100.1-Introduction.Rmd

# Introduction

```{r, include=FALSE}
source("formatters.R", local=knitr::knit_global())
```

`r tufte::newthought("This")` first part is dedicated to the description of the experimental
environment used in this book.

As mentioned earlier, our approach is data-driven and empirical. It is
thus appropriate to start this text with a description of the sources of
data that we will use, and how to fetch publicly available financial
data from the Internet.

In the following chapters, we will make repeated use of the Rmetrics
pricing libraries, and often compare different models applied to the
same data. To facilitate this comparison, the Rmetrics pricing libraries
have been wrapped in a object-oriented framework, which hides most of
the implementation details, and allows us to focus on the key features
of the financial instruments and models. The framework uses the S4
object model [@Genolini2008] of the R language, and is described in
Chapter \@ref(basic-components).

Simulation is our tool of choice to explore the features of pricing
models and test their robustness. To that end, we have developped a
framework that facilitates the definition and testing of simple risk
management strategies. The core of this simulation framework is the `r to_index("DataProvider")`
class, which is presented in Chapter \@ref(sim-framework).

In addition to the packages found on CRAN, data sets and code used in the
text have been gathered into three packages:


empfin
: contains all the data sets and several utility functions for simple bond pricing and the manipulation of dates,

fInstrument
: provides the `r to_index("fInstrument", "classes")` class, that wraps the Rmetrics pricing library and the `r to_index("DataProvider")`
class, that acts as a container for market data,

DynamicSimulation
: contains tools needed to perform dynamic simulations, such as scenario generators and delta hedging simulators.