Testing for One-Factor Models versus Stochastic Volatility Models



(i)d Define Zn,m = maxj=1 ,...,J Zn,m,rj and ZM = maxj∙=1 ,...,J ZMrj , where 0 < r 1 < ... <

rj-1 <rj < . . . < rJ < 1, for j =1, . . . , J, with J arbitrarily large but finite. If, as

n,m, ξn 1 → ∞, n → ∞ and n 0, and, for any ε > 0 arbitrarily small, m/n 1 ε 0,

then

Zn,m -→ ZM,

with

ZMr1

ZMr 2

.

.

.

ZMrJ


0,


VM(r1,r1)

VM(r2,r1)


VM(rJ,r1)


VM(r1,r2)

VM(r2,r2)

.

.

.

VM(rJ ,r2)


VM(r1,rJ)
VM ( r 2 ,r J )

.

.

.

VM (rJ,rJ)


(8)


where that, r,r',

VM (r, r')


VM ( r', r) = 2 σ4


( a ) LX (min( r, r' ) ,a )d a.


(ii) Under HA, if, as n, m, ξn-1 →∞, n →∞and n2 0, and if m/n π 0, then,

pointwise in r (0, 1],


Pr ω : -ɪ=
m


1,


where ς(ω) > 0 for all ω Ω+, where Ω+ is defined as in the statement of Ha.

Notice that, as shown in the proof in the Appendix, under the alternative hypothesis, and in
the case where
ft is a one-dimensional process, the dominant term of the proposed statistic is
a scaled version of the absolute value of the difference between the local times of
Xt and ft . If
instead
ft is a multidimensional process, then the multivariate local time analogue of the Lf (1, a)
used in Theorem 1 is not defined, but it can still be interpreted as a occupation density of the
multivariate diffusion
ft (see e.g. Geman and Horowitz, 1980 and Bandi and Moloche, 2001).
Therefore, in both cases, there exists an (almost surely) strictly positive random variable
ς, such
that (1
/m) Zm,n,r| ≥ ς, with probability approaching one.

The following Corollary considers the case where r = 1, i.e. when we use the whole span of
data in constructing the test statistic.

Corollary 1. Let Assumption 1 hold. Under H0, if, as n, m, ξn-1 →∞, n →∞ and n2 0,
and, for any
ε > 0 arbitrarily small, m/n 1 0, then

Zn,m,1 -d MN 0,2 σ4 (a) LX (1,a)da .



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