Results 1  10
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68
A Theoretical Comparison Between Integrated and Realized Volatilities
, 2002
"... In this paper, we provide both qualitative and quantitative measures of the precision of measuring integrated volatility by realized volatility for a fixed frequency of observation. We start by characterizing for a general diffusion the dierence between realized and integrated volatility for a given ..."
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Cited by 134 (8 self)
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In this paper, we provide both qualitative and quantitative measures of the precision of measuring integrated volatility by realized volatility for a fixed frequency of observation. We start by characterizing for a general diffusion the dierence between realized and integrated volatility for a given frequency of observation. Then we compute the mean and variance of this noise and the correlation between the noise and the integrated volatility in the Eigenfunction Stochastic Volatility model of Meddahi (2001a). This model has as special cases lognormal, affine and GARCH diusion models. Using previous empirical results, we show that the noise is substantial compared with the unconditional mean and variance of integrated volatility, even if one employs fiveminute returns. We also propose a simple approach to capture the information about integrated volatility contained in the returns through the leverage eect. We show that in practice, the leverage effect does not matter.
Separating microstructure noise from volatility
, 2006
"... There are two variance components embedded in the returns constructed using high frequency asset prices: the timevarying variance of the unobservable efficient returns that would prevail in a frictionless economy and the variance of the equally unobservable microstructure noise. Using sample moment ..."
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Cited by 131 (9 self)
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There are two variance components embedded in the returns constructed using high frequency asset prices: the timevarying variance of the unobservable efficient returns that would prevail in a frictionless economy and the variance of the equally unobservable microstructure noise. Using sample moments of high frequency return data recorded at different frequencies, we provide a simple and robust technique to identify both variance components. In the context of a volatilitytiming trading strategy, we show that careful (optimal) separation of the two volatility components of the observed stock returns yields substantial utility gains.
Maximum likelihood estimation for stochastic volatility models
 JOURNAL OF FINANCIAL ECONOMICS
, 2007
"... We develop and implement a method for maximum likelihood estimation in closedform of stochastic volatility models. Using Monte Carlo simulations, we compare a full likelihood procedure, where an option price is inverted into the unobservable volatility state, to an approximate likelihood procedure ..."
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Cited by 106 (3 self)
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We develop and implement a method for maximum likelihood estimation in closedform of stochastic volatility models. Using Monte Carlo simulations, we compare a full likelihood procedure, where an option price is inverted into the unobservable volatility state, to an approximate likelihood procedure where the volatility state is replaced by proxies based on the implied volatility of a shortdated atthemoney option. The approximation results in a small loss of accuracy relative to the standard errors due to sampling noise. We apply this method to market prices of index options for several stochastic volatility models, and compare the characteristics of the estimated models. The evidence for a general CEV model, which nests both the affine Heston model and a GARCH model, suggests that the elasticity of variance of volatility lies between that assumed by the two nested models.
Correcting the Errors: Volatility Forecast Evaluation Using HighFrequency Data and Realized Volatilities
, 2004
"... We develop general modelfree adjustment procedures for the calculation of unbiased volatility loss functions based on practically feasible realized volatility benchmarks. The procedures, which exploit the recent nonparametric asymptotic distributional results in BarndorffNielsen and Shephard (200 ..."
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Cited by 68 (12 self)
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We develop general modelfree adjustment procedures for the calculation of unbiased volatility loss functions based on practically feasible realized volatility benchmarks. The procedures, which exploit the recent nonparametric asymptotic distributional results in BarndorffNielsen and Shephard (2002a) along with new results explicitly allowing for leverage effects, are both easytoimplement and highly accurate in empirically realistic situations. On properly accounting for the measurement errors in the volatility forecast evaluations reported in Andersen, Bollerslev, Diebold and Labys (2003), the adjustments result in markedly higher estimates for the true degree of return volatility predictability.
Analytic evaluation of volatility forecasts
 International Economic Review
, 2004
"... The development of estimation and forecasting procedures using empirically realistic continuoustime stochastic volatility models is severely hampered by the lack of closedform expressions for the transition densities of the observed returns. In response to this, Andersen, Bollerslev, Diebold and L ..."
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Cited by 59 (10 self)
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The development of estimation and forecasting procedures using empirically realistic continuoustime stochastic volatility models is severely hampered by the lack of closedform expressions for the transition densities of the observed returns. In response to this, Andersen, Bollerslev, Diebold and Labys (2002) have recently advocated modeling and forecasting the (latent) integrated volatility of primary import from a pricing perspective based on simple reduced form time series models for the observable realized volatilities, constructed from the summation of highfrequency squared returns. Building on the eigenfunction stochastic volatility class of models introduced by Meddahi (2001), we present analytical expressions for the loss in forecast eÆciency associated with this easytoimplement procedure as a function of the sampling frequency of the returns underlying the realized volatility measures. On numerically quantifying this eÆciency loss for such popular continuoustime models as GARCH, multifactor aÆne, and lognormal diusions, we nd that the realized volatility reduced form procedures perform remarkably well in comparison to the optimal (nonfeasible) forecasts conditional on the full sample path realization of the latent instantaneous volatility process.
Financial asset returns, directionofchange forecasting and volatility dynamics
, 2003
"... informs doi 10.1287/mnsc.1060.0520 ..."
Realized Volatility Forecasting and Market Microstructure Noise
, 2009
"... We extend the analytical results for reduced form realized volatility based forecasting in Andersen, Bollerslev and Meddahi (2004) to allow for market microstructure frictions in the observed highfrequency returns. Our results build on the eigenfunction representation of the general stochastic vola ..."
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Cited by 30 (3 self)
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We extend the analytical results for reduced form realized volatility based forecasting in Andersen, Bollerslev and Meddahi (2004) to allow for market microstructure frictions in the observed highfrequency returns. Our results build on the eigenfunction representation of the general stochastic volatility class of models developed by Meddahi (2001). In addition to traditional realized volatility measures and the role of the underlying sampling frequencies, we also explore the forecasting performance of several alternative volatility measures designed to mitigate the impact of the microstructure noise. Our analysis is facilitated by a simple unified quadratic form representation for all these estimators. Our results suggest that the detrimental impact of the noise on forecast accuracy can be substantial. Moreover, the linear forecasts based on a simpletoimplement ‘average’ (or ‘subsampled’) estimator obtained by averaging standard sparsely sampled realized volatility measures generally performs on par with the best alternative robust measures.