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

Abstract

In this paper, we assess the informational content of daily range, realized variance, realized bipower variation, two time scale realized variance, realized range and implied volatility in daily, weekly, biweekly and monthly out-of-sample Value-at-Risk (VaR) predictions. We use the recently proposed Realized GARCH model combined with the skewed student distribution for the innovations process and a Monte Carlo simulation approach in order to produce the multi- Jel Classification: C13; C53; C58; G17; G21; G32

Forecasting stock market volatility using Realized GARCH model: International evidence

“…Following Louzis et al(2013), we use an AR(1) specification for modeling the conditional mean of the GARCH models 2 .The conditional mean equation is…”

“…The Realized GARCH model is still relatively nascent, with few empirical applications in the equity markets. Other notable applications include Hansen et al (2012), Louzis, Xanthopoulos-Sisinis, & Refenes (2013), and Watanabe (2012), all of which are based solely on the U.S. equity market. Second, our study uses a data set with a relatively long sample period, as compared to most of the existing studies on realized volatility forecasting.…”

Market risk of BRIC Eurobonds in the financial crisis period

“…The first range estimator was suggested in Parkinson (1980). A recent study in range estimators of volatility is Louzis, Xanthopoulos-Sisinis, and Refenes (2013). A third group of nonparametric estimators is realized range-based volatility estimators.…”

Modeling and forecasting exchange rate volatility in time-frequency domain

“…Mcmillan and Speight (2012) for example utilize intra-day data and show that we can obtain forecasts superior to forecasts from GARCH(1,1). Louzis et al (2013) assesses the informational content of alternative realized volatility estimators using Realized GARCH in Value-at-Risk prediction. We aim to extend this line of research by investigating the importance of disentangling jump variation and integrated variance in recently developed framework, which combines appeal of a widely used GARCH(1,1) and high frequency data.…”