A version of Chevet's theorem for stable processes

Let $\text{X}\text{}\text{?}\text{bo}\text{Y}$ be the injective tensor product of the separable Banach spaces X and Y and let S_X, S_Y and $\text{S}{}_{\mathrm{<mtext>X\; </mtext><mtext>\backslash </mtext><mtext>?</mtext><mtext>bo\; Y</mtext>}}$ be the unit spheres of these spaces. The tensor product of two symmetric finite measures η₁ on S_X and η₂ on S_Y, η₁?η₂, is defined in a natural way as a measure on $\text{S}{}_{\mathrm{<mtext>X\; </mtext><mtext>\backslash </mtext><mtext>?</mtext><mtext>bo\; Y</mtext>}}$. It is shown that η₁? η₂ is the spectral measure of a p-stable random variable W on $\text{X}\text{}\text{?}\text{bo}\text{Y}$, 0 <p < 2, if and only if η₁ and η₂ are the spectral measures of p-stable random variables U and V on X and Y, respectively. Actually upper and lower bounds for $\text{(E∥ W∥}{}^{\mathrm{r}}\text{)}{}^{\mathrm{<mtext>1</mtext><mtext>r</mtext>}}$ in terms of the random variables U and V are obtained. When X = C(S), Y = C(T) with S, T compact metric spaces, and η₁, and η₂ are discrete, our results imply that if θ_i, θ_ij are i.i.d. standard symmetric real valued p-stable random variables, 0 < p <2, x_i?C(S), and y_i?C(T), then the series ∑_ijθ_ijx_i(s) y_j(t) converges uniformly a.s. iff the series ∑_iθ_ix_i(s) and ∑_iθ_iy_i(t) both converge uniformly a.s. When p = 2 this follows from Chevet's theorem on Gaussian processes. Several examples are given. One of them requires an interesting upper bound on the probability distribution of the maximum of i.i.d. p-stable random variables taking values in a general Banach space.