Financial Modelling in Python

P1: JYS

c09 JWBK378-Fletcher May 12, 2009 19:0 Printer: Yet to come

142 Financial Modelling in Python

exercised before the end of the trade.

def call (self, symbol value pairs to add = None):

.

.

.

# calculate pv

underlying = ctr.retrieve

symbol("underlying")

underlying *= 0

cnt=0

for l in self.

trade.legs():

underlying += ctr.retrieve

symbol("leg"+str(cnt))

cnt += 1

self.

symbol listener (to , "underlying", underlying)

ctr.update

symbol("underlying", underlying, to )

if self.

regression model:

berm = ctr.retrieve

symbol("berm")

berm = self.

exercise helper.max(to , \

self.

trade.exercise type()*underlying, berm)

ctr.update

symbol("berm", berm, to )

pv = 0

if self.

trade.has exercise schedule():

if self.

trade.exercise type() == exercise type.callable:

pv = ctr.retrieve

symbol("berm").mean()

else:

pv = ctr.retrieve

symbol("underlying").mean() \

+ctr.retrieve

symbol("berm").mean()

else:

pv = ctr.retrieve

symbol("underlying").mean()

return pv

The above pricing framework is generic and makes few assumptions on either the model or

the underlying numerical container. One obvious caveat to this is that the pricing framework

doesn’t handle stubs, the treatment of which is beyond the scope of this book. As a final

comment, the inclusion of fees into the framework is left as an exercise for the reader.

9.3 CONCLUDING REMARKS

In this chapter we have developed two pricing frameworks: a lattice pricing framework for

pricing non-path-dependent ...