Quantile forecasts of daily exchange rate returns from forecasts of realized volatility

By Michael P. Clements, A. Galvão, Jae Hyun Kim. 2008

127 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/4262db6a62ac3f0fe75e7dfca32e5ad2c14658a7

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Citations in the corpus

Optimally Harnessing Inter-Day and Intra-Day Information for Daily Value-at-Risk Prediction (2013, unknown): “In this vein, our paper builds upon the studies by Giot and Laurent (2004), Angelidis and Degiannakis (2008) and Martens et al. (2009) on stock indices, Clements et al. (2008) on exchange rates and Brownlees and Gallo (2010) on specific NYSE stocks.”
Risk Everywhere: Modeling and Managing Volatility (2018, unknown): “27 Echoing this same theme, Clements, Galvao, and Kim (2008) also find that that the parameterization implicit in the original HAR model generally results in superior out-of-sample forecasts compared to a freely parameterized MIDAS model for the high-frequency intraday squared returns.”
Comparison of Volatility Measures: A Risk Management Perspective (2010, unknown): “(2004) and Clements, Galvao & Kim (2006) that contain VaR forecasting applications using realized”
Realizing smiles: Options pricing with realized volatility (2013, unknown): “In a concurrent paper, Christoffersen, Feunou, Jacobs, and Meddahi (2010) generalise the GARCH option pricing approach by extending the Heston and Nandi (2000) GARCH model to include RV measures.”
Volatility and Quantile Forecasts by Realized Stochastic Volatility Models with Generalized Hyperbolic Distribution (2016, background): “Giot and Laurent (2004) and Clements, Galvão, and Kim (2008) investigate the quantile forecast performance of GARCH models with the RV estimator although they are not fully contemporaneous models.”
A comprehensive review of Value at Risk methodologies (2014, methodology): “The models based on RV work quite well to estimate VaR (see Asai et al., 2011; Brownlees and Gallo, 2010; Clements et al., 2008; Giot and Laurent, 2004; Andersen et al., 2003).”
Semiparametric Conditional Quantile Models for Financial Returns and Realized Volatility (2013, methodology): “To assess the relative performance of the various quantile models, we follow Clements et al. (2008) and focus on pairwise comparison based on the tick-loss function suggested by Giacomini & Komujer (2005): Lα(et+1) = (α− 1{et+1 < 0})et+1, (12) where et+1 = rt+1 − qα(rt+1|Ωt).”
HAR Modeling for Realized Volatility Forecasting (2012, unknown): “…model with the MIDAS model; [3] use an extension of this model to forecast the volatility of stock prices, foreign exchange rates and bond prices; [31] implement it for risk management with VaR measures; [20] use it to analyze the risk-return tradeoff; [18] use it to study the relation between…”
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).”