Abstract
In this paper, we will first give the numerical simulation of the sub-fractional Brownian motion through the relation of fractional Brownian motion instead of its representation of random walk. In order to verify the rationality of this simulation, we propose a practical estimator associated with the LSE of the drift parameter of mixed sub-fractional Ornstein–Uhlenbeck process, and illustrate the asymptotical properties according to our method of simulation when the Hurst parameter \(H>1/2\).
Similar content being viewed by others
References
Gatheral, J., Jaisson, T., Rosenbaum, M.: Volatility is rough. Quant. Finance 18(6), 933–949 (2018)
Euch, O., Rosenbaum, M.: Perfect hedging under rough Heston models. Ann. Appl. Probab. 28(6), 3813–3856 (2018)
Euch, O., Rosenbaum, M.: The characteristic function of rough Heston models. Math. Finance 29(1), 3–38 (2019)
Euch, O., Fukasawa, M., Rosenbaum, M.: The microstructural foundations of leverage effect and rough volatility. Finance Stoch. 22(2), 241–280 (2018)
Liu, J., Li, L., Yan, L.: Sub-fractional model for credit risk pricing. Int. J. Nonlinear Sci. Numer. Simul. 11(4), 231–236 (2010)
Morozewicz, A., Filatova, D.: On the simulation of sub-fractional Brownian motion. In: 20th International Conference on Methods and Models in Automation and Robotics (2015)
Cai, C., Chigansky, P., Kleptsyna, M.: Mixed Gaussian process: a filtering approach. Ann. Probab. 44(4), 3032–3075 (2016)
El Machkouri, M., Es-Sebaiy, K., Ouknine, Y.: Least squares estimator for non-ergodic Ornstein–Uhlenbeck processes driven by Gaussian processes. J. Korean Stat. Soc. 45, 329–341 (2016)
Zili, M.: Mixed sub-fractional Brownian motion. Random Oper. Stoch. Equ. 22(3), 163–178 (2013)
Paxson, V.: Fast, approximate synthesis of fractional Gaussian noise for generating self-similar network traffic. ACM SIGCOMM Comput. Commun. Rev. 27(5), 5–18 (1997)
Nualart, D.: The Malliavin Calculus and Related Topics, 2nd edn. Springer, Berlin (2006)
Biagini, F., Hu, Y., Øksendal, B., Zhang, T.: Stochastic Calculus for Fractional Brownian Motion and Application. Springer, Berlin (2008)
Chigansky, P., Kleptsyna, M.: Statistical analysis of mixed fractional Ornstein–Uhlenbeck process. Theory Probab. Appl. 63(3), 500–519 (2018)
Hu, Y., Nualart, D.: Parameter estimation for fractional Ornstein–Uhlenbeck processes. Stat. Probab. Lett. 80, 1030–1038 (2010)
Hu, Y., Nualart, D., Zhou, H.: Parameter estimation for fractional Ornstein–Uhlenbeck processes of general Hurst parameter. Stat. Inference Stoch. Process. 22, 111–142 (2019)
Veillette, M., Taqqu, M.: Properties and numerical evaluation of the Rosenblatt distribution. Bernoulli 19(3), 982–1005 (2013)
Cheridito, P., Kawaguchi, H., Maejima, M.: Fractional Ornstein–Uhlenbeck processes. Electron. J. Probab. 8, 1–14 (2003)
Chen, Y., Hu, Y., Wang, Z.: Parameter estimation of complex fractional Ornstein–Uhlenbeck processes with fractional noise, ALEA. Lat. Am. J. Probab. Math. Stat. 14, 613–629 (2017)
Hu, Y.: Analysis on Gaussian Process. World Scientific Publishing Co. Pte. Ltd., Hackensack, NJ (2017)
Nualart, D., Ortiz-Latorre, S.: Central limit theorems for multiple stochastic integrals and Malliavin calculus. Stoch. Process. Appl. 118, 614–628 (2008)
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Funding
Chunhao Cai is supported by the Fundamental Research Funds for the SUFE No. 2020110294. Weilin Xiao is supported by the National Natural Science Foundation of China, Grant No. 71871202.
Appendix
Appendix
1.1 Proof of Main Theorem
In this part, we will prove the main results of Theorem 3.3 and Theorem 3.4. First of all, let us introduce the following lemma.
Lemma 6.1
Let \(S_t^H\) be a sub-fractional Brownian motion. Then, we have
and
Proof
In fact
when for fixed real number \(t,s\ge 0\), we have \(|t+s|^{2H-2}\le |t-s|^{2H-2}\) we have
From the web only Lemma 5.4 of [14] we know
which achieves the proof of (6.1) and the same for (6.2). \(\square \)
The following lemma plays a key role in the proof of Theorem 3.4 when in the norm \(\Vert \cdot \Vert _{{\mathcal {H}}}\) we have to calculate the inner product with respect to the standard Brownian motion.
Lemma 6.2
For \(H>1/2\), we have
Proof
The two limits
complete the proof.\(\square \)
1.1.1 Proof of Theorem 3.3
The result in [14] gives the strong consistency for the LSE from the ergodicity. Since the increment of the sfBm is not stationary, we cannot use the ergodicity to prove the consistency of \({\bar{\vartheta }}_T\). Now, a standard calculation yields
On the other hand, a straightforward calculation shows that
With the L’Hôspital’s rule, we have
Now we only need to prove that
For \(0\le t\le T\), let \(W_t\) be a standard Brownian motion and \(X_t=X_t^{(1)}+X_t^{(2)}+X_t^{(3)}\). Then, we have
and
With the ergodic property of these three processes presented in [17], we have the following results:
and
so (6.4) needs this result:
First of all
when \(|u+s|^{2H-2}\le (us)^{H-1}\) for \(u,s\ge 0\), then with L’Hôpital rule we have the following inequality:
With this inequality, we can easily obtain the convergence in probability
From (6.7), Borel–Cantelli lemma, we can easily obtain the convergence almost surely in (6.5) for \(1/2<H<3/4\). For the case \(3/4\le H<1\), we will apply the method of The Theorem 2.1 in [18]. In fact with the convergence in probability means there exists a sub-sequence which converges almost surely to 0; on the other hand, the equation (6.7) verifies the condition of second-order Winer-Ito chaos in Proposition 3.4 of [18], together with GRR inequality (see Theorem 2.1 in [19]) (6.5) will be achieved.
1.1.2 Proof of Theorem 3.4
Step 1: We shall use Malliavin calculus and the fourth moment theorem (see, for example, Theorem 4 in [20]) to prove (3.4).
In fact, using (3.2), we have
where \(F_T\) is the double stochastic integral
By (6.4), we know that \(\frac{1}{T}\int _0^T X_t^2dt\) converges in probability and in \(L^2\) as T tends to infinity to \(\frac{1}{2\vartheta }+H\vartheta ^{-2H}\Gamma (2H)\). From Theorem 4 of [20], we have to check the following two conditions:
-
(i). \({\mathbf {E}}(F_T^2)\) converges to a constant as T tends to infinity
$$\begin{aligned} \lim _{T\rightarrow \infty } {\mathbf {E}}F_T^2= & {} \vartheta ^{1-4H}H^2(4H-1)\left( \Gamma (2H)^2 +\frac{\Gamma (2H)\Gamma (3-4H)\Gamma (4H-1)}{\Gamma (2-2H)}\right) \\&+\frac{1}{2\vartheta }. \end{aligned}$$ -
(ii). \(\Vert DF_T\Vert _{{\mathcal {H}}}^2\) converges in \(L^2\) to a constant as T tends to infinity.
We first check the condition (i). When \(W_t\) and \(S_t^H\) are independent, we have \({\mathbf {E}}F_T^2={\mathbf {E}}\left( F_{1,T}^2+F_{T,2}^2\right) \), with \(F_{1,T}=\frac{1}{2\sqrt{T}}\int _0^T\int _0^T e^{-\vartheta |t-s|}\mathrm{d}W_t\mathrm{d}W_s,\,\, F_{2,T}=\frac{1}{2\sqrt{T}}\int _0^T\int _0^T e^{-\vartheta |t-s|}\mathrm{d}S_t^H\mathrm{d}S_s^H\).
A standard calculation together with (2.5) yields
From [14], we have
A simple calculation yields
Now, if we can prove
then the last three terms of \({\mathbf {E}}F_{2,T}^2\) will tend to zero with the fact \(|s_2+s_1|^{2H-2}\le |s_2-s_1|^{2H-2}\).
Denote
Using the L’Hôspital’s rule, we have
Let \(T-s_2=x_1, T-s_1=x_2, T-u_1=x_3\). Ignoring the sign, we have
Let \( J_T=\int _{[0,T]^3}e^{\vartheta y_1-\vartheta |x_2-x_3|}\left( x_3^{2H-2}y_1^{2H-2}\right) \mathrm{d}y_1\mathrm{d}x_2\mathrm{d}x_3\). Then, using the L’Hôspital’s rule, we get
On the other hand, we can easily obtain \(\frac{de^{-\vartheta T}}{dT}=-\vartheta e^{-\vartheta T}\). Moreover, with the L’Hôspital’s rule, it is easy to check that
which implies the Eq. (6.12).
Consequently, with (6.12) and the fact \(|s_2+s_1|^{2H-2}\le |s_2-s_1|^{2H-2}\), it is easy to see that
Combining (6.10), (6.11), (6.12) with (6.13), we verify condition (i).
Now we will check the condition (ii). For \(s\le T\), we have
From (2.5) we have
We first consider the first term of the above equation. A straightforward calculation shows that
From the proof of Theorem 3.3, the independence of the \(W_t\) and \(S_t^H\) in the msfBm, the convergence to 0 for the standard Brownian motion case in the proof of Theorem 3.4 of [14], the ergodicity and stationary of the fractional O-U process (see [17]) and Lemma 6.2, we can easily obtain that all these three terms converge in \(L^2\) as T tends to infinity.
Now let us look at the third term of \(\Vert D_sF_T\Vert _{{\mathcal {H}}}^2\). A standard calculation yields
where
and
With the same method of Theorem 3.4 in [14], Lemma 6.1 and the independence of the \(W_t\) and \(S_t^H\) in msfBm, we have
then we have
which implies that \(\lim \limits _{T\rightarrow \infty }{\mathbf {E}} C_T\) exists. Finally, we obtain that \(C_T\) converges in \(L^2\) to a constant. Thus, condition (ii) satisfies.
Step 2: Case \(H=3/4\). From (3.2) we have
where \(F_T\) is defined by (6.9).
We still use the fourth moment theorem (see, for example, Theorem 4 in [20]) and check two conditions of Step 1. Using same calculations of Step 1, we can show that
and
On the other hand, a straightforward calculation shows that
Then, we have
where the equality comes from Lemma 6.6 in [15].
Thus, condition (i) and condition (ii) are obvious when we add a term of \(\frac{1}{\sqrt{\log T}}\) and \(T^{8H-6}=1\) with \(H=\frac{3}{4}\).
Step 3: In this step we will prove the theorem when \(3/4<H<1\). From (3.2), we have
Let us mention that the condition (ii) in Step 1 will not be satisfied when \(H>3/4\). Fortunately, we still have the following convergence:
and
With the similarity of the process \(\xi \) and Lemma 6.6 in [15], we have
which achieves the proof.
Rights and permissions
About this article
Cite this article
Cai, C., Wang, Q. & Xiao, W. Mixed Sub-fractional Brownian Motion and Drift Estimation of Related Ornstein–Uhlenbeck Process. Commun. Math. Stat. 11, 229–255 (2023). https://doi.org/10.1007/s40304-021-00245-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40304-021-00245-8