Risk evaluation is a forecast, and its validity must be backtested. Probability distribution forecasts are used in this … correlation), and that the bivariate forecasts provided by a risk methodology based on historical innovations performs correctly …

In the aftermath of the Global Financial Crisis, some risk management practitioners have advocated wider adoption of … Bayesian inference to replace Value- at-Risk (VaR) models in order to minimize risk failures. Despite its limitations, the … [increasingly] Bayesian—continues to be a key methodological foundation of risk management and regulation-related risk modeling …

In aftermath of the Financial Crisis, some risk management practitioners advocate wider adoption of Bayesian inference … to replace Value-at-Risk (VaR) models for minimizing risk failures (Borison & Hamm, 2010). They claim reliance of …-Bayesian and [increasingly] Bayesian – continues to be a key methodological foundation of risk management and regulation related …

Tail risk refers to the possibility that a rare event would adversely affect the value of a portfolio in a significant … manner. It became much more relevant due to recent periods of strong market turbulence.We describe how to quantify such risk …, which tail risk protection strategies were considered in the literature, their effectiveness and associated costs. We also …

model with heterogeneous agents, we reveal the existence of an extreme weather risk premium in the cross-section of stock … returns. In the period from 1995 to 2019, domestic U.S. stocks with the most negative sensitivity to thunderstorm losses … risk factors from standard asset pricing models nor by firm characteristics. Our results reveal a novel link between …

The stochastic mathematical model of the credit risk process is examined. It is assumed that in unstable economic … condition of default may be a cause for credit risk. The fund value of the crediting is considered as some random variable that … random variables are independent the expected risk of crediting for a single loan is found. On base of the expected risk …

number of discrete actions, representing low-level agents. For the low-level agents, we use a set of Hierarchical Risk Parity … (HRP) and Hierarchical Equal Risk Contribution (HERC) models with different hyperparameters, which all run in parallel, off …, representing low-level agents. For the low-level agents, we use a set of Hierarchical Risk Parity (HRP) and Hierarchical Equal Risk …