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241
Realized Variance and Market Microstructure Noise
, 2005
"... We study market microstructure noise in highfrequency data and analyze its implications for the realized variance (RV) under a general specification for the noise. We show that kernelbased estimators can unearth important characteristics of market microstructure noise and that a simple kernelbas ..."
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Cited by 263 (13 self)
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We study market microstructure noise in highfrequency data and analyze its implications for the realized variance (RV) under a general specification for the noise. We show that kernelbased estimators can unearth important characteristics of market microstructure noise and that a simple kernelbased estimator dominates the RV for the estimation of integrated variance (IV). An empirical analysis of the Dow Jones Industrial Average stocks reveals that market microstructure noise is timedependent and correlated with increments in the efficient price. This has important implications for volatility estimation based on highfrequency data. Finally, we apply cointegration techniques to decompose transaction prices and bid–ask quotes into an estimate of the efficient price and noise. This framework enables us to study the dynamic effects on transaction prices and quotes caused by changes in the efficient price.
Efficient estimation of stochastic volatility using noisy observations: A multiscale approach
, 2004
"... With the availability of high frequency financial data, nonparametric estimation of volatility of an asset return process becomes feasible. A major problem is how to estimate the volatility consistently and efficiently, when the observed asset returns contain error or noise, for example, in the form ..."
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Cited by 154 (14 self)
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With the availability of high frequency financial data, nonparametric estimation of volatility of an asset return process becomes feasible. A major problem is how to estimate the volatility consistently and efficiently, when the observed asset returns contain error or noise, for example, in the form of microstructure noise. The former (consistency) has been addressed heavily in the recent literature, however, the resulting estimator is not quite efficient. In Zhang, Mykland, and AïtSahalia (2003), the best estimator converges to the true volatility only at the rate of n −1/6. In this paper, we propose an efficient estimator which converges to the true at the rate of n −1/4, which is the best attainable. The estimator remains valid when the observation noise is dependent. Some key words and phrases: consistency, dependent noise, discrete observation, efficiency, Ito process, microstructure noise, observation error, rate of convergence, realized volatility
Predicting volatility: getting the most out of return data sampled at different frequencies
, 2004
"... We consider various MIDAS (Mixed Data Sampling) regression models to predict volatility. The models differ in the specification of regressors (squared returns, absolute returns, realized volatility, realized power, and return ranges), in the use of daily or intradaily (5minute) data, and in the le ..."
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Cited by 144 (20 self)
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We consider various MIDAS (Mixed Data Sampling) regression models to predict volatility. The models differ in the specification of regressors (squared returns, absolute returns, realized volatility, realized power, and return ranges), in the use of daily or intradaily (5minute) data, and in the length of the past history included in the forecasts. The MIDAS framework allows us to compare models across all these dimensions in a very tightly parameterized fashion. Using equity return data, we find that daily realized power (involving 5minute absolute returns) is the best predictor of future volatility (measured by increments in quadratic variation) and outperforms model based on realized volatility (i.e. past increments in quadratic variation). Surprisingly, the direct use of highfrequency (5minute) data does not improve volatility predictions. Finally, daily lags of one to two months are sufficient to capture the persistence in volatility. These findings hold both in and outofsample.
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 130 (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.
Ultra high frequency volatility estimation with dependent microstructure noise
"... We analyze the impact of time series dependence in market microstructure noise on the properties of estimators of the integrated volatility of an asset price based on data sampled at frequencies high enough for that noise to be a dominant consideration. We show that combining two time scales for tha ..."
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Cited by 100 (11 self)
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We analyze the impact of time series dependence in market microstructure noise on the properties of estimators of the integrated volatility of an asset price based on data sampled at frequencies high enough for that noise to be a dominant consideration. We show that combining two time scales for that purpose will work even when the noise exhibits time series dependence, analyze in that context a refinement of this approach based on multiple time scales, and compare empirically our different estimators to the standard realized volatility.
Microstructure noise in the continuous case: the preaveraging approach’,
 Stochastic Processes and their Applications 119,
, 2009
"... Abstract This paper presents a generalized preaveraging approach for estimating the integrated volatility, in the presence of noise. This approach also provides consistent estimators of other powers of volatility in particular, it gives feasible ways to consistently estimate the asymptotic varian ..."
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Cited by 95 (18 self)
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Abstract This paper presents a generalized preaveraging approach for estimating the integrated volatility, in the presence of noise. This approach also provides consistent estimators of other powers of volatility in particular, it gives feasible ways to consistently estimate the asymptotic variance of the estimator of the integrated volatility. We show that our approach, which possesses an intuitive transparency, can generate rate optimal estimators (with convergence rate n −1/4 ).
Multivariate realised kernels: consistent positive semidefinite estimators of the covariation of equity prices with noise and nonsynchronous trading
, 2008
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Estimation of volatility functionals in the simultaneous presence of microstructure noise and jumps
, 2009
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Estimating covariation: Epps effect and microstructure noise
 Journal of Econometrics, forthcoming
, 2009
"... This paper is about how to estimate the integrated covariance 〈X, Y 〉T of two assets over a fixed time horizon [0, T], when the observations of X and Y are “contaminated ” and when such noisy observations are at discrete, but not synchronized, times. We show that the usual previoustick covariance e ..."
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Cited by 59 (3 self)
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This paper is about how to estimate the integrated covariance 〈X, Y 〉T of two assets over a fixed time horizon [0, T], when the observations of X and Y are “contaminated ” and when such noisy observations are at discrete, but not synchronized, times. We show that the usual previoustick covariance estimator is biased, and the size of the bias is more pronounced for less liquid assets. This is an analytic characterization of the Epps effect. We also provide optimal sampling frequency which balances the tradeoff between the bias and various sources of stochastic error terms, including nonsynchronous trading, microstructure noise, and time discretization. Finally, a twoscales covariance estimator is provided which simultaneously cancels (to first order) the Epps effect and the effect of microstructure noise. The gain is demonstrated in data.