# Process with covariance $E[Y_{t}Y_{s}]=a_{1}-a_{2}|t-s|$

We have a centered Gaussian process $$X_{t}$$ where we have exact equality $$E[X_{t}X_{s}]=a_{1}-a_{2}|t-s|$$ for $$|t-s|<\epsilon_{0}\ll \frac{a_{1}}{a_{2}}$$ and $$a_{i}>0$$.

Q: I am curious if there is any other concrete Gaussian process $$(Y_{s})_{s\in [0,\epsilon_{0}]}$$ out there with the same exact covariance when $$|t-s|<\epsilon_{0}$$ for some $$\epsilon_{0}>0$$ (not asymptotical behaviour with error term, but exact equality).

It will be interesting if $$Y_{t}$$ is in terms of some known process like a functional of Brownian motion or a stationary solution of some SDE.

We are not concerned with $$Y_{t}$$ having different distribution than $$X_{t}$$(even though they do looked as Gaussian processes over $$t\in [0,\epsilon']$$ for $$\epsilon'$$ small enough). Our main concern is if such covariances have been studied in the literature or if we can devise one.

Some idea: start from $$Y_{t}=\int_{0}^{t}f(r,t)dW_{r}$$ and try to find a deterministic $$f(r,t)$$ with the desired covariance: by Ito isometry $$\int_{0}^{s}f(r,s+h)f(r,s)ds=a_{1}-a_{2}h$$.

Our process

Let $$X_{\epsilon}(x)\sim N(0,\ln\frac{1}{\epsilon})$$ with covariance:

For simplicity above we suppressed the $$\epsilon$$ and just let $$X_{t}:=X_{\epsilon}(t)$$.

Our particular process. Consider the hyperbolic measure $$\lambda:=\frac{1}{y^{2}}dx dy$$ in the upper half-plane and a White noise process W indexed by Borel sets of finite hyperbolic area:

$$\{A\subset \mathbb{H}: \lambda(A)<\infty; \sup_{(x,y),(x',y')\in A}|x-x'|<\infty\}$$

with covariance:

$$E[W(A_{1})W(A_{2})]:=\lambda(A_{1}\cap A_{2}).$$

Then let $$X_{t}=W(V_{\epsilon}+t)$$ for

$$V_{\epsilon}:=\{(x,y)\in \mathbb{H}: x\in [-1/4,1/4]\text{ and }max(2|x|,\epsilon)\leq y<1/2\}.$$

By Pólya’s theorem, any even real-valued function $$f$$ on $$\mathbb R$$ with $$f(\infty-)=0$$ which is convex on $$[0,\infty)$$ is positive definite. So, any such function is the (auto)covariance function of a stationary Gaussian process; see e.g. Section "Properties of the Autocovariance Function", page 2.

Now just take any two different functions, $$f_1$$ and $$f_2$$, of the Pólya class such that $$f_2(t)=1-|t|=f_2(t)$$ for $$|t|\le1/2$$. Then the corresponding stationary Gaussian processes, say $$(X_{1,t})$$ and $$(X_{2,t})$$, with the covariance functions $$f_1$$ and $$f_2$$ will have different distributions. Therefore, these two processes will be different from each other.

To be more specific, note first here that, by vertical and horizontal re-scaling, without loss of generality $$a_1=a_2=1$$, so that $$EX_sX_t=1-|t-s|\quad\text{if}\quad|t-s|\le u, \tag{1}$$ where $$u\in(0,1)$$. Let then $$Y_t:=B_{t+1}-B_t=\int_t^{t+1}dB_s,$$ where $$(B_t)_{t\in\mathbb R}$$ is the standard Brownian motion with $$B_0=0$$. Then $$EY_sY_t=1-|t-s|\quad\text{if}\quad|t-s|\le 1$$ (with $$EY_sY_t=0$$ if $$|t-s|>1$$), so that $$EY_sY_t=EX_sX_t\quad\text{if}\quad|t-s|\le u,$$ as desired.

For more examples, take any $$h\in(0,1)$$ and let $$U_t:=\frac1{\sqrt2}\,(Y_{(1-h)t}+Z_{(1+h)t}),$$ where $$(Z_t)$$ is an independent copy of the Gaussian process $$(Y_t)$$. Then $$EU_sU_t=1-|t-s|=EY_sY_t \quad\text{if}\quad|t-s|\le1/(1+h)$$ and hence $$EU_sU_t=EX_sX_t \quad\text{if}\quad|t-s|\le\min[u,1/(1+h)],$$ as desired.

• Thank you Iosif. The focus of the question was on finding some known process with the above mentioned covariance. Also I didn't write $\leq u$, I wrote $<u$. The distribution comment was just auxiliary, not the focus of the question. Commented Apr 19, 2020 at 20:41
• If you like in your setting, you should think that the underlying interval we study is $[0,u]$ for $u<\frac{1}{2}$ and so we get the same distribution there by uniqueness of covariance for Gaussian processes. Commented Apr 19, 2020 at 20:47
• @OOESCoupling : Your question was "if there is any other Gaussian process $Y_s$ out there with the same exact covariance when $|t-s|<\epsilon_0$ for some $\epsilon_0>0$". This question was fully answered by just modifying the process you had with such a covariance and thus using no additional knowledge. Your comment about the distinction between $<u$ and $\le u$ looks strange to me, since the covariance function of any 2nd-order process continuous at 0 is continuous everywhere, by Bochner's theorem. Anyhow, now I have given another version of the answer, based on Pólya's theorem. Commented Apr 19, 2020 at 21:57
• Thank you Iosif. We are only looking at them as Gaussian processes restricted over $[0,\frac{1}{2}]$ ignoring the rest. Not as processes over the entire positive line, which one can devise many examples as you wrote. Commented Apr 19, 2020 at 22:30
• @OOESCoupling : You keep changing the question. It was "if there is any other Gaussian process [with certain general properties]". Now, after getting full answers to the previous versions of your questions, you change the question to "It will be interesting if $Y_t$ is in terms of some known process like a functional of Brownian motion or a stationary solution of some SDE." An appropriate thing to do in such situations is, not to invalidate valid answers, but to post the additional questions separately. Otherwise, what did I spend my time for? Commented Apr 20, 2020 at 0:13

By vertical and horizontal re-scaling, without loss of generality $$a_1=a_2=1$$, so that $$EX_sX_t=1-|t-s|\quad\text{if}\quad|t-s|\le u, \tag{1}$$ where $$u\in(0,1)$$. Take any $$h\in(0,1)$$ and let $$U_t:=\frac1{\sqrt2}\,(X_{(1-h)t}+Y_{(1+h)t}),$$ where $$(Y_t)$$ is an independent copy of your Gaussian process $$(X_t)$$. Then $$EU_sU_t=1-|t-s|=EX_sX_t \tag{2}$$ if $$|t-s|\le u/(1+h)$$, as desired.

Moreover, letting $$u$$ take the largest possible value such that (1) still holds, we will see that the first equality in (2) will fail to hold if $$|t-s|=u$$, which shows that the distribution of $$(U_t)$$ is different from that of $$(X_t)$$. Therefore, the process $$(U_t)$$ is different from the process $$(X_t)$$.