}
	cat(res,file = FID, sep = "\n")
	return(NULL)
} 
#z <- sapply( .packages(TRUE), function(x) try( cite.by.name(x) ) )
#close(FID)
#UNFOLD
@
%UNFOLD

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\appendix%FOLDUP

%UNFOLD

%It is trivial to show that for random variable $\vect{y}$,
%\E{\gram{\wrapParens{\vect{y} - \vect{z}}}} is minimized by
%$\vect{z} = \E{\vect{y}}$. Moreover, we have
%\begin{equation*}
%\begin{split}
%\E{\gram{\wrapParens{\vect{y} - \E{\vect{y}}}}} 
%&= \E{\trace{\gram{\wrapParens{\vect{y} - \E{\vect{y}}}}}},\\
%&= \trace{\E{\ogram{\wrapParens{\vect{y} - \E{\vect{y}}}}}},\\
%&= \trace{\VAR{\vect{y}}}.
%\end{split}
%\end{equation*}
%Thus, if we take the expectation
%of \eqnref{cos_law_form}, we can then bound the left hand
%side from below by the trace of the variance:
%\begin{equation}
%\begin{split}
%\trace{\VAR{\fnorm{\trchol{\pvsig}\sportwfnc{\mreti}}}} 
%&\le 2 - 2 \E{\frac{\pql{\sportwfnc{\mreti}}}{\psnropt}},\\
%&= 2 \wrapParens{1 -
%\trAB{{\E{\fnorm{\trchol{\pvsig}\sportwfnc{\mreti}}}}}{%
%\fnorm{\trchol{\pvsig}\pportwopt}}}.
%\label{eqn:var_bounds}
%\end{split}
%\end{equation}
%By bounding the variance via a \txtCR bound, we can find an
%upper bound on the expected value of \pql{\sportw}.

%Using the quadratic formula, we have
%\begin{equation}
%\cfnc{\gramprskvec} \le \frac{-\ssiz\gramprskvec +
%\sqrt{\ssiz^2\wrapParens{\gramprskvec}^2 + 2
%\ssiz\gramprskvec\wrapParens{\nlatf-1}}}{\nlatf - 1}.
%\end{equation}
%For $\ssiz\gramprskvec$ large, we have a cancellation of terms.
%Because the square root function is concave, it is less than its
%linear approximation about $\ssiz^2\wrapParens{\gramprskvec}^2$. 
%That is, we have $\sqrt{x + \epsilon} \le \sqrt{x} +
%\oneby{2\sqrt{x}}\epsilon$.
%Thus 
%\begin{equation}
%\cfnc{\gramprskvec} \le 1. \mbox{oops: 2FIX}
%\end{equation}

\end{document}
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