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sparse grid method in the libor market model. option valuation and the

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Three different θ values represent three canonical discretization schemes. By sett<strong>in</strong>g<br />

θ = 0.5 for Crank-Nicolson, we shall first average <strong>and</strong> <strong>the</strong>n discrim<strong>in</strong>ate explicit <strong>and</strong><br />

implicit parts as follows:<br />

U m+1<br />

h,L<br />

− ∆τ<br />

2 W m+1<br />

h,L<br />

= Uh,L m + ∆τ<br />

2 W h,L m (4.11)<br />

As a result of such discretization we should arrive at Ax = b system of discrete stencil<br />

equations, where A is a M i ×· · ·×M d b<strong>and</strong> matrix of known coefficients, x is a vector<br />

of unknown solutions U m+1 <strong>and</strong> b is a vector of known values that correpond to <strong>the</strong><br />

right h<strong>and</strong> side of (4.11). The necessity to simultaneously solve a large system of<br />

discrete equations poses <strong>the</strong> dilemma of <strong>the</strong> choice of an efficient iterative solver, such<br />

as BiCGSTAB [van der Vorst, 1992] or Multi<strong>grid</strong> [Wessel<strong>in</strong>g, 2004].<br />

Stencil equation for 2 forward rates. Given <strong>the</strong> discrete equation for 2 forward<br />

rates, we can rearrange <strong>the</strong> terms of (4.11) to arrive at <strong>the</strong> follow<strong>in</strong>g stencil equality:<br />

−ψU m+1<br />

i−1,j−1 −βU m+1<br />

i−1,j ψU m+1<br />

i−1,j+1<br />

−ζU m+1<br />

i,j−1 (γ + 2)U m+1<br />

i,j −ηU m+1<br />

i,j+1<br />

ψU m+1<br />

i+1,j−1 −αU m+1<br />

i+1,j −ψU m+1<br />

i+1,j+1<br />

} {{ }<br />

unknowns<br />

where <strong>the</strong> known coefficients are:<br />

=<br />

ψUi−1,j−1 m βUi−1,j m −ψUi−1,j+1<br />

m<br />

ζUi,j−1 m −γUi,j m ηUi,j+1<br />

m<br />

−ψUi+1,j−1 m }<br />

αUi+1,j m {{<br />

ψUi+1,j+1<br />

m }<br />

knowns<br />

α = ∆τσ2 i (m∆τ)L2 i<br />

4h 2 i<br />

β = ∆τσ2 i (m∆τ)L2 i<br />

4h 2 i<br />

γ = ∆τσ2 i (m∆τ)L2 i<br />

2h 2 i<br />

+ ∆τµi(m∆τ)Li<br />

4h i<br />

− ∆τµ i(m∆τ)L i<br />

4h i<br />

+ ∆τσ2 j (m∆τ)L2 j<br />

2h 2 j<br />

ψ = ∆τρ ijσ i (m∆τ)σ j (m∆τ)L i L j<br />

8h ih j<br />

ζ = ∆τσ2 j (m∆τ)L2 j<br />

4h 2 j<br />

η = ∆τσ2 j (m∆τ)L2 j<br />

4h 2 j<br />

− ∆τµ j(m∆τ)L j<br />

4h j<br />

+ ∆τµ j(m∆τ)L j<br />

4h j<br />

− 1<br />

<strong>and</strong> <strong>the</strong> b<strong>and</strong> coefficient matrix A hav<strong>in</strong>g <strong>the</strong> same pattern as Figure (4.3).<br />

Forward rate expiry. While solv<strong>in</strong>g (4.9), we should keep <strong>in</strong> m<strong>in</strong>d that <strong>the</strong> lifetime<br />

of any given forward rate is bounded by its fix<strong>in</strong>g date on <strong>the</strong> tenor structure. Has such<br />

maturity of an underly<strong>in</strong>g rate been reached, <strong>the</strong> dimensionality of orig<strong>in</strong>al problem<br />

reduces by one. The simpliest <strong>and</strong> most natural way to <strong>in</strong>troduce such behavior <strong>in</strong>to<br />

<strong>valuation</strong> process is to reset forward rate’s volatility value to zero after it has matured.<br />

Therefore, by solv<strong>in</strong>g LMM PDE backwards <strong>in</strong> time, we gradually <strong>in</strong>crease <strong>the</strong> number<br />

of rates that are sett<strong>in</strong>g <strong>in</strong> motion <strong>the</strong> numerical mechanism of <strong>option</strong> <strong>valuation</strong>.<br />

Notation σ(m∆τ) <strong>and</strong> µ(m∆τ) is meant to serve as a rem<strong>in</strong>der of this feature.<br />

35

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