Realized Volatility Risk

Allen, David E.; McAleer, Michael; Scharth, Marcel

doi:10.2139/ssrn.1520797

Cited by 4 publications

(6 citation statements)

References 54 publications

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“…This makes it very appealing. Moreover, it also allows to straightforwardly account for time-varying volatility of volatility, a feature that currently attracts researcher's interest, like Barndorff-Nielsen and Veraart (2009), and has been recognized to be important also for realized volatility; see Corsi et al (2008) and Allen, McAleer, and Scharth (2010).…”

Section: Introductionmentioning

confidence: 99%

Localized Realized Volatility Modeling

Chen

Härdle

Pigorsch

2010

Journal of the American Statistical Association

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With the recent availability of high-frequency financial data the long-range dependence of volatility regained researchers' interest and has led to the consideration of long-memory models for volatility. The long-range diagnosis of volatility, however, is usually stated for long sample periods, while for small sample sizes, such as one year, the volatility dynamics appears to be better described by short-memory processes. The ensemble of these seemingly contradictory phenomena point towards short-memory models of volatility with nonstationarities, such as structural breaks or regime switches, that spuriously generate a long memory pattern. In this paper we adopt this view on the dependence structure of volatility and propose a localized procedure for modeling realized volatility. That is at each point in time we determine a past interval over which volatility is approximated by a local linear process. A simulation study shows that long memory processes as well as short memory processes with structural breaks can be well approximated by this local approach. Furthermore, using S&P500 data we find that our local modeling approach outperforms long-memory type models and models with structural breaks in terms of predictability.

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Section: Introductionmentioning

confidence: 99%

Localized Realized Volatility Modeling

Chen

Härdle

Pigorsch

2010

Journal of the American Statistical Association

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“…Nevertheless, the importance of empirical findings of the volatility literature for option pricing and other applications should not be understated. In this paper we have analyzed an options pricing framework based on a new realized volatility model that captures all the relevant empirical regularities of the realized volatility series of the S&P 500 index, the dually asymmetric realized volatility (DARV) model of Allen et al [1]. Our results, though not extensive enough to be conclusive, indicate that the presence of persistent leverage effects and time varying volatility risk are essential for understanding common option pricing anomalies.…”

Section: Resultsmentioning

confidence: 96%

“…To calculate the realized volatility series we use tick-by-tick open to close quotes originated in the E-Mini S&P500 futures market of the Chicago Mercantile Exchange. 1 The period of analysis for the realized volatility starts in January 2, 1996, and ends in November 28, 2008. For the options, we record daily intraday option prices close to the 14:30 time mark between January 2, 2001, and ends in March 15, 2004.…”

Section: Realized Volatility and Datamentioning

confidence: 99%

“…The dually asymmetric realized volatility (DARV) model introduced by Allen et al [1] is a first step in analyzing and incorporating the modeling qualities of a more realistic setting for the volatility risk within a standard realized volatility model. The dual asymmetry in the model comes from leverage effects (as seen in the last section) and the positive relation between the level of volatility and the degree of volatility risk.…”

Section: The Dually Asymmetric Realized Volatility Modelmentioning

confidence: 99%

“…In this paper we propose an options pricing framework based on a new realized volatility model that captures all the relevant empirical regularities of the realized volatility series of the S&P 500 index, the dually asymmetric realized volatility (DARV) model of Allen et al [1]. In this setting returns display conditional volatility, skewness and kurtosis.…”

Section: Introductionmentioning

confidence: 99%

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