Are Realized Volatility Models Good Candidates for Alternative Value at Risk Prediction Strategies?

Abstract

In this paper, we assess the Value at Risk (VaR) prediction accuracy and efficiency of six ARCH-type models, six realized volatility models and two GARCH models augmented with

The Role of High‐Frequency Intra‐daily Data, Daily Range and Implied Volatility in Multi‐period Value‐at‐Risk Forecasting

“…In particular, realized volatility estimators that utilize ultra-high-frequency intra-daily data (see McAller and Medeiros, 2008, for a review on realized volatility) have been employed in VaR forecasting studies (see, for example, Giot and Laurent, 2004;Beltratti and Morana, 2005;Angelidis and Degiannakis, 2008;Martens et al, 2009;Louzis et al, 2011). Brownless and Gallo (2010) were the first to investigate the ability of alternative realized volatility and VOLATILITY MEASURES In this section we briefly describe the three distinct categories of volatility measures employed in this study.…”