We present dynamic trading strategies that target a predefined level of risk measured by volatility, Value-at-Risk (VaR) or Conditional-Value-at-Risk (CVaR). Recent studies have shown that volatility targeting increases the risk-adjusted performance and heightens utility gains for mean-variance...

Backtesting risk measures represents a challenge and complex methods are often required. In this paper, we propose a new framework for backtesting that can be applied to every law invariant risk measures. We base our approach on the formalization of the concept of level of coverage associated...

Despite the use of VaR as a means to control risk, using VaR can have the opposite effect. VaR is used by bank and insurance regulators more than any other risk measure. A value-at-risk (VaR) constraint on the probability that future firm equity value will be less than a floor, when the floor is...

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...

In this study we hypothesise that more frequent extreme negative daily equity returns result in higher tail risk, and this subsequently increases firms' likelihood of entering financial distress. Specifically, we investigate the role of Value-at-risk and Expected Shortfall in aggravating firms'...

Under the Basel II regulatory framework non-negligible statistical problems arise when backtesting risk measures. In this setting backtests often become infeasible due to a low number of violations leading to heavy size distortions. According to Escanciano and Olmo (2010, 2011) these problems...

We introduce a class of quantile-based risk measures that generalize Value at Risk (VaR) and, likewise Expected Shortfall (ES), take into account both the frequency and the severity of losses. Under VaR a single confidence level is assigned regardless of the size of potential losses. We allow...

In this paper we propose a new measure for systemic risk: the Financial Risk Meter (FRM). This measure is based on the penalization parameter () of a linear quantile lasso regression. The FRM is calculated by taking the average of the penalization parameters over the 100 largest US publicly...

In this study we consider the risk estimation as a stochastic process based on the Sample Quantile Process (SQP) - which is a generalization of the Value-at-Risk calculated on a rolling sample. Using SQP's, we are able to show and quantify the pro-cyclicality of the current way financial...