allocation and risk management require estimates of the volatility of these factors. While realized volatility has become a …

and risk features than very simple and very complex models. Combinations of two strategies help, in particular, to reduce … risk features like volatility and largest loss, which indicates that complete densities provide useful information for risk. …

term spread indicators, as determinants of the long-term risk of aggregated future asset prices. However, the subsequent …

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 …

daily risk estimates to the monetary authorities at the beginning of the trading day, using a variety of Value-at-Risk (VaR …) models to measure risk. Sometimes the risk estimates communicated using these models are too high, thereby leading to large … capital requirements and high capital costs. At other times, the risk estimates are too low, leading to excessive violations …

-C-MGARCH) model of Fülle and Herwartz (2021). As an empirical illustration we take the perspective of a risk averse agent and employ … risk forecasting for daily returns over 10 years for heterogeneous market environments including, for example, the COVID-19 …