two-part compound risk models. In this generalized formula of credibility premium of compound sum, we are able to derive a …

the period 1972:1-2014:12 to forecasts our tail risk indicators with each model in pseudo-real time. Our key finding is …

loss risk. This study uses geometric Brownian motion (GBM) and Value at Risk (VaR; with the Monte Carlo Simulation approach …) on the daily closing price of JKII from 1 August 2020-13 August 2021 to predict the price and loss risk of JKII at 16 … 2.03%. Then, using VaR with a Monte Carlo Simulation approach, the loss risk prediction for 16 August 2021 (one …

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 institutions measure their risk. Analysing 11 stock indices, we show that …

This paper demonstrates that existing quantile regression models used for forecasting Value-at-Risk (VaR) and expected … open-faced sandwich (OFS) method is proposed which improves uncertainty quantification in risk forecasts. Simulation and … empirical results highlight the improvements in risk forecasts ensuing from the proposed methods …