, including correlation coefficients, error functions, and stepwise regression, are employed to analyze the selected variables … enhance the development of prediction models for stock indices in Jordan and other countries by incorporating relevant …

to as PD-LGD correlation (here PD refers to probability of default, which is often used synonymously with default rate …This paper seeks to identify computationally efficient importance sampling (IS) algorithms for estimating large …). There is a large literature on modelling stochastic LGD and PD-LGD correlation, but there is a dearth of literature on using …

to outliers. To address this issue, we propose a robust alternative that estimates the SUR parameters in a more outlier … the GMCL model and illustrate the importance of taking into account contemporaneous correlations and structural …

constraints. We assume randomness on the covariance matrix as a way of generalizing. We analyse common barrier derivatives …

risks. We discuss the impact of tree topology on the second-order moments and tail statistics of the resulting distribution …

We introduce an additive stochastic mortality model which allows joint modelling and forecasting of underlying death …

We explore the Monte Carlo steps required to reduce the sampling error of the estimated 99.9% quantile within an … risk measures. Naturally, financial analysts and regulators are interested in mitigating sampling errors, as prescribed in … EU Regulation 2018/959. In particular, the sampling error of the 99.9% quantile is of paramount importance, along the …