Abstract
A theoretical analysis tool, iterated optimal stopping, has been used as the basis of a numerical algorithm for American options under regime switching (Le and Wang in SIAM J Control Optim 48(8):5193–5213, 2010). Similar methods have also been proposed for American options under jump diffusion (Bayraktar and Xing in Math Methods Oper Res 70:505–525, 2009) and Asian options under jump diffusion (Bayraktar and Xing in Math Fin 21(1):117–143, 2011). An alternative method, local policy iteration, has been suggested in Huang et al. (SIAM J Sci Comput 33(5):2144–2168, 2011), and Salmi and Toivanen (Appl Numer Math 61:821–831, 2011). Worst case upper bounds on the convergence rates of these two methods suggest that local policy iteration should be preferred over iterated optimal stopping (Huang et al. in SIAM J Sci Comput 33(5):2144–2168, 2011). In this article, numerical tests are presented which indicate that the observed performance of these two methods is consistent with the worst case upper bounds. In addition, while these two methods seem quite different, we show that either one can be converted into the other by a simple rearrangement of two loops.
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This work was supported by Tata Consulting Services and the Natural Sciences and Engineering Research Council of Canada.
Appendix: Error Bound for Local Policy Iteration with Inexact Inner Solution
Appendix: Error Bound for Local Policy Iteration with Inexact Inner Solution
In this Appendix, we generalize the result in Theorem 6.1 to include the effect of an approximate solution to Line 5 in Algorithm 6.1. We need only generalize the steps used in [21].
If \( V^{n+1}\) is a solution to Eq. (5.2) then
while from Algorithm 6.1, we have
The term \( \mathcal{E }^{k}\) in Eq. (9.2) takes into account that that we may not necessarily have the exact solution to Line 5 in Algorithm 6.1, if we use Algorithm 6.2.
Subtracting Eq. (9.1) from Eq. (9.2) we obtain
If \({\hat{Q}}\) satisfies
then, from Eq. (9.3), we have, \((E^{k+1} = (V^{n+1})^{k+1} - V^{n+1})\)
or, since \(\mathcal{A }(Q)\) is an \(M\) matrix,
where \(\mathbf{{e}} = [1,1,\ldots ,1]^{\prime }\). Similarly
Hence if
then
Equations (9.6) and (9.9) then give
From [21], we have that
and \(C_2\) bounded, hence
and in view of Eq. (9.11), we obtain
Manipulation of Line 4 in Algorithm 6.2 results in
Recall that at convergence of Algorithm 6.2, we have
When Algorithm 6.2 terminates, we have
then, from Eqs. (9.14) and (9.16) (with \(U^{m+1} = (V^{n+1})^{k+1}\))
and then
which implies
Since \(\mathcal{A }(Q^m)\) is bounded, \( \Vert \mathcal{E }^{k} \Vert _{\infty }\) (and hence \(\Vert \mathcal{E }_{ \max } \Vert _{\infty }\)) can be made arbitrarily small by making \(tolerance_{inner}\) small in Algorithm 6.2.
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Babbin, J., Forsyth, P.A. & Labahn, G. A Comparison of Iterated Optimal Stopping and Local Policy Iteration for American Options Under Regime Switching. J Sci Comput 58, 409–430 (2014). https://doi.org/10.1007/s10915-013-9739-3
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DOI: https://doi.org/10.1007/s10915-013-9739-3