Since 2008’s financial crisis, risk management has focused in extreme market movements, i.e. low probability but high impact financial returns. This requires to precisely know the far tails of the probability distribution function underlying the returns’ generation process. Extreme values...

This paper characterizes the probability of a market failure defined as the default of two or more globally systemically important banks (G-SIBs) in a small interval of time. The default probabilities of the G-SIBs are correlated through the possible existence of a market-wide stress event. The...

It is now an accepted fact that the majority of financial markets worldwide are neither normal nor constant, and South Africa is no exception. One idea that can be used to understand such markets and has been gaining popularity recently is that of regimes and regime-switching models. In this...

The performance of dynamic trading and investment strategies can be difficult to predict. Although not without its problems, analysis of the historical performance of a strategy can provide valuable insight into its general risk and return properties. Furthermore, historical analysis allows one...

We propose and backtest a multivariate Value-at-Risk model for financial returns based on Tukey's g-and-h distribution. This distributional assumption is especially useful if (conditional) asymmetries as well as heavy tails have to be considered and fast random sampling is of importance. To...

This paper derives -- considering a Gaussian setting -- closed form solutions of the statistics that Adrian and Brunnermeier (2010) and Acharya et al. (2009) have suggested as measures of systemic risk to be attached to individual banks. The statistics equal the product of statistic specific...

We introduce a new model for time-varying spatial dependence. The model extends the well-known static spatial lag model. All parameters can be estimated conveniently by maximum likelihood. We establish the theoretical properties of the model and show that the maximum likelihood estimator for the...

We introduce a new meaure of risk appetite in financial markets, based on the cross sectional behavior of excess returns. Turning them into probabilities through a Markov Switching model, we define one global risk appetite measure as the cross-sectional average of the individual probabilities...