Value-at-Risk (VaR) has become the universally accepted metric in the financial services industry for internal control and for regulatory reporting. This has focused attention on methods of measuring, estimating and forecasting lower tail risk. One promising technique is Quantile Regression...

Value-at-Risk (VaR) has become the universally accepted metric in the financial services industry for internal control and for regulatory reporting. This has focused attention on methods of measuring, estimating and forecasting lower tail risk. One promising technique is Quantile Regression...

In this paper, we document that realized variation measures constructed from high-frequency returns reveal a large degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which forecasting errors in realized volatility are substantive. Even...

This paper applies two measures to assess spillovers across markets: the Diebold Yilmaz (2012) Spillover Index and the Hafner and Herwartz (2006) analysis of multivariate GARCH models using volatility impulse response analysis. We use two sets of data, daily realized volatility estimates taken...

The paper examines the relative performance of Stochastic Volatility (SV) and Generalised Autoregressive Conditional Heteroscedasticity (GARCH) (1,1) models fitted to ten years of daily data for FTSE. As a benchmark, we used the realized volatility (RV) of FTSE sampled at 5 min intervals taken...