Open AccessQUANTILE FORECASTS OF FINANCIAL RETURNS USING REALIZED GARCH MODELS *
Author(s) -
WATANABE TOSHIAKI
Publication year - 2012
Publication title -
the japanese economic review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.205
H-Index - 28
eISSN - 1468-5876
pISSN - 1352-4739
DOI - 10.1111/j.1468-5876.2011.00548.x
Subject(s) - autoregressive conditional heteroskedasticity , econometrics , economics , volatility (finance) , quantile , value at risk , autoregressive model , stock (firearms) , heteroscedasticity , conditional probability distribution , finance , risk management , geography , archaeology
This article applies the realized generalized autoregressive conditional heteroskedasticity (GARCH) model, which incorporates the GARCH model with realized volatility, to quantile forecasts of financial returns, such as Value‐at‐Risk and expected shortfall. Student's t ‐ and skewed Student's t ‐distributions as well as normal distribution are used for the return distribution. The main results for the S&P 500 stock index are: (i) the realized GARCH model with the skewed Student's t ‐distribution performs better than that with the normal and Student's t ‐distributions and the exponential GARCH model using the daily returns only; and (ii) using the realized kernel to take account of microstructure noise does not improve the performance.