understanding diversification strategies. It introduces entropic value at risk (EVaR) as a coherent risk measure, which is an upper … bound to the conditional value at risk (CVaR), and explores its generalization, relativistic value at risk (RLVaR), rooted …, particularly in scenarios of heightened risk and increased concentration, crucial for mitigating negative net performances during …

performance, an array of risk-adjusted metrics, including Std Sharpe, ES Sharpe, VaR Sharpe, Information ratio, Sortino ratio …, Treynor ratio, and various downside risk metrics (historical VaR, modified VaR, Expected Shortfall, loss deviation, downside …-factor models highlight significant systematic market risk, reflected in notably high beta coefficients, negative alphas, and active …

We examine how sensitive the new performance indexes incorporating high moments and disaster risk are to disaster risk …. The new performance indexes incorporating high moments and disaster risk are the Aumann-Serrano performance index and … underlying risk. We show, by numerical examples and empirical examples, how sensitive these indexes are to disaster risk …

findings suggest that machine learning methods provide more accurate models of stock returns based on risk factors than … standard regression-based methods of estimation. They also indicate that certain risk factors and combinations of risk factors …

Evaluating risk measures, premiums, and capital allocation based on dependent multi-losses is a notoriously difficult … by a heavy-tailed background risk. A particular attention is given to the distortion and weighted risk measures and … allocations, as well as their special cases such as the conditional layer expectation, tail value at risk, and the truncated tail …

We explore a multi-asset jump-diffusion pricing model, combining a systemic risk asset with several conditionally … assets that works even if the data are incomplete and asynchronous. Alternatively, to find risk-neutral parameters, the least … propose a Laplace-transform-based approach to computing Value at Risk (VaR) and conditional VaR (also known as the expected …

investment decisions. The used risk attribution quantification models GARCH (1.1), EGARCH (1.1), GARCH-M (1.1) and TGARCH (1 ….1) are adapted to predict the volatility of investment funds. The current development focuses on forecasting the risk … of the models GARCH, EGARCH and GARCH-M with the highest risk concentration the investment fund "Golden Lev Index 30 …