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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

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