, if the past volatility is low, the historical computation of the risk measure underestimates the future risk, while in … periods of high volatility, the risk measure overestimates the risk.Moreover, using a simple GARCH (1,1) model, we conclude … that this pro-cyclical effect is related to the clustering of volatility. We argue that this has important consequences for …

Determining multiple assets’ portfolio volatility using the VaR model has proven to have so many pitfalls; once the … expected volatility to compute the amended VaR. While executing the adjusted VaR using this introduced optimization method, it …

link between monetary policy rate uncertainty and equity return volatility, both in theory and data. This paper uses … for equity variance and volatility at weekly, monthly and even quarterly horizons. The findings imply that market views of …

assets. Building on this idea, we propose the use of a highly flexible and tractable model to forecast the volatility of an …

Standard realized volatility (RV) measures estimate the latent volatility of an asset price using high frequency data … estimate of volatility to the application in which it will be used. For example, if the volatility measure will be used in a … volatility. We use methods from machine learning to estimate optimal “bespoke” RVs for heterogeneous autoregressive (HAR) and …

the long-term volatility of the gold markets. By utilising a mixed data sampling model, GARCH-MIDAS, we show that various … and price uncertainties contain useful forecasting information for long-term volatility of gold markets. In light of these …