We show that the ability of oil price changes to predict stock returns is largely limited to five extreme geopolitical events: the 2022 invasion of Ukraine, the 2003 invasion of Iraq, the 1990/91 Persian gulf war, the 1986 OPEC collapse, and the 1973 Arab-Israel war. In the counterfactual...

We show that the ability of oil price changes to predict stock returns is largely limited to five extreme geopolitical events: the 2022 invasion of Ukraine, the 2003 invasion of Iraq, the 1990/91 Persian gulf war, the 1986 OPEC collapse, and the 1973 Arab-Israel war. In the counterfactual...

A traditional VaR approach is not suitable to assess the risk that merger arbitrage funds carry in their portfolios. We propose a simple two-state or three-state model that captures the risk characteristics of the deals in which merger arbitrage funds invest.This model has been tested on a set...

We present a model that captures risks of hedge funds only using their historical performance as input. This statistical model is a multivariate distribution where the marginals derive from an AR(1)/AGARCH(1,1) process with t_5 innovations, and the dependency is a grouped-t copula. The process...

A traditional VaR approach is not suitable to assess the risk of merger arbitrage hedge funds. We recently proposed a simple two- or three-state model that captures the risk characteristics of the deals in which merger arbitrage funds invest. Here, we refine the model, and demonstrate that it...

Finally, Stéphane Daul and Elena Gutíerrez Vidal present a set of case studies on insurance liability replication. Under modern valuation conventions, insurance firms increasingly use market-consistent techniques to value both their assets and liabilities. Consequently, the market...

We analyze the performance of investable portfolios built using predicted stock returns from machine learning methods and attribute their performance to linear, marginal non-linear and interaction effects. We use a large set of features including price-based, fundamental-based, and...

Recently many research articles have focused on the prediction of stock returns using machine learning methods. All show that regression trees and neural networks have superior predicting power than linear models. In this paper we analyze the performance of investable portfolios built using...

We describe a model that takes into account the tail dependence present in a large set of historical risk factor data using the modern concept of copulas. We extend the popular t-copula to obtain a new grouped t-copula which describes more accurately the dependence among risk factors of...