Abstract
We study the Langevin equation with stationary-increment Gaussian noise. We show the strong consistency and the asymptotic normality with Berry–Esseen bound of the so-called second moment estimator of the mean reversion parameter. The conditions and results are stated in terms of the variance function of the noise. We consider both the case of continuous and discrete observations. As examples we consider fractional and bifractional Ornstein–Uhlenbeck processes. Finally, we discuss the maximum likelihood and the least squares estimators.
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T. Sottinen was partially funded by the Finnish Cultural Foundation (National Foundations’ Professor Pool). L.Viitasaari was partially funded by the Emil Aaltonen Foundation.
Appendix: Proofs of Lemmas
Appendix: Proofs of Lemmas
Proof of Lemma 2.1
Let \(t>0\) and let \(\lfloor t\rfloor \) be the greatest integer not exceeding t. Then
By the Minkowski’s inequality and stationary of the increments,
The claim follows from this.\(\square \)
Proof of Lemma 3.1
By changing the variable in (2.4) we obtain
Since v is strictly increasing, this shows that \(\psi \) is also strictly decreasing. Furthermore, \(\psi (\theta )\rightarrow 0\) as \(\theta \rightarrow \infty \) by the monotone convergence theorem.
By the Lebesgue’s dominated convergence theorem, the function
is smooth. Consequently, \(\psi \) is smooth.
Finally, let us show that \(\psi \) is convex. Differentiating \(\psi \) we observe
from which it follows, by integration by parts, that
where \(\mathrm {d}v\) is the measure associated to the increasing function v. Similarly, differentiating and using integration by parts again we get
Consequently, \(\psi \) is convex.\(\square \)
Proof of Lemma 3.2
Let \(T\ge 0\) be fixed. Then, by Sottinen and Viitasaari (2016, Theorem 12), there exists a Brownian motion W and a kernel \(K_T\in L^2([0,T]^2)\) such that we have the representation
for all \(t\le T\).
Consequently \(X_t\) belongs to the 1st Wiener chaos for all \(t>0\). Then note that
Consequently, it belongs to the 2nd Wiener chaos. Finally, note that, because \(\gamma \) is continuous, the integral
can be defined as a limit in \(L^2(\Omega )\) in the 2nd Wiener chaos. The claim follows from this.\(\square \)
Proof of Lemma 3.3
The claim is an obvious consequence of Isserlis’ theorem.\(\square \)
Proof of Lemma 3.4
By the symmetry \(\gamma (s,t)=\gamma (t,s)\)
\(\square \)
Proof of Lemma 3.5
Note that \(Q_T/\sqrt{\mathbf {E}[Q_T^2]}\) belongs to the 2nd Wiener chaos and has unit variance. Consequently, by the fourth-moment Proposition 3.2, it suffices to show that
Denote
Then, by Lemma 3.3,
and, by Lemma 3.4
Consequently,
The claim follows from this.\(\square \)
Proof of Lemma 3.6
We have
By Proposition 2.2 and ergodicity condition \(\frac{1}{T}\int _0^T |r(t)|\mathrm {d}t \rightarrow 0\) it follows that
Moreover, by symmetry, change-of-variables and the Fubini theorem
Consequently, by assuming that \(T> 1\),
This shows \(T w_\theta (T)\ge C\) which, together with (6.2) and (6.3), implies \(\mathbf {E}[(Q^\theta _T)^2] \sim w_\theta (T)\). Also, we have shown the equality
Consider then the case \(\int _0^\infty r_\theta (t)^2\, \mathrm {d}t < \infty \). By the equivalence \(\mathbf {E}[(Q^\theta _T)^2] \sim \frac{4}{T^2}\int _0^T r_\theta (t)^2(T-t)\, \mathrm {d}t\), we have
Here the first term converges to \(\int _0^\infty r_\theta (t)^2\, \mathrm {d}t < \infty \). For the second term we have
which completes the proof.\(\square \)
Proof of Lemma 3.7
Let us first split
For the term \(A_1\), let us first estimate
Consequently,
Since we are interested in the case \(T\rightarrow \infty \), we can assume that T is bigger than some absolute positive constant. Consequently, since \(r_\theta \) continuous with \(r_\theta (0)>0\), it follows from the estimate above that
Therefore, by applying Lemmas 3.5 and 3.6, it follows that \(A_1\le C_\theta R_\theta (T)\).
Let us then consider the term \(A_2\). Now, by the mean value theorem,
where
Since \(\sqrt{\frac{w_\theta (T)}{\mathbf {E}[(Q^\theta _T)^2]}} \sim 1\), it follows that
Consequently, by the asymptotic equivalence of \(w_\theta (T)\sim \mathbf {E}[(Q^\theta _T)^2]\), it remains to show that
(Actually, we show that the left hand side is bounded.) For this purpose, we estimate, by using the inequality \(|\sqrt{a}-\sqrt{b}| \le \sqrt{|a-b|}\) and the identity \(a^2-b^2=(a+b)(a-b)\), that
By applying Proposition 2.2 to the estimate above, we obtain
Now, \(|r_\theta (t-s)|\le r_\theta (0)\) and \(|\gamma _\theta (t,s)|\le r_\theta (0) +1\), by Proposition 2.2. Consequently, the integral above is bounded, and the proof is finished.\(\square \)
Proof of Lemma 4.1
Let
Then
By the Taylor’s theorem
for some \(\xi (t) \in [a(t), a(t)+1]\) and \(\eta (t)\in [a(t)-1,a(t)]\). Consequently,
which shows the exponential decay.\(\square \)
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Sottinen, T., Viitasaari, L. Parameter estimation for the Langevin equation with stationary-increment Gaussian noise. Stat Inference Stoch Process 21, 569–601 (2018). https://doi.org/10.1007/s11203-017-9156-6
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DOI: https://doi.org/10.1007/s11203-017-9156-6