Financial Modelling in Python

P1: JYS

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

138 Financial Modelling in Python

events = self. timeline.events(time)

# evaluate explanatory variables and immediate exercise values

for event in events:

# set event on controller

ctr.set

event(event)

# evaluate

if is

exercise event(event):

# evaluate underlying

underlying = ies[ex

cnt, :]

cnt = 0

for l in self.

trade.legs():

underlying += ctr.retrieve

symbol("leg"+str(cnt))

cnt += 1

underlying *= self.

trade.exercise type()

underlying *= normalisation

# evaluate explanatory variables and numeraire

ns[ex

cnt, :] = ctr.numeraire(to )

vs[ex

cnt, :] = ctr.explanatory variables(to )

ex

cnt += 1

# evaluate cash flows

for event in events:

# set event on controller

ctr.set

event(event)

# evaluate

if is

pay event(event):

# evaluate payoff

cpn = ctr(to

)

symbol = "leg"+str(event.leg

id())

leg

pv = ctr.retrieve symbol(symbol)

leg

pv += cpn

self.

symbol listener (to , symbol, leg pv)

ctr.update

symbol(symbol, leg pv, to )

from

=to

.

.

.

Once we have all of this information we compute the immediate exercise values by subtracting

from the total sum of all cash flows each of the stored sum of cash flows in turn. The regression

is then carried out via an invocation of the free function pickup

value regression.The

method ends with the creation of an instance of the exercise

helper class, which will be

used in the actual pricing of the callable structure.

class monte carlo pricer:

def

exercise ...