Modelling daily value-at-risk using realized volatility and arch type models

By P. Giot, Sébastien-Yves Laurent. 2001

468 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/9efd5eb63520ff1bb4cd786aa995ef2ab87ce270

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Finance

Citations in the corpus

Comparison of Volatility Measures: A Risk Management Perspective (2010, unknown): “Furthermore, extending Giot and Laurent (2004), we assume that the conditional variance ht is an affine function of the conditional expectation rv(m,δ) t|t−1 at time t − 1 of the volatility measure m, namely, rt = √ c + d rv(m,δ) t|t−1 ηt .”
Autoregressive Conditional Heteroscedasticity (ARCH) Models: A Review (2004, methodology): “Ebens [105], Giot and Laurent [146], and Thomakos and Wang [291] proposed the use of an ARFIMA model, in the form of (41), in order to fit the logarithmic realized variance.”
Optimally Harnessing Inter-Day and Intra-Day Information for Daily Value-at-Risk Prediction (2013, unknown): “For instance, in the context of data up to year 2001 on the CAC40 index, S&P500 futures and the YEN/USD and DEM/USD currencies, Giot and Laurent (2004) conclude that ARFIMA realized volatility-based VaR forecasts are unable to beat those obtained from the skewed Student (skt) APARCH model.”
Forecasting S&P 500 volatility: Long memory, level shifts, leverage effects, day-of-the-week seasonality, and macroeconomic announcements (2009, methodology): “As an alternative method for evaluating the forecasts of the different models from a more economic point of view, we consider Value-at-Risk (VaR) estimates which are constructed using the volatility forecasts of the different models, see also Giot and Laurent (2004). Under the 1998 Market Risk Amend”
Frontiers in VaR forecasting and backtesting (2016, methodology): “…availability of intradaily ultra-high frequency data (UHFD), there is a growing literature that proposes estimating the daily volatility using measures based on this type of observations; see Giot and Laurent (2004), Clements et al. (2008), Coroneo and Veredas (2012) and Fuertes and Olmo (2013).”
Asymmetric power distribution: Theory and applications to risk measurement (2007, methodology): “Particularly interesting flexible densities are obtained as generalizations of Student-t density: (2) a generalized t-distribution and a skewed t-distribution (see Hansen, 1994; Fernandez and Steel, 1998; Giot and Laurent, 2004; Patton, 2004; Kuester et al., 2006; Paolella, 2006).”
A power GARCH examination of the gold market (2007, methodology): “(2000), Tooma and Sourial (2004) and Giot and Laurent (2004). These papers have documented the applicability of the PARCH model to stock market data. McKenzie et al. (2001) examined the applicability of the PARCH model to futures contracts traded on the London metal exchange (LME). The authors found”
Do High-Frequency Measures of Volatility Improve Forecasts of Return Distributions? (2011, unknown): “Andersen, Bollerslev, Diebold, and Labys (2003), and Giot and Laurent (2004) consider the value of RV for forecasting and for Value-at-Risk.”
Semiparametric Conditional Quantile Models for Financial Returns and Realized Volatility (2013, background): “Notable exceptions include Andersen, Bollerslev, Diebold & Labys (2003), Giot & Laurent (2004) and Clements, Galvao & Kim (2008), who all combine time-series models for realized volatility with either parametric or nonparametric estimators of conditional distributions, and the recent contributions…”