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Option Pricing in Affine Generalized Merton Models

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Advanced Modelling in Mathematical Finance

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 189))

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Abstract

In this article we consider affine generalizations of the Merton jump diffusion model Merton (J Finan Econ 3:125–144, 1976 [8]) and the respective pricing of European options. On the one hand, the Brownian motion part in the Merton model may be generalized to a log-Heston model, and on the other hand, the jump part may be generalized to an affine process with possibly state dependent jumps. While the characteristic function of the log-Heston component is known in closed form, the characteristic function of the second component may be unknown explicitly. For the latter component we propose an approximation procedure based on the method introduced in Belomestny et al. (J Funct Anal 257(4):1222–1250, 2009 [1]). We conclude with some numerical examples.

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Notes

  1. 1.

    Roger Lord confirmed to J.S. a typo in the published version and so we refer to the preprint version.

References

  1. Belomestny, D., Kampen, J., Schoenmakers, J.: Holomorphic transforms with application to affine processes. J. Funct. Anal. 257(4), 1222–1250 (2009)

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  2. Carr, P., Madan, D.: Option valuation using the fast Fourier transform. J. Comput. Financ. 2, 61–74 (1999)

    Article  Google Scholar 

  3. Cont, R., Tankov, P.: Financial modelling with jump processes. Chapman & Hall/CRC Financial Mathematics Series. Chapman & Hall/CRC, Boca Raton, FL (2004)

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  4. Duffie, D., Filipović, D., Schachermayer, W.: Affine processes and applications in finance. Ann. Appl. Probab. 13(3), 984–1053 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  5. Duffie, D., Pan, J., Singleton, K.: Transform analysis and asset pricing for affine jump-diffusions. Econometrica 68(6), 1343–1376 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  6. Eberlein, E., Glau, K., Papapantoleon, A.: Analysis of Fourier transform valuation formulas and applications. Appl. Math. Financ. 17(3), 211–240 (2010)

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  7. Lord, R., Kahl, C.: Complex logarithms in Heston-like models. Math. Fin. 20(4), 671–694 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  8. Merton, R.C.: Option pricing when underlying stock returns are discontinuous. J. Financ. Econ. 3, 125–144 (1976)

    Article  MATH  Google Scholar 

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Correspondence to John Schoenmakers .

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Appendix: Generator and \(\mathfrak {b}_{\beta }\) for the HSDJ Model

Appendix: Generator and \(\mathfrak {b}_{\beta }\) for the HSDJ Model

By conferring (7), (8), and (26), we have in fact

$$ v(x,dz)=v^{0}(dz)+x^{\top }v^{1}(dz)=\lambda _{0}\mu _{0}(z_{1})\delta _{0} (z_{2})dz_{1}dz_{2}+x_{2}\lambda _{1}\mu _{1}(z_{1})\delta _{0}(z_{2} )dz_{1}dz_{2} $$

with \(\delta _{0}\) being the Dirac delta function, that is the (singular) density of the Dirac probability measure \(\mathbb {R}\) concentrated in \(\left\{ 0\right\} .\) Thus, the generator of the HSDJ model is given by

$$\begin{aligned} Af\,(x_{1},x_{2})&=\left( -\lambda _{0}a_{0}-\left( \frac{1}{2}\alpha ^{2}+\lambda _{1}a_{1}\right) x_{2}\right) \partial _{x_{1}}f+\kappa \left( \theta -x_{2}\right) \partial _{x_{2}}f\\&+\,\frac{1}{2}\alpha ^{2}x_{2}\partial _{x_{1}x_{1}}f+\alpha \sigma \rho x_{2}\partial _{x_{1}x_{2}}f+\frac{1}{2}\sigma ^{2}x_{2}\partial _{x_{2}x_{2}}f\\&\!\!\!\!\!+\,\int _{\mathbb {R}}\left[ f(x_{1}+z_{1},x_{2})-f(x_{1} ,x_{2})-z_{1}\partial _{x_{1}}f\right] \left( \lambda _{0}\mu _{0}(z_{1} )dz_{1}+x_{2}\lambda _{1}\mu _{1}(z_{1})dz_{1}\right) . \end{aligned}$$

Since we are dealing with jump probability densities rather than infinite jump measures, as in the case of infinite activity processes, the generator may be written as

$$\begin{aligned}\begin{gathered} Af\,(x_{1},x_{2})=\left( -\lambda _{0}\left( \mathfrak {m}_{0}+a_{0}\right) -\left( \frac{1}{2}\alpha ^{2}+\lambda _{1}\left( \mathfrak {m}_{1} +a_{1}\right) \right) x_{2}\right) \partial _{x_{1}}f\\ +\,\kappa \left( \theta -x_{2}\right) \partial _{x_{2}}f+\frac{1}{2}\alpha ^{2}x_{2}\partial _{x_{1}x_{1}}f+\alpha \sigma \rho x_{2}\partial _{x_{1}x_{2} }f+\frac{1}{2}\sigma ^{2}x_{2}\partial _{x_{2}x_{2}}f\\ \!\!\!\!\!+\,\lambda _{0}\int _{\mathbb {R}}\left[ f(x_{1}+y,x_{2})-f(x_{1} ,x_{2})\right] \mu _{0}(y)dy\\ \!\!\!\!\!+\,x_{2}\lambda _{1}\int _{\mathbb {R}}\left[ f(x_{1}+y,x_{2} )-f(x_{1},x_{2})\right] \mu _{1}(y)dy, \end{gathered}\end{aligned}$$

using (29).

With \(f_{u}(x)=e^{\mathfrak {i}u^{\top }x}\) we so obtain,

$$\begin{aligned}\begin{gathered} \frac{Af_{u}(x)}{f_{u}(x)}=\left( -\lambda _{0}\left( \mathfrak {m}_{0} +a_{0}\right) -\left( \frac{1}{2}\alpha ^{2}+\lambda _{1}\left( \mathfrak {m}_{1}+a_{1}\right) \right) x_{2}\right) \mathfrak {i}u_{1}\\ +\,\kappa \left( \theta -x_{2}\right) \mathfrak {i}u_{2}-\frac{1}{2}\alpha ^{2}x_{2}u_{1}^{2}-\alpha \sigma \rho x_{2}u_{1}u_{2}-\frac{1}{2}\sigma ^{2} x_{2}u_{2}^{2}\\ +\,\lambda _{0}\psi _{0}(u_{1})+x_{2}\lambda _{1}\psi _{1}(u_{1}) \end{gathered}\end{aligned}$$

with

$$\begin{aligned} \psi _{i}(\xi ):=\int _{\mathbb {R}}\left( e^{\mathfrak {i}\xi y}-1\right) \mu _{i}(y)dy, \,\, i=0,1. \end{aligned}$$
(33)

Note that we have

$$\begin{aligned} \mathfrak {m}_{i}+a_{i}=\psi _{i}(-\mathfrak {i}), \,\, i=0,1. \end{aligned}$$
(34)

The first order derivatives w.r.t. u are,

$$\begin{aligned} \partial _{u_{1}}\frac{Af_{u}(x)}{f_{u}(x)}&=-\lambda _{0}\left( \mathfrak {m}_{0}+a_{0}\right) \mathfrak {i}-\left( \frac{1}{2}\alpha ^{2}+\lambda _{1}\left( \mathfrak {m}_{1}+a_{1}\right) \right) \mathfrak {i} x_{2}\\&-\,\alpha ^{2}x_{2}u_{1}-\alpha \sigma \rho x_{2}u_{2}+\lambda _{0} \partial _{u_{1}}\psi _{0}(u_{1})+x_{2}\lambda _{1}\partial _{u_{1}}\psi _{1} (u_{1})\\ \partial _{u_{2}}\frac{Af_{u}(x)}{f_{u}(x)}&=\kappa \left( \theta -x_{2}\right) \mathfrak {i}-\alpha \sigma \rho x_{2}u_{1}-\sigma ^{2}x_{2}u_{2}. \end{aligned}$$

For the second order derivatives we have

$$\begin{aligned} \partial _{u_{1}u_{1}}\frac{Af_{u}(x)}{f_{u}(x)}&=-\alpha ^{2}x_{2} +\lambda _{0}\partial _{u_{1}u_{1}}\psi _{0}(u_{1})+x_{2}\lambda _{1} \partial _{u_{1}u_{1}}\psi _{1}(u_{1})\\ \partial _{u_{1}u_{2}}\frac{Af_{u}(x)}{f_{u}(x)}&=-\alpha \sigma \rho x_{2}, \,\, \partial _{u_{2}u_{2}}\frac{Af_{u}(x)}{f_{u}(x)}=-\sigma ^{2}x_{2}, \end{aligned}$$

and for multi-indices \(\beta \) with \(\left| \beta \right| \ge 3,\) i.e. the higher order ones,

$$\begin{aligned} \partial _{u^{\beta }}\frac{Af_{u}(x)}{f_{u}(x)}= {\left\{ \begin{array}{ll} \lambda _{0}\partial _{u_{1}^{\left| \beta \right| }}\psi _{0} (u_{1})+x_{2}\lambda _{1}\partial _{u_{1}^{\left| \beta \right| }} \psi _{1}(u_{1}) \text { for } \beta =(\left| \beta \right| ,0),\\ 0 \text { if } \beta \ne (\left| \beta \right| ,0). \end{array}\right. } \end{aligned}$$
(35)

Hence the ingredients (14) of the recursion (15) are in multi-index notation as follows.

\(\left| \beta \right| =0:\)

$$\begin{aligned}\begin{gathered} \mathfrak {b}_{0}(x,u)=-\lambda _{0}\left( \mathfrak {m}_{0}+a_{0}\right) \mathfrak {i}u_{1}+\kappa \theta \mathfrak {i}u_{2}+\lambda _{0}\psi _{0}(u_{1})\\ +x_{2}\left( \lambda _{1}\psi _{1}(u_{1})-\left( \frac{1}{2}\alpha ^{2} +\lambda _{1}\left( \mathfrak {m}_{1}+a_{1}\right) \right) \mathfrak {i} u_{1}-\kappa \mathfrak {i}u_{2}-\frac{1}{2}\alpha ^{2}u_{1}^{2}-\alpha \sigma \rho u_{1}u_{2}-\frac{1}{2}\sigma ^{2}u_{2}^{2}\right) , \end{gathered}\end{aligned}$$

whence

$$\begin{aligned} \mathfrak {b}_{0}^{0}(u)&=-\lambda _{0}\left( \mathfrak {m}_{0} +a_{0}\right) \mathfrak {i}u_{1}+\kappa \theta \mathfrak {i}u_{2}+\lambda _{0} \psi _{0}(u_{1}),\\ \mathfrak {b}_{0,e_{1}}^{1}(u)&=0, \,\, \mathfrak {b}_{0,e_{2}} ^{1}(u)=\lambda _{1}\psi _{1}(u_{1})-\left( \frac{1}{2}\alpha ^{2}+\lambda _{1}\left( \mathfrak {m}_{1}+a_{1}\right) \right) \mathfrak {i}u_{1}\\&-\,\kappa \mathfrak {i}u_{2}-\frac{1}{2}\alpha ^{2}u_{1}^{2}-\alpha \sigma \rho u_{1}u_{2}-\frac{1}{2}\sigma ^{2}u_{2}^{2}. \end{aligned}$$

For \(\left| \beta \right| \) \(=1,\) (14) yields

$$\begin{aligned} \mathfrak {b}_{(1,0)}(x,u)&=-\lambda _{0}\left( \mathfrak {m}_{0} +a_{0}\right) -\lambda _{0}\partial _{u_{1}}\psi _{0}(u_{1})\mathfrak {i}-\left( \frac{1}{2}\alpha ^{2}+\lambda _{1}\left( \mathfrak {m}_{1}+a_{1}\right) \right) x_{2}\\&+\,\alpha ^{2}x_{2}u_{1}\mathfrak {i}+\alpha \sigma \rho x_{2}u_{2} \mathfrak {i}-x_{2}\lambda _{1}\partial _{u_{1}}\psi _{1}(u_{1})\mathfrak {i}\\ \mathfrak {b}_{(0,1)}(x,u)&=\kappa \left( \theta -x_{2}\right) +\alpha \sigma \rho x_{2}u_{1}\mathfrak {i}+\sigma ^{2}x_{2}u_{2}\mathfrak {i,} \end{aligned}$$

whence

$$\begin{aligned} \mathfrak {b}_{(1,0)}^{0}(u)&=-\lambda _{0}\left( \mathfrak {m}_{0} +a_{0}\right) -\lambda _{0}\partial _{u_{1}}\psi _{0}(u_{1})\mathfrak {i,}\,\, \mathfrak {b}_{(1,0),e_{1}}^{1}(u)=0,\\ \mathfrak {b}_{(1,0),e_{2}}^{1}(u)&=-\left( \frac{1}{2}\alpha ^{2} +\lambda _{1}\left( \mathfrak {m}_{1}+a_{1}\right) \right) +\alpha ^{2} u_{1}\mathfrak {i}+\alpha \sigma \rho u_{2}\mathfrak {i}-\lambda _{1} \partial _{u_{1}}\psi _{1}(u_{1})\mathfrak {i} \end{aligned}$$

and

$$\begin{aligned} \mathfrak {b}_{(0,1)}^{0}(u)&=\kappa \theta , \,\, \mathfrak {b} _{(0,1),e_{1}}^{1}(u)=0,\\ \mathfrak {b}_{(0,1),e_{2}}^{1}(u)&=-\kappa +\alpha \sigma \rho u_{1} \mathfrak {i}+\sigma ^{2}u_{2}\mathfrak {i.} \end{aligned}$$

Next, for \(\left| \beta \right| \) \(=2,\) (14) yields

$$\begin{aligned} \mathfrak {b}_{(2,0)}(x,u)&=\alpha ^{2}x_{2}-\lambda _{0}\partial _{u_{1} u_{1}}\psi _{0}(u_{1})-x_{2}\lambda _{1}\partial _{u_{1}u_{1}}\psi _{1}(u_{1}),\\ \mathfrak {b}_{(1,1)}(x,u)&=\alpha \sigma \rho x_{2},\\ \, \mathfrak {b}_{(0,2)}(x,u)&=\sigma ^{2}x_{2}, \end{aligned}$$

whence

$$\begin{aligned} \mathfrak {b}_{(2,0)}^{0}(u)&=-\lambda _{0}\partial _{u_{1}u_{1}}\psi _{0}(u_{1}), \,\, \mathfrak {b}_{(2,0),e_{1}}^{1}(u)=0,\\ \mathfrak {b}_{(2,0),e_{2}}^{1}(u)&=\alpha ^{2}-\lambda _{1}\partial _{u_{1}u_{1}}\psi _{1}(u_{1}),\\ \mathfrak {b}_{(1,1)}^{0}(u)&=\mathfrak {b}_{(1,1),e_{1}}^{1}(u)=0,\quad \mathfrak {b}_{(1,1),e_{2}}^{1}(u)=\alpha \sigma \rho ,\\ \mathfrak {b}_{(0,2)}^{0}(u)&=\mathfrak {b}_{(0,2),e_{1}}^{1}(u)=0,\quad \mathfrak {b}_{(0,2),e_{2}}^{1}(u)=\sigma ^{2}. \end{aligned}$$

For multi-indices \(\beta \) with \(\left| \beta \right| \ge 3\) we get

$$ \mathfrak {b}_{\beta }(x,u)= {\left\{ \begin{array}{ll} \lambda _{0}\mathfrak {i}^{-\left| \beta \right| }\partial _{u_{1}^{\left| \beta \right| }}\psi _{0}(u_{1})+x_{2}\lambda _{1}\mathfrak {i}^{-\left| \beta \right| }\partial _{u_{1}^{\left| \beta \right| }}\psi _{1}(u_{1}) \text { for } \beta =(\left| \beta \right| ,0),\\ 0 \text { if } \beta \ne (\left| \beta \right| ,0), \end{array}\right. } $$

whence

$$ \mathfrak {b}_{\beta }^{0}(u)= {\left\{ \begin{array}{ll} \lambda _{0}\mathfrak {i}^{-\left| \beta \right| }\partial _{u_{1}^{\left| \beta \right| }}\psi _{0}(u_{1}) \text { for } \beta =(\left| \beta \right| ,0),\\ 0 \text { if } \beta \ne (\left| \beta \right| ,0), \end{array}\right. } $$

and

$$\begin{aligned} \mathfrak {b}_{\beta ,e_{1}}^{1}(u)&=0,\\ \mathfrak {b}_{\beta ,e_{2}}^{1}(u)&= {\left\{ \begin{array}{ll} \lambda _{1}\mathfrak {i}^{-\left| \beta \right| }\partial _{u_{1}^{\left| \beta \right| }}\psi _{1}(u_{1}) \text { for } \beta =(\left| \beta \right| ,0),\\ 0\text { if } \beta \ne (\left| \beta \right| ,0). \end{array}\right. } \end{aligned}$$

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Bayer, C., Schoenmakers, J. (2016). Option Pricing in Affine Generalized Merton Models. In: Kallsen, J., Papapantoleon, A. (eds) Advanced Modelling in Mathematical Finance. Springer Proceedings in Mathematics & Statistics, vol 189. Springer, Cham. https://doi.org/10.1007/978-3-319-45875-5_10

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