Predicting financial volatility: High-frequency time-series forecasts vis-à-vis implied volatility: Predicting Financial Volatility

By M. Martens, Jason Zein. 2004

125 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/39374914bb237e2d856d9083a39f9523c7b40839

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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, methodology): “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…”
Forecasting Volatility of the U.S. Oil Market (2014, unknown): “Martens and Zein (2004) compared forecasts obtained from a long-memory model of RV with options-implied volatility for the WTI futures market.”
The Volatility of Realized Volatility (2005, unknown): “The relevance of our findings for extended realized-volatility models, for example models including leverage effects (see Martens et al., 2004), will be subject of future research.”
Modeling and predicting the CBOE market volatility index (2014, background): “See, among others, Canina and Figlewski (1993), Christensen and Prabhala (1998), Fleming (1998), Blair, Poon and Taylor (2001ab)), Martens and Zein (2004) Koopman, Jungbacker and Hol (2005), and Bandi and Perron (2006).”
Refined Inference on Long Memory in Realized Volatility (2008, background): “See, among others, Li (2002), Andersen et al. 256 O. Lieberman and P. C. B. Phillips (2003), Pong et al. (2004), Martens and Zein (2004).”
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).”
Do Jumps Matter for Volatility Forecasting? Evidence from Energy Markets (2016, background): “…the growing literature that uses intraday data to obtain more accurate volatility forecasts (Andersen and Bollerslev, 1998; Areal and Taylor, 2002; Martens and Zein, 2004; Giot and Laurent, 2007; Corsi and Reno, 2009; Corsi et al., 2010; Chung et al., 2011; Patton and Sheppard, 2015; Sévi, 2014).”