Forecasting Currency Volatility: A Comparison of Implied Volatilities and AR(FI)MA Models

By Shiu-yan Eddie Pong, M. Shackleton, Stephen J. Taylor, Xinzhong Xu. 2004

303 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/23abdac0e88bfd0e1c58d75932b0b4ac46233cfa

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Citations in the corpus

A Discrete-Time Model for Daily S & P500 Returns and Realized Variations: Jumps and Leverage Effects (2009, unknown): “More recently, several studies, including Andersen et al. (2003), Martens et al. (2004), Martens and Zein (2004), Pong et al. (2004), and Thomakos and Wang (2003) among others, have advocated the use of ARFIMA type models, along with approximate long-memory component type structures in Andersen et…”
Comparison of Volatility Measures: A Risk Management Perspective (2010, unknown): “(2005), Deo, Hurvich & Lu (2006), Pong, Shacketon, Taylor & Xu (2004) ) and regression models”
A comprehensive review of Value at Risk methodologies (2014, methodology): “this hypothesis can be found in studies by Sener et al. (2012), Bali and Theodossiou (2007), Abad and Benito (2012), Chen et al.”
Closed-form transformations from risk-neutral to real-world distributions (2007, methodology): “Other approaches include maximum entropy densities [Buchen and Kelly (1996)], non-parametric estimates [Ait-Sahalia and Lo (1998)], multi- 1 Recent evidence is provided by Poon and Granger (2003), Martens and Zein (2004), Pong et al (2004) and Jiang and Tian (2005).”
Refined Inference on Long Memory in Realized Volatility (2008, unknown): “See, among others, Li (2002), Andersen et. al. (2003), Pong et. al. (2004) , Martens and Zein (2004).”
Forecasting Volatility Using High-Frequency Data (2011, unknown): “However, the out-of-sample implied volatility forecasts that Pong et al. (2004) employ are “enhanced” by regressing ex-post realized volatilities on implied volatilities, and using the regression coefficients to better match the target.”
Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility (2007, unknown): “First, it seems natural that jump risk may be priced differently from easier-to-hedge continuous price variability; see, for example, Santa-Clara and Yan (2004). Hence, separately modeling and forecasting the continuous sample path, or integrated volatility, and jump components of the quadratic vari”
Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data—But Which Frequency to Use? (2008, unknown): “…correspondence to Dick van Dijk, Econometric Institute, Erasmus University, Rotterdam, P.O. Box 1738, Rotterdam NL-3000 DR, The Netherlands; E-mail: djvandijk@few.eur.nl Taylor (2002), Thomakos and Wang (2003), Martens et al. (2004), Pong et al. (2004), and Koopman et al. (2005), among others.”
A Reduced Form Framework for Modeling Volatility of Speculative Prices Based on Realized Variation Measures (2007, background): “(2002), Corsi (2004), Deo, Hurvich and Lu (2006), Koopman, Jungbacker and Hol (2005), Martens, van Dijk and Pooter (2004), Pong, Shackleton, Taylor and Xu (2004) , and Thomakos and Wang (2003), among others.”
The Volatility of Realized Volatility (2005, unknown): “5 See, for example, Martens et al. (2004), Martens and Zein (2004), Oomen (2004), Pong et al. (2004) ,”
The role of implied volatility in forecasting future realized volatility and jumps in foreign exchange, stock, and bond markets (2007, unknown): “(1995), and Pong et al. (2004) hold up even to these new improvements of the return-based forecasts.”