QPR-13706: fix numerous typos

Author: O'Brien

Docs/UserGuide/curve_configurations/commodity_curves.tex

@@ -62,7 +62,7 @@ \subsubsection{Commodity Curves}
 \item InterpolationMethod [Optional]: The variable on which the interpolation is performed. The allowable values are
 Linear, LogLinear, Cubic, Hermite, LinearFlat, LogLinearFlat, CubicFlat, HermiteFlat, BackwardFlat, ForwardFlat. This is different to yield curves above in that Flat versions
 of the standard methods are defined, with each of these if there is no Spot price than any extrapolation between $T_0$ and the
-first future price will be flat (i.e. the first future price will be copied back "Flat" to $T_0$).
+first future price will be flat (i.e. the first future price will be copied back ``Flat'' to $T_0$).
 If the element is omitted or left blank, then it defaults to \emph{Linear}.
 \item Conventions [Optional]: The conventions to use, if omited it is assumed that these quotes are Outright prices.
 \item Extrapolation [Optional]: Set to \emph{True} or \emph{False} to enable or disable extrapolation respectively. If

Docs/UserGuide/curve_configurations/commodity_volatilities.tex

@@ -392,7 +392,7 @@ \subsubsection{Commodity Volatilities}
 
 Note that, similar to the procedure outlined above for the absolute strike surface, quote strings are created from the configuration to be looked up in the market. For the put deltas, quote strings of the form \lstinline!COMMODITY_OPTION/RATE_LNVOL/{N}/{C}/e_n/DEL/{T}/Put/d_m! are created. Here, \lstinline!d_m! are the \lstinline!PutDeltas! and \lstinline!{T}! is the delta type i.e.\ either \lstinline!Spot! or \lstinline!Fwd!. Similarly for the call deltas, quote strings of the form \lstinline!COMMODITY_OPTION/RATE_LNVOL/{N}/{C}/e_n/DEL/{T}/Call/d_j! are created where \lstinline!d_j! are the \lstinline!CallDeltas!. For ATM, quote strings of the form \lstinline!COMMODITY_OPTION/RATE_LNVOL/{N}/{C}/e_n/DEL/ATM/{A}[/DEL/{T}]! are created where \lstinline!{A}! is the \lstinline!AtmType! i.e.\ \lstinline!AtmSpot!, \lstinline!AtmFwd! or \lstinline!AtmDeltaNeutral! and \lstinline!{T}! is the optional delta type.
 
-Also, it is worth adding a note here on the interpolation for a delta based surface. Assume we want the volatility at time $t$ and absolute strike $s$ i.e. at the $(t, s)$ node. For the maturity time $t$, a delta "slice" i.e. a set of (delta, vol) pairs for that time $t$, is obtained by interpolating, or extrapolating, the variance in the time direction on each delta column. Then for each (delta, vol) pair at time $t$, an absolute strike value is deduced to give a slice at time $t$ in terms of absolute strike i.e. a set of (strike, vol) pairs at time $t$. This strike versus volatility curve is then interpolated, or extrapolated, to give the vol at the $(t, s)$.
+Also, it is worth adding a note here on the interpolation for a delta based surface. Assume we want the volatility at time $t$ and absolute strike $s$ i.e. at the $(t, s)$ node. For the maturity time $t$, a delta ``slice'' i.e. a set of (delta, vol) pairs for that time $t$, is obtained by interpolating, or extrapolating, the variance in the time direction on each delta column. Then for each (delta, vol) pair at time $t$, an absolute strike value is deduced to give a slice at time $t$ in terms of absolute strike i.e. a set of (strike, vol) pairs at time $t$. This strike versus volatility curve is then interpolated, or extrapolated, to give the vol at the $(t, s)$.
 
 Listing \ref{lst:comm_vol_apo_surface_config} outlines the configuration for the APO volatility surface. This currently only supports a \lstinline!QuoteType! of \lstinline!ImpliedVolatility! and \lstinline!VolatilityType! must be \lstinline!Lognormal!. This configuration takes a base commodity volatility surface and builds a surface that can be queried for volatilities to price APOs directly i.e.\ using the volatility directly in a Black 76 formula along with the average future price. It uses the approach described in the Section entitled \textit{Commodity Average Price Option - Future Settlement Prices} in the Product Catalogue to go from future option volatilities to APO volatilities.
 

Docs/UserGuide/curve_configurations/default_curves_from_cds.tex

@@ -6,7 +6,7 @@ \subsubsection{Default Curves from CDS}
 
 \begin{itemize}
 \item
-\lstinline!CurveId!: Unique identifier for the bootstrapped default curve. For index term curves a suffix \lstinline!_5Y! should be appended to the name indicating the index term, since this is the prefered name looked up by index cds and index cds option pricers. If such a curve is not found, the pricers will fall back to the specified credit curve id without suffix, i.e. following this naming convention is not mandatory, but recommended.
+\lstinline!CurveId!: Unique identifier for the bootstrapped default curve. For index term curves a suffix \lstinline!_5Y! should be appended to the name indicating the index term, since this is the preferred name looked up by index cds and index cds option pricers. If such a curve is not found, the pricers will fall back to the specified credit curve id without suffix, i.e. following this naming convention is not mandatory, but recommended.
 
 \item \lstinline!CurveDescription! [Optional]:
 A description of the default curve. It is for information only and may be left blank.

Docs/UserGuide/curve_configurations/yieldcurves.tex

@@ -28,7 +28,7 @@ \subsubsection{Yield Curves}
 \end{listing}
 
 The meaning of each of the top level elements in Listing \ref{lst:top_level_yc} is given below. If an element is labelled
-as 'Optional', then it may be excluded or included and left blank.
+as `Optional', then it may be excluded or included and left blank.
 \begin{itemize}
 \item CurveId: Unique identifier for the yield curve.
 \item CurveDescription: A description of the yield curve. This field may be left blank.
@@ -262,7 +262,7 @@ \subsubsection*{Average OIS Segment}
 IRS quote and an OIS-LIBOR basis swap spread quote.  The IDs of these two quotes are stored in the
 \lstinline!CompositeQuote! node. The \lstinline!RateQuote! node holds the ID of the vanilla IRS quote and the
 \lstinline!SpreadQuote! node holds the ID of the OIS-LIBOR basis swap spread quote. The \lstinline!PillarChoice! node
-determines the bootstrap pillars that are used (MaturityDate, LastRelevantDate, if not given 'LastRelevantDate' is the
+determines the bootstrap pillars that are used (MaturityDate, LastRelevantDate, if not given `LastRelevantDate' is the
 default value).
 
 For the \lstinline!Priority! and \lstinline!MinDistance! nodes see the explanation under ``Simple Segment''.
@@ -317,7 +317,7 @@ \subsubsection*{Tenor Basis Segment}
 values are short for receive, and long for pay. These are optional nodes. If they are left blank or omitted, then the projection
 curve is assumed to equal the curve being bootstrapped i.e.\ the current CurveId. However, at least one of the nodes
 needs to be populated to allow the bootstrap to proceed. The \lstinline!PillarChoice! node determines the bootstrap pillars
-that are used (MaturityDate, LastRelevantDate, if not given 'LastRelevantDate' is the default value).
+that are used (MaturityDate, LastRelevantDate, if not given `LastRelevantDate' is the default value).
 
 For the \lstinline!Priority! and \lstinline!MinDistance! nodes see the explanation under ``Simple Segment''.
 
@@ -351,7 +351,7 @@ \subsubsection*{Cross Currency Segment}
 in the other currency i.e.\ the currency in the currency pair that is not equal to the currency in Listing
 \ref{lst:top_level_yc}. The \lstinline!SpotRate! node holds the ID of a spot FX quote for the currency pair that is
 looked up in the {\tt market.txt} file. The \lstinline!PillarChoice! node determines the bootstrap pillars that are used
-(MaturityDate, LastRelevantDate, if not given 'LastRelevantDate' is the default value).
+(MaturityDate, LastRelevantDate, if not given `LastRelevantDate' is the default value).
 
 \begin{listing}[H]
 %\hrule\medskip
@@ -712,9 +712,9 @@ \subsubsection*{Ibor Fallback Segment}
 \end{listing}
 
 \subsubsection*{Discount Ratio Segment}
-\label{sec:dicount_ratio_segment}
+\label{sec:discount_ratio_segment}
 
-When the node name is \lstinline!DiscountRatio!, the \lstinline!Type! node has the only allowable value \emph{Dicount
+When the node name is \lstinline!DiscountRatio!, the \lstinline!Type! node has the only allowable value \emph{Discount
 Ratio} and the node has the structure shown in Listing \ref{lst:discount_ratio_segment}. This segment is used to build a
 curve with discount factors $P(0,t)$ from three input curves with discount factors $P_b(0,t)$, $P_n(0,t)$ and $P_d(0,t)$
 (``base'', ``numerator'', ``denominator'' curves) following the equation

Docs/UserGuide/examples/examples.tex

@@ -141,7 +141,7 @@ \section{Examples}\label{sec:examples}
 {\tt exposure\_trade\_*.csv} & Trade exposure evolution reports\\
 {\tt exposure\_nettingset\_*.csv} &  Netting set exposure evolution reports\\
 {\tt rawcube.csv} & NPV cube in readable text format \\
-{\tt netcube.csv} & NPV cube after netting and colateral, in readable text format \\
+{\tt netcube.csv} & NPV cube after netting and collateral, in readable text format \\
 {\tt *.csv.gz} & Intermediate storage of NPV cube and scenario data \\
 {\tt *.pdf} &  Exposure graphics produced by the python script {\tt run.py} after ORE completed\\
 \hline
@@ -473,7 +473,7 @@ \subsubsection{Sensitivity Analysis}
     </Analytic>
 \end{minted}
 
-The usual ``raw'' sensitivity analysis is performed by bumping the "raw" rates (zero rates, hazard rates, inflation zero rates, optionlet vols).
+The usual ``raw'' sensitivity analysis is performed by bumping the ``raw'' rates (zero rates, hazard rates, inflation zero rates, optionlet vols).
 This is followed by the Jacobi transformation that turns ``raw'' sensitivities  into sensitivities in the par domain (Deposit/FRA/Swap rates, FX Forwards, CC Basis Swap spreads, 
 CDS spreads, ZC and YOY Inflation Swap rates, flat Cap/Floor vols). The conversion is controlled by the additional {\tt ParConversion} data blocks 
 in {\tt sensitivity.xml} where the assumed par instruments and corresponding conventions are coded, as shown below for three types of discount curves.
@@ -670,7 +670,7 @@ \subsubsection{Stand-alone Par Conversion Utility}
 \label{example:marketrisk_parconversionl}
 
 This example ({\tt python run\_parconversion.py}) demonstrates ORE's capability to convert external computed zero sensitivities (e.g Zero rates) to par sensitivities (e.g. to Swap rates) 
-that is implemented  by means of a Jacobi transformation of the "raw" sensitivities (e.g. to zero rates), see a sketch of the 
+that is implemented  by means of a Jacobi transformation of the ``raw'' sensitivities (e.g. to zero rates), see a sketch of the 
 methodology in \cite{methods} and section \ref{sec:sensitivity} for configuration details.
 
 To perform a par sensitivity analysis, the following required change in {\tt ore.xml} is required
@@ -716,7 +716,7 @@ \subsubsection{Stand-alone Par Conversion Utility}
 \item baseNpvColumn: The base npv of the trade / nettingset / portfolio in currency.
 \end{enumerate}
 
-This is followed by the Jacobi transformation that turns "raw" sensitivities  into sensitivities in the par domain (Deposit/FRA/Swap rates, FX Forwards, CC Basis Swap spreads, 
+This is followed by the Jacobi transformation that turns ``raw'' sensitivities  into sensitivities in the par domain (Deposit/FRA/Swap rates, FX Forwards, CC Basis Swap spreads, 
 CDS spreads, ZC and YOY Inflation Swap rates, flat Cap/Floor vols). The conversion is controlled by the additional {\tt ParConversion} data blocks 
 in {\tt sensitivity.xml} where the assumed par instruments and corresponding conventions are coded, as shown below for three types of discount curves.
 
@@ -2208,15 +2208,15 @@ \subsection{American Monte Carlo}\label{example:amc}
 The cases in this section, folder {\tt AmericanMonteCarlo},  demonstrate how to use American Monte Carlo Simulation (AMC)
 to generate exposures in ORE.
 \begin{itemize}
-\item We start with benchmarking against "classic" exposure simulation, i.e. 
+\item We start with benchmarking against ``classic'' exposure simulation, i.e. 
   we run both AMC and classic simulation on a small IR/FX portfolio, almost vanilla, that
   consists of a Bermudan Swaption, Single and Cross Currency Swaps, FX Swap and FX Option,
   and we compare the resulting AMC vs. classic exposures. \\
   Run with {\tt python run\_benchmark.py}
 \item Scripted Bermudan Swaption and LPI Swap: This case shows that the scripted trade
   framework works with AMC too, demonstrated here with a scripted Bermudan Swaption and an LPI Swap. \\
   Run with: {\tt python run\_scriptedberm.py}
-\item FX TaRF: FxTaRF product is implemented using the scripted trade framework "under the hood"
+\item FX TaRF: FxTaRF product is implemented using the scripted trade framework ``under the hood''
   with the payoff script embedded into C++, so that it is neither explicit in the trade XML nor
   in the script library.\\
   Run with: {\tt python run\_fxtarf.py}
@@ -2229,7 +2229,7 @@ \subsubsection{Benchmarking AMC vs Classic Simulation}
 
 This example demonstrates how to use American Monte Carlo simulation (AMC) to generate exposures in ORE.
 For a sketch of the methodology and comments on its implementation in ORE see \cite{methods}.
-Moreover we discuss the essential configuration chnages for applying AMC.
+Moreover we discuss the essential configuration changes for applying AMC.
 
 Calling 
 
@@ -2860,7 +2860,7 @@ \subsubsection{NPV Sensitivities with AAD and GPUs}
 \item as above but using the Computation Graph, see {\tt UseCG=true} in {\tt pricingengine\_cg.xml}, which
   is the basis for the following two approaches ((14 sec on Apple M2 Max))
 \item using AAD, see {\tt pricingengine\_ad.xml} ((2.2 sec on Apple M2 Max))
-\item using the external device if available, see {\tt pricingengine\_gpu.xml} (2.3 sec on Apple M2 Max with "OpenCL/Apple/Apple M2 Max" device) 
+\item using the external device if available, see {\tt pricingengine\_gpu.xml} (2.3 sec on Apple M2 Max with ``OpenCL/Apple/Apple M2 Max'' device) 
 \end{itemize}
 to compare sensitivities and performance. In the latter case we have set the external device in
 {\tt pricingengine\_gpu.xml} to ``BasicCpu/Default/Default'' which mimics an external device on the CPU.

Docs/UserGuide/examples/legacyexamples.tex

@@ -1461,7 +1461,7 @@ \subsection{Multifactor Hull-White Scenario Generation}% Example 37
 \end{table}
 
 again matching the input principal components quite well. The second eigenvector is the negative of the input vector
-here (the principal compoennt analysis can not distinguish these of course).
+here (the principal component analysis can not distinguish these of course).
 
 The example also produces a plot comparing the input eigenvectors and the model implied eigenvectors as shown in figure \ref{fig:ex37}.
 
@@ -1809,7 +1809,7 @@ \subsubsection*{Multi Leg Options / MC pricing engine}
 \item \verb+IrCalibrationStrategy+ can be \verb+None+, \verb+CoterminalATM+, \verb+UnderlyingATM+
 \item \verb+FXCalibration+ can be \verb+None+ or \verb+Bootstrap+
 \item \verb+ExtrapolateFxVolatility+ can be \verb+true+ or \verb+false+; if false, no calibration instruments are used
-  that require extrapolation of the market fx volatilty surface in option expiry direction
+  that require extrapolation of the market fx volatility surface in option expiry direction
 \item \verb+Corr_Key1_Key2+: These entries describe the cross asset model correlations to be used; the syntax for
   \verb+Key1+ and \verb+Key2+ is the same as in the simulation configuration for the cross asset model
 \end{enumerate}
@@ -1820,7 +1820,7 @@ \subsection{Par Sensitivity Analysis}% Example 40
 %--------------------------------------------------------------------
 
 The example in folder {\tt Examples/Example\_40}  demonstrates ORE's par sensitivity analysis (e.g. to Swap rates) 
-that is implemented  by means of a Jacobi transformation of the "raw" sensitivities (e.g. to zero rates), see a sketch of the 
+that is implemented  by means of a Jacobi transformation of the ``raw'' sensitivities (e.g. to zero rates), see a sketch of the 
 methodology in \cite{methods} and section \ref{sec:sensitivity} for configuration details.
 
 To perform a par sensitivity analysis, the following required change in {\tt ore.xml} is required
@@ -1851,8 +1851,8 @@ \subsection{Par Sensitivity Analysis}% Example 40
 \item CapFloor volatilities
 \end{itemize}
 
-The usual sensitivity analysis is performed by bumping the "raw" rates (zero rates, hazard rates, inflation zero rates, optionlet vols).
-This is followed by the Jacobi transformation that turns "raw" sensitivities  into sensitivities in the par domain (Deposit/FRA/Swap rates, FX Forwards, CC Basis Swap spreads, 
+The usual sensitivity analysis is performed by bumping the ``raw'' rates (zero rates, hazard rates, inflation zero rates, optionlet vols).
+This is followed by the Jacobi transformation that turns ``raw'' sensitivities  into sensitivities in the par domain (Deposit/FRA/Swap rates, FX Forwards, CC Basis Swap spreads, 
 CDS spreads, ZC and YOY Inflation Swap rates, flat Cap/Floor vols). The conversion is controlled by the additional {\tt ParConversion} data blocks 
 in {\tt sensitivity.xml} where the assumed par instruments and corresponding conventions are coded, as shown below for three types of discount curves.
 
@@ -2051,7 +2051,7 @@ \subsection{Initial Margin: ISDA SIMM and IM Schedule}% Example 44
 
 \subsubsection*{IM Schedule}
 
-As an additonal case in this example we demonstrate how to use the IM Schedule method to compute initial margin.
+As an additional case in this example we demonstrate how to use the IM Schedule method to compute initial margin.
 The related input file is {\tt Input/ore\_schedule.xml}. It is also run when calling {\tt python run.py}, and results are written to folder 
 {\tt Output/IM\_SCHEDULE}.
 The basic input is provided in CRIF file format where ORE expects two lines per trade, one with RiskClass = PV and one with RiskClass = Notional, 
@@ -2169,7 +2169,7 @@ \subsection{Zero to Par sensitivity Conversion Analysis}% Example 50
 %--------------------------------------------------------------------
 
 The example in folder {\tt Examples/Example\_50} demonstrates ORE's capability to convert external computed zero sensitivities (e.g Zero rates) to par sensitivities (e.g. to Swap rates) 
-that is implemented  by means of a Jacobi transformation of the "raw" sensitivities (e.g. to zero rates), see a sketch of the 
+that is implemented  by means of a Jacobi transformation of the ``raw'' sensitivities (e.g. to zero rates), see a sketch of the 
 methodology in \cite{methods} and section \ref{sec:sensitivity} for configuration details.
 
 To perform a par sensitivity analysis, the following required change in {\tt ore.xml} is required
@@ -2215,7 +2215,7 @@ \subsection{Zero to Par sensitivity Conversion Analysis}% Example 50
 \item baseNpvColumn: The base npv of the trade / nettingset / portfolio in currency.
 \end{enumerate}
 
-This is followed by the Jacobi transformation that turns "raw" sensitivities  into sensitivities in the par domain (Deposit/FRA/Swap rates, FX Forwards, CC Basis Swap spreads, 
+This is followed by the Jacobi transformation that turns ``raw'' sensitivities  into sensitivities in the par domain (Deposit/FRA/Swap rates, FX Forwards, CC Basis Swap spreads, 
 CDS spreads, ZC and YOY Inflation Swap rates, flat Cap/Floor vols). The conversion is controlled by the additional {\tt ParConversion} data blocks 
 in {\tt sensitivity.xml} where the assumed par instruments and corresponding conventions are coded, as shown below for three types of discount curves.