Eigenvalues of random Hamiltonian matrices A real $2n\times 2n$ Hamiltonian matrix has the general form
$$H=\begin{pmatrix}
A & B \cr
C & -A^T
\end{pmatrix}
$$
where $A$, $B$ and $C$ are $n\times n$ matrices, and $B$ and $C$ are symmetric. Are there any results regarding the eigenvalue distribution of an ensemble of such matrices? For example, the above condition is equivalent to the symmetry of $JH$ with
$$
J=\begin{pmatrix}
0 & 1 \cr
-1 & 0
\end{pmatrix},
$$
so it would be natural to consider the Gaussian orthogonal ensemble for $JH$. Generically, the eigenvalues come in quadruples: $\lambda, -\lambda, \bar\lambda, -\bar\lambda$
 A: The exponent $M=e^H\ $ of a Hamiltonian matrix $H$ is a symplectic matrix. So you might equivalently ask for the distribution of the eigenvalues $\xi=e^{\lambda}\ $ of $M$. There is an extensive literature on this in random matrix theory, I give some pointers below. 
The random symplectic matrix $M$ appears as the transfer matrix for the wave equation of a disordered medium, with many applications in optics and electronics. The most natural ensemble for the transfer matrix is inherited from the circular ensemble of the scattering matrix $S$ for the same wave equation. There is a one-to-one algebraic relation between the symplectic transfer matrix $M$ and the unitary scattering matrix $S$. The random matrix ensemble for $S$ is the familiar circular ensemble.
Here are some references to papers on random transfer matrix ensembles. (Notice that the word Hamiltonian has a different meaning in these papers.) 


*

*S. Bachmann, M. Butz, W. De
Roeck, Disordered
quantum wires: microscopic origins
of the DMPK theory and Ohm's law
(2012).

*M. Caselle, U. Magnea, Random
    matrix theory and symmetric
    spaces (2004).

*J. An, Z. Wang, K. Yan, A
    generalization of random matrix
    ensembles (2005).

*P. Devillard, Statistics
of transfer matrices for disordered
quantum thin metallic slabs
(1991).

A: In the course of a physics project in my group, I have had an opportunity to learn more about the eigenvalue statistics of Hamiltonian matrices. (Our physics problem actually involved skew-Hamiltonian matrices, so I made a small detour, joined by Jonathan Edge & Jan Dahlhaus.)
The ensemble is the one you suggested: $2n\times 2n$ real matrices $H$ with Hamiltonian symmetry and normally distributed elements. It is convenient to rescale the eigenvalues $\varepsilon_k$ of $H$ by a factor $\sqrt{2n}$, and separate the real and imaginary parts:
$(2n)^{-1/2}\varepsilon_k=x_k+iy_k$.
The eigenvalue density in the complex plane $x+iy$ consists of three parts: a two-dimensional density $\rho_{c}(x,y)$ of the complex eigenvalues, a one-dimensional density $\rho_{r}(x)$ of the real eigenvalues and another one-dimensional density $\rho_{i}(y)$ of the imaginary eigenvalues.
Based on numerical experiments, I can offer three conjectures:
1) For large $n$, the rescaled complex eigenvalues $x_k+iy_k$ uniformly cover a disc of unit radius, 
$\lim_{n\rightarrow\infty}n^{-1}\rho_{c}(x,y)=2/\pi$ for $x^2+y^2<1$.
2) For large $n$, the rescaled real eigenvalues $x_k$ uniformly cover the interval $-1<x<1$, with density 
$\lim_{n\rightarrow\infty}n^{-1/2}\rho_{r}(x)=1/\sqrt{\pi}$. 
Therefore the expectation value of the number $n_{r}$ of real eigenvalues satisfies $\lim_{n\rightarrow\infty}n^{-1/2}E[n_{r}]=2/\sqrt{\pi}$.
3) Also the rescaled imaginary eigenvalues $iy_{k}$ have a uniform density in the large-$n$ limit, in the interval $-1<y<1$, but this density is less than the density of the real eigenvalues. The expectation value of the number $n_{i}$ of imaginary eigenvalues satisfies $\lim_{n\rightarrow\infty}n^{-1/2}E[n_{i}]={\rm constant}\approx 0.72$.
Conjectures 1 and 2 were proven by Edelman and collaborators in the absence of Hamiltonian symmetry, so when all $(2n)^{2}$ real matrix elements of $H$ are chosen from independent normal distributions. [This is known as the (real) Ginibre ensemble.] Our numerics suggests that, for large matrices, the Hamiltonian symmetry only affects the (rescaled) eigenvalue distribution within a distance of order $n^{-1/2}$ from the imaginary axis.
By way of illustration, I include a plot of the eigenvalues $\varepsilon$ of $200$ real matrices of size $100\times 100$ (so $n=50$), with normally distributed matrix elements, both with the Hamiltonian symmetry (left) and without (right). These eigenvalues are shown without rescaling, so they cover a disc of radius $\sqrt{2n}=10$.

