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An example with 3 banks
=======================

This document describe a toy example for the use of the package systemicrisk.

```{r}
set.seed(123910) # arbitrary seed 
library(systemicrisk)
```

Suppose we are dealing with 3 banks and that they have total interbank
liabilities/assets given by the following:
```{r}
l <- c(1,2.5,3)
a <- c(0.7,2.7,3.1)
```
Suppose we are assuming $p=0.5$ and $\lambda=0.25$.
Then we can run the Gibbs sampler to get a sample of the liabilities
matrix condtional on $l$ and $a$.
```{r}
L <- sample_ERE(l,a,p=0.5,lambda=0.25,nsamples=200,thin=20,burnin=10)
```
Some examples of the matrics generated are below.
```{r}
L[[1]]
L[[2]]
L[[3]]
```
Diagnostic of the R-output
--------------------------
All the caveats of MCMC algorithms apply. In particular it is useful
to plot the paths of the individual liabilities, which we do below
for the liabilities of Bank 1 towards Bank 2.
```{r fig.width=7,fig.height=4}
plot(sapply(L,function(x)x[1,2]),type="b")
```

Also, the autocorrelation function should decline quickly.
```{r fig.width=7,fig.height=4}
acf(sapply(L,function(x)x[1,2]))
```

In this case it decays quickly below the white-noise threshold (the
horizontal dashed lines).

Default of banks
----------------
To be able to talk about default of banks  we need to know the
external assets and liabilities of the banks.
Suppose we assume the following:
```{r}
ea <- c(1,1,1)
el <- c(1,1,1)
```

```{r}
default(L[[1]],ea=ea,el=el)$defaultind
default(L[[2]],ea=ea,el=el)$defaultind
default(L[[3]],ea=ea,el=el)$defaultind
```

Below we apply two different default  algorithms (without default costs and with default costs) to the liabilities matrices and compute the average number of times each bank defaults.
```{r}
rowMeans(sapply(L, function(Lakt) default(Lakt,ea=ea,el=el)$defaultind))
```
```{r}
rowMeans(sapply(L, function(Lakt) default(Lakt,ea=ea,el=el,alpha=0.98,beta=0.98)$defaultind))
```