Eigenvalue statistics of Elliptic Volatility Model with power-law tailed volatility

In this paper we study an ensemble of random matrices called Elliptic Volatility Model, which arises in finance as models of stock returns. This model consists of a product of independent matrices $X=\mathrm{\Sigma }Z$ where $Z$ is a $T$ by $S$ matrix of i.i.d. light-tailed variables with mean 0 and variance 1 and $\mathrm{\Sigma }$ is a diagonal matrix. In this paper, we take the randomness of $\mathrm{\Sigma }$ to be i.i.d. heavy tailed. We obtain an explicit formula for the empirical spectral distribution of $X^{\ast }X$ in the particular case when the elements of $\mathrm{\Sigma }$ are distributed as Student’s t with parameter 3. We furthermore obtain the distribution of the largest eigenvalue in more general case, and we compare our results to financial data.