This paper introduces a new computational tool for the analysis of the risks embedded in a set of prices of European-style options. The software enables the estimation of the risk-neutral density (RND) from the observed option prices by means of orthogonal polynomial expansions. Orthogonal...

We propose a non-structural method to retrieve the risk-neutral density (RND) impliedby options on the CBOE Volatility Index (VIX). The methodology is based on orthogonalpolynomial expansions around a kernel density and yields the RND of the underlyingasset without the need for a parametric...

Building on the Liquidity Coverage Ratio created under the Basel III regulatory agreement, this paper introduces the notion of Liquidity Coverage at Risk (LCRisk), which is the probability that a bank becomes insolvent in the next 30-days. LCRisk has a closed-form expression and it can be...

Financial risk managers routinely use non-linear time series models to predict the downside risk of the capital under management. They also need to evaluate the adequacy of their model using so-called backtesting procedures. The latter involve hypothesis testing and evaluation of loss functions....

We perform a large-scale empirical study to compare the forecasting performance of single-regime and Markov-switching GARCH (MSGARCH) models from a risk management perspective. We find that, for daily, weekly, and ten-day equity log-returns, MSGARCH models yield more accurate Value-at-Risk,...

In this paper we propose a new class of dynamic mixture models (DAMMs) being able to sequentially adapt the mixture components as well as the mixture composition using information coming from the data. The information driven nature of the proposed class of models allows to exactly compute the...