We propose a machine learning framework to capture the dynamics of high-frequency limit
order books in financial equity markets and automate real-time prediction of metrics such as …

It is well-established that equity returns are not Normally distributed, but what should the
portfolio manager do about this, and is it worth the effort? It is now feasible to employ better …

We examine certain Generalized Hyperbolic (GH) distributions for modeling equity returns,
compared to usual Normal distributions. We describe these GH distributions and some of …

Portfolio credit derivatives, such as basket credit default swaps (basket CDS), require for their
pricing an estimation of the dependence structure of defaults, which is known to exhibit tail …

We use a generalized birth–death stochastic process to model the high-frequency dynamics
of the limit order book, and illustrate it using parameters estimated from Level II data for a …

Purpose – The purpose of this paper is to describe a generalization of the familiar two‐sample
t‐test for equality of means to the case where the sample values are to be given unequal …

Estimation of the covariance of a high-dimensional returns vector is well-known to be
impeded by the lack of long data history. We extend the work of Goldberg, Papanicolaou, and …

Recent research identifies and corrects bias, such as excess dispersion, in the leading
sample eigenvector of a factor-based covariance matrix estimated from a high-dimension low …

Given a collection of single-market covariance matrix forecasts for different markets, we
describe how to embed them into a global forecast of total risk. We do this by starting with any …

This chapter is about forecasting risk. The vague word ‘‘risk’’refers to the degree of future
variability of a quantity of interest, such as price return. A risk model is a quantitative approach …