Downside return risk, or the covariance between stock returns and the market in bad states, performs significantly better compared to the single factor CAPM model. I construct a general equilibrium model and show that the same holds using firm policies rather than stock returns. Specifically,...

Risk evaluation is a forecast, and its validity must be backtested. Probability distribution forecasts are used in this work and allow for more powerful validations compared to point forecasts. Our aim is to use bivariate copulas in order to characterize the in-sample copulas and to validate...

We study the impact of regulations on the investment decisions of a defined benefits pension plan. We assess the influence of ex ante (preventive) and ex post (punitive) risk constraints on the gains to dynamic, as opposed to myopic, decision making. We find that preventive measures, such as...

This paper proves that the (negative) certainty equivalent (CE) in reference-dependent decision theories (such as Prospect Theory) always satisfies the well-known axiomatic characterisation of a monetary risk measure, although in rational Expected Utility Theory this only holds in special cases....

Average Value-at-Risk (AVaR) is a potential alternative to Value-at-Risk in the financial regulation of banking and insurance institutions. To understand how AVaR influences a company's investment behavior, we study portfolio optimization under the AVaR constraint. Our main contribution is to...

We derive upper and lower bounds for the Range Value-at-Risk of a unimodal random variable under knowledge of the mean, variance, symmetry, and a possibly bounded support. Moreover, we provide a generalization of the Gauss inequality for symmetric distributions with known support

In this paper, we leverage on real-world stock market data including the relatively calm period between 2012 and 2020, the COVID-19 shock in early 2020 and the recovery during its aftermath, to investigate asset allocation strategies with risk constraints defined in terms of optimal expected...

Regular or automated processes require reliable software applications that provide accurate volatility and Value-at-Risk forecasts. The univariate and multivariate GARCH models proposed in the literature are reviewed and the suitability of selected R functions for automated forecasting systems...