The Volatility of Realized Volatility

Corsi, Fulvio; Mittnik, Stefan; Pigorsch, Christian; Pigorsch, Uta

doi:10.1080/07474930701853616

Cited by 359 publications

(272 citation statements)

References 77 publications

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“…Second, the HAR-RV model succeeds in reproducing the long memory features of the time-series, while being easier to estimate particularly on a shorter time-horizon. Third, the heterogeneous behavior assumed between economic agents may be justified by the fact that traders, (2001) and Corsi et al (2008) 26 . The HAR-RV model using daily, weekly and monthly realized-volatility components may be defined as follows:…”

Section: Adf Test D(gp H)d From Regressionmentioning

confidence: 99%

On the Realized Volatility of the ECX CO2 Emissions 2008 Futures Contract: Distribution, Dynamics and Forecasting

Chevallier

Sévi

2009

SSRN Journal

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Abstract:The recent implementation of the EU Emissions Trading Scheme (EU ETS) in January 2005 created new financial risks for emitting firms. To deal with these risks, options are traded since October 2006. Because the EU ETS is a new market, the relevant underlying model for option pricing is still a controversial issue. This article improves our understanding of this issue by characterizing the conditional and unconditional distributions of the realized volatility for the 2008 futures contract in the European Climate Exchange (ECX), which is valid during Phase II (2008)(2009)(2010)(2011)(2012) of the EU ETS. The realized volatility measures from naive, kernel-based and subsampling estimators are used to obtain inferences about the distributional and dynamic properties of the ECX emissions futures volatility. The distribution of the daily realized volatility in logarithmic form is shown to be close to normal. The mixture-of-distributions hypothesis is strongly rejected, as the returns standardized using daily measures of volatility clearly departs from normality. A simplified HAR-RV model (Corsi, 2009) with only a weekly component, which reproduces long memory properties of the series, is then used to model the volatility dynamics. Finally, the predictive accuracy of the HAR-RV model is tested against GARCH specifications using one-step-ahead forecasts, which confirms the HAR-RV superior ability. Our conclusions indicate that (i) the standard Brownian motion is not an adequate tool for option pricing in the EU ETS, and (ii) a jump component should be included in the stochastic process to price options, thus providing more efficient tools for risk-management activities.JEL Classification: C5, G1, Q4.

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Section: Adf Test D(gp H)d From Regressionmentioning

confidence: 99%

On the Realized Volatility of the ECX CO2 Emissions 2008 Futures Contract: Distribution, Dynamics and Forecasting

Chevallier

Sévi

2009

SSRN Journal

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“…where −1 = 1 , −1 , ⋯ , , −1 ′, = 1 , 2 , ⋯ , is a set of indices with 0 < 1 < uncorrelated process with finite, but not necessarily constant variance (Corsi, Mittnik, Pigorsch, and Pigorsch 2008). Corsi (2009) …”

Section: The Linear Heterogeneous Autoregressive Modelmentioning

confidence: 99%

Forecasting Realized Volatility with Linear and Nonlinear Models

McAleer

Medeiros

2009

SSRN Journal

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In this paper we consider a nonlinear model based on neural networks as well as linear models to forecast the daily volatility of the S&P 500 and FTSE 100 futures. As a proxy for daily volatility, we consider a consistent and unbiased estimator of the integrated volatility that is computed from high frequency intra-day returns. We also consider a simple algorithm based on bagging (bootstrap aggregation) in order to specify the models analyzed.

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“…Corsi et al (2008) and Corsi (2009) show that the HAR model is able to reproduce the longrange dependence typical of RV series. However, as noted by Maheu and McCurdy (2002) and McAleer and Medeiros (2008), the dynamic pattern of RV is subject to structural breaks and could potentially vary over time.…”

Section: The Time-varying Har Modelmentioning

confidence: 99%

“…However the model selection procedure, based on the predictive likelihood, excludes that breaks in the long-run mean during the financial crises are responsible for the increase in the observed persistence of the volatility 2 series. Moreover, the higher volatility of the persistent volatility factor increases the degree of dispersion of the volatility around its long-run value, and thus the volatility of volatility (see Corsi et al, 2008). Interestingly, the growth of the volatility of the persistent factor is reflected in an increase of the relative weight of the daily volatility component in the auxiliary TV-HAR model.…”

Section: Introductionmentioning

confidence: 99%