Despite its wide use, the Hill estimator and its plot remain to be difficult to use in Extreme Value Theory (EVT) due to substantial sampling variations in extreme sample quantiles. In this paper, we propose a new plot we call the eigenvalue plot which can be seen as a generalization of the Hill plot. The theory behind the plot is based on a heavy-tailed parametric distribution class called the scaled Log phase-type (LogPH) distributions, a generalization of the ordinary LogPH distribution class which was previously used to model insurance claims data. We show that its tail property and moment condition are well aligned with EVT. Based on our findings, we construct the eigenvalue plot from fitting a shifted PH distribution to the excess log data with a minimal phase size. Through various numerical examples we illustrate and compare our method against the Hill plot.

We examine the roles of two financial intermediaries, lenders and venture capitalists, in a sample of more than 6000 IPO firms during 1980–2012. Venture capitalists and lenders generally fund different types of firms and, on average, are substitutes; however, in some instances we observe interactions and complementary roles between the two funding sources. Firms with high debt have lower valuation uncertainty, and lower initial day returns than those backed by venture capital. However, firms with high debt levels underperform in the long-run, especially those without venture capital. We provide some evidence that firms backed by reputable venture capitalists perform better.

Multi-asset class, multimarket central counterparties (CCPs) are becoming less uncommon as a result of merges between specialized (single-asset class, single market) CCPs and market demands for greater capital efficiency. Yet, traditional CCP risk management models often lack the necessary sophistication to estimate potential losses relative to the closeout process of a defaulter’s portfolio in a multi-asset class, multimarket environment. As a result, multi-asset class, multimarket CCPs usually rely on a simplified silo approach for risk calculation which not only fails to deliver efficiency, but may also increase systemic risk. The CORE (Closeout Risk Evaluation) approach, on the other hand, provides conceptual and mathematical tools necessary for robust and efficient central counterparty risk evaluation in multi-asset class and multimarket environments, acknowledging the portfolio dynamics involved in the closeout process as well as important “real life” market frictions.