Pricing Asian Options in a Semimartingale Model - Department of ...
Pricing Asian Options in a Semimartingale Model - Department of ...
Pricing Asian Options in a Semimartingale Model - Department of ...
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We can write<br />
( )<br />
d<br />
Xt<br />
S t<br />
=<br />
=<br />
=<br />
( ) ( ) ( ) ( )<br />
X t<br />
S t<br />
− Xt−<br />
S t−<br />
= q t− − Xt−<br />
S t−<br />
1 − 1<br />
1+∆H t<br />
= q t− − Xt− ∆Ht<br />
S t− 1+∆H t<br />
.<br />
( ) (<br />
)<br />
q t− − Xt−<br />
S t−<br />
dH t − rdt − d〈H c 〉 t +<br />
( ) (<br />
q t− − Xt−<br />
S t−<br />
dH t − rdt − d〈H c 〉 t −<br />
( ) ( ∫ ∞<br />
q t− − Xt−<br />
S t−<br />
dHt c − d〈H c 〉 t +<br />
−∞<br />
(<br />
q t− − Xt−<br />
S t−<br />
) (<br />
∆Ht<br />
1+∆H t<br />
− ∆H t<br />
)<br />
.<br />
)<br />
∆H2 t<br />
1+∆H t<br />
x (µ(dt, dx) − ν(dt, dx)) −<br />
∫ ∞<br />
−∞<br />
)<br />
x 2<br />
1+x µ(dt, dx)<br />
or<br />
(<br />
∫ ∞<br />
∫ ∞<br />
)<br />
dZ t = (q t− − Z t− ) dHt c − d〈H c x<br />
〉 t + x (µ(dt, dx) − ν(dt, dx)) −<br />
2<br />
1+x µ(dt, dx) .<br />
−∞<br />
−∞<br />
Observe that Z t is a Markovian process under Q. Theorem 2.3 and the Markovian property give us the value<br />
process<br />
v(t, Z t ) = E Q [(Z T − K 1 ) + |F t ],<br />
which is a mart<strong>in</strong>gale by def<strong>in</strong>ition.<br />
Note d〈Z c 〉 t = (q t− − Z t− ) 2 d〈H c 〉 t and thus<br />
dv(t, Z t ) = v t (t, Z t− )dt + v z (t, Z t− )dZ + 1 2 v zz(t, Z t− )d〈Z c 〉 t<br />
+v(t, Z t ) − v(t, Z t− ) − v z (t, Z t− )∆Z t<br />
= v t (t, Z t− )dt + v z (t, Z t− )dZ + 1 2 v zz(t, Z t− )(q t− − Z t− ) 2 d〈H c 〉 t<br />
(<br />
)<br />
+v t, Z t− + (q t− − Z t− ) ∆H<br />
1+∆H<br />
− v(t, Z t− ) − v z (t, Z t− )(q t− − Z t− ) ∆H<br />
1+∆H<br />
= Local Mart<strong>in</strong>gale + v t (t, Z t− )dt + 1 2 v zz(t, Z t− )(q t− − Z t− ) 2 d〈H c 〉 t<br />
∫ ∞ { (<br />
)<br />
+ v t, Z t− + (q t− − Z t− ) x<br />
1+x<br />
−∞<br />
}<br />
−v(t, Z t− ) − v z (t, Z t− )(q t− − Z t− ) x<br />
1+x<br />
ν(dt, dx).<br />
The fact that a predictable local mart<strong>in</strong>gale with f<strong>in</strong>ite variation start<strong>in</strong>g at zero is zero concludes the pro<strong>of</strong>.<br />
⋄<br />
Corollary 3.4 In the case when H is a Lévy process, the <strong>in</strong>tegro-differential equation simplifies to<br />
(3.5) v t (t, z) + c 2 v zz(t, z)(q t− − z) 2<br />
for 0 ≤ t ≤ T and z ∈ R.<br />
+<br />
∫ ∞<br />
−∞<br />
{ (<br />
)<br />
}<br />
v t, z + (q t− − z) x<br />
1+x<br />
− v(t, z) − v z (t, z)(q t− − z) x<br />
1+x<br />
K(dx) = 0<br />
Pro<strong>of</strong>. The canonical decomposition <strong>of</strong> H is<br />
H t = rt +<br />
∫ t<br />
0<br />
√ c dWs +<br />
∫ t<br />
0<br />
∫ ∞<br />
−∞<br />
x ( µ(ds, dx) − K(dx)dt )<br />
where W t is a standard Brownian Motion. Apply<strong>in</strong>g theorem 3.3, we get<br />
(3.6) v t (t, Z t− ) + c 2 v zz(t, Z t− )(q t− − Z t− ) 2<br />
∫ ∞ {<br />
+ v<br />
−∞<br />
(<br />
t, Z t− + (q t− − Z t− ) x<br />
1+x<br />
S<strong>in</strong>ce the support for Z t− is R, we get the above equation.<br />
)<br />
}<br />
− v(t, Z t− ) − v z (t, Z t− )(q t− − Z t− ) x<br />
1+x<br />
K(dx) = 0.<br />
⋄<br />
6