A flexible predictive density combination model is introduced for large financial data sets which allows for dynamic weight learning and model set incompleteness. Dimension reduction procedures allocate the large sets of predictive densities and combination weights to relatively small sets....

A flexible predictive density combination is introduced for large financial data sets which allows for model set incompleteness. Dimension reduction procedures that include learning allocate the large sets of predictive densities and combination weights to relatively small subsets. Given the...

A Bayesian dynamic compositional model is introduced that can deal with combining a large set of predictive densities. It extends the mixture of experts and the smoothly mixing regression models by allowing for combination weight dependence across models and time. A compositional model with...

The recent evolution of prudential regulation establishes a new requirement for banks and supervisors to perform reverse stress test exercises in their risk assessment processes, aimed at detecting default or near-default scenarios. We propose a reverse stress test methodology based on a...

In this research paper ARCH-type models and option implied volatilities (IV) are applied in order to estimate the Value-at-Risk (VaR) of a stock index futures portfolio for several time horizons. The relevance of the asymmetries in the estimated volatility estimation is considered. The empirical...

We consider forecast combination and, indirectly, model selection for VAR models when there is uncertainty about which variables to include in the model in addition to the forecast variables. The key di erence from traditional Bayesian variable selection is that we also allow for uncertainty...

Purpose: This paper examines the associative and causal relationship between changes in the implied volatility index (VIX) and stock market returns, with data from 15 countries representing both developed and emerging economies.1 We also examine the dynamic variation, if any in the nature of the...

A reflection on the lackluster growth over the decade since the Global Financial Crisis has renewed interest in preventative measures for a long-standing problem. Advances in machine learning algorithms during this period present promising forecasting solutions. In this context, the paper...

We propose a modeling framework which allows for creating probability predictions on a future market crash in the medium term, like sometime in the next five days. Our framework draws upon noticeable similarities between stock returns around a financial market crash and seismic activity around...

A general theoretical and empirical framework is developed for assessing the potential of a vertically integrated firm to foreclose downstream competitors. Using this framework a policymaker may also evaluate the empirical welfare effects from a vertically integrated firm raising rivals' costs....