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Quanto Adjustments in the Presence of Stochastic Volatility

Quanto Adjustments in the Presence of Stochastic Volatility

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with suitable probabilities P1 and P2. In rema<strong>in</strong>der <strong>of</strong> this section we aim to obta<strong>in</strong> closed-form solutions for P1<br />

and P2. For this, we consider <strong>the</strong> correspond<strong>in</strong>g characteristic functions f1 and f2:<br />

f1 (φ) = E QY<br />

�<br />

�<br />

1 iφ ln S(T<br />

e<br />

)�<br />

QY iφ ln S(T<br />

, f2 (φ) = E e<br />

)�<br />

.<br />

Def<strong>in</strong><strong>in</strong>g x(t) = ln S(t), we start with work<strong>in</strong>g on f1:<br />

f1 (φ) =<br />

1<br />

F q �<br />

(1+iφ)x(T<br />

EQY e<br />

)�<br />

.<br />

(t, T )<br />

Apply<strong>in</strong>g Itô’s Lemma we obta<strong>in</strong> from (3):<br />

dx(t) =<br />

�<br />

r X − d − ρS,F XσF Xν(t) − 1<br />

2 ν(t)2<br />

�<br />

dt + ρS,νν(t)dW QY<br />

�<br />

ν (t) + 1 − ρ2 S,νν(t)dW (t).<br />

Us<strong>in</strong>g <strong>the</strong> <strong>in</strong>dependence <strong>of</strong> W and <strong>the</strong> tower property we have<br />

f1 (φ) = e(1+iφ)[(rX −d)(T −t)+x(t)]<br />

E QY<br />

�<br />

F q (t, T )<br />

e (1+iφ)<br />

�<br />

−ρS,F X σF X<br />

and with <strong>the</strong> equation (5) and Lemma 1 <strong>of</strong> <strong>the</strong> appendix<br />

f1 (φ) =<br />

×<br />

� T<br />

t<br />

�<br />

1 T<br />

ν(s)ds− 2 t ν(s)2 � T<br />

ds+ρS,ν t<br />

e (1+iφ)[(rX −d)(T −t)+x(t)]−(1+iφ) ρ S,ν<br />

2δ (ν(t) 2 +δ 2 (T −t))<br />

F q (t, T )<br />

�<br />

QY<br />

ν(s)dWν (s) +(1+iφ) 2 1−ρ2 S,ν � T<br />

2 t ν(s)2 �<br />

ds<br />

× D (t, T, ν(t), ˆs1, ˆs2, ˆs3) (8)<br />

with<br />

�<br />

1 + iφ<br />

ˆs1 = − (1 + iφ)<br />

2<br />

� 1 − ρ 2 �<br />

�<br />

� 2κρS,ν<br />

κ<br />

S,ν − 1 + , ˆs2 = (1 + iφ)<br />

δ<br />

ˆ θρS,ν<br />

δ<br />

+ ρS,F<br />

�<br />

XσF X , ˆs3 = (1 + iφ) ρS,ν<br />

2δ .<br />

Analogously, we get for f2 :<br />

with<br />

f2 (φ) = e [(rX −d)(T −t)+x(t)]iφ−iφ ρ S,ν<br />

2δ (ν(t) 2 +δ 2 (T −t)) × D (t, T, ν(t), ˜s1, ˜s2, ˜s3) (9)<br />

˜s1 = φ2<br />

2<br />

�<br />

� � 2 iφ<br />

1 − ρS,ν + 1 −<br />

2<br />

2κρS,ν<br />

� �<br />

κ<br />

, ˜s2 = iφ<br />

δ<br />

ˆ θρS,ν<br />

δ<br />

+ ρS,F<br />

�<br />

XσF X , ˜s3 = iφ ρS,ν<br />

2δ .<br />

Hav<strong>in</strong>g closed-form solutions for <strong>the</strong> characteristic functions f1 and f2 enables us to compute <strong>the</strong> probabilities P1<br />

and P2 via Fourier <strong>in</strong>version: 4<br />

Pj = 1 1<br />

+<br />

2 π<br />

� ∞<br />

Re<br />

0<br />

� e −iφ ln K fj<br />

iφ<br />

�<br />

dφ, j = 1, 2. (10)<br />

In summary, <strong>the</strong> quanto call price equation (7) toge<strong>the</strong>r with <strong>the</strong> explicit formulas (8), (9) for <strong>the</strong> characteristic<br />

functions f1, f2 and equation (10) give a closed-form solution for standard quanto call options. The value <strong>of</strong> a<br />

European quanto put option P q (t, T, K) can be obta<strong>in</strong>ed us<strong>in</strong>g <strong>the</strong> put-call parity for quanto options:<br />

P q (t, T, K) = C q (t, T, K) + e −rY (T −t) K − e −r Y (T −t) F q (t, T ).<br />

To <strong>the</strong> best <strong>of</strong> our knowledge this is <strong>the</strong> first paper to give closed-form formulas for standard quanto options <strong>in</strong> a<br />

stochastic volatility model framework which enables a fast and efficient pric<strong>in</strong>g <strong>of</strong> <strong>the</strong>se options also <strong>in</strong> <strong>the</strong> presence<br />

<strong>of</strong> stochastic volatility and avoids <strong>the</strong> deployment <strong>of</strong> Monte Carlo methods or numerical solutions <strong>of</strong> PDEs.<br />

4 We refer <strong>the</strong> reader to Lord and Kahl [7] for <strong>the</strong> numerical aspects <strong>of</strong> <strong>the</strong> Fourier <strong>in</strong>version.<br />

5

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