A Combined Filtering Approach to High-Frequency Volatility Estimation with Mixed-Type Microstructure Noises PDF Download
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Author: Yinfen Tang
Publisher:
ISBN:
Category :
Languages : en
Pages : 35
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Book Description
This paper introduces a solution that combines the Kalman and particle fi lters to the challenging problem of estimating integrated volatility using high-frequency data where the underlying prices are perturbed by a mixture of random noise and price discreteness. An explanation is presented of how the proposed combined filtering approach is able to correct for bias due to this mixed-type microstructure effect. Simulation and empirical studies on the tick-by-tick trade price data for four US stocks in the year 2009 show that our method has clear advantages over existing high-frequency volatility estimation methods.
Author: Yinfen Tang
Publisher:
ISBN:
Category :
Languages : en
Pages : 35
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Book Description
This paper introduces a solution that combines the Kalman and particle fi lters to the challenging problem of estimating integrated volatility using high-frequency data where the underlying prices are perturbed by a mixture of random noise and price discreteness. An explanation is presented of how the proposed combined filtering approach is able to correct for bias due to this mixed-type microstructure effect. Simulation and empirical studies on the tick-by-tick trade price data for four US stocks in the year 2009 show that our method has clear advantages over existing high-frequency volatility estimation methods.
Author: Yacine Ait-Sahalia
Publisher:
ISBN:
Category :
Languages : en
Pages : 43
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Book Description
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.
Author: Yacine Aït-Sahalia
Publisher:
ISBN: 9783865580849
Category : Assets (Accounting)
Languages : de
Pages : 41
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Book Description
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.
Author: Z. Merrick Li
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Xin Zhang
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
This dissertation work focuses on developing statistical methods for volatility estimation and prediction with high frequency financial data. We consider two kinds of volatility: integrated volatility and jump variation. In the first part, we introduce the methods for integrated volatility estimation with the presence of microstructure noise. We will first talk about the optimal sampling frequency for integrated volatility estimation since subsampling is very popular in practice. Then we will discuss about those methods based on subsampling. Two-scale estimator is developed using the subsampling idea while taking advantage of all of the data. An extension to the multi-scale further improves the efficiency of the estimation. In the second part, we propose a heterogenous autoregressive model for the integrated volatility estimators based on subsampling. An empirical approach is to estimate integrated volatility using high frequency data and then fit the estimates to a low frequency heterogeneous autoregressive volatility model for prediction. We provide some theoretical justifications for the empirical approach by showing that these estimators approximately obey a heterogenous autoregressive model for some appropriate underlying price and volatility processes. In the third part, we propose a method for jump variation estimation using wavelet techniques. Previously, jumps are not assumed in the model. In this part, we will concentrate on jump variation estimation and there- fore, we will be able to estimate the integrated volatility and jump variation individually. We show that by choosing a threshold, we will be able to detect the jump location, and by using the realized volatility processes instead of the original price process, we will be able to improve the convergence rate of estimation. We include both numerical and empirical results of this method.
Author: Yifan Li
Publisher:
ISBN:
Category :
Languages : en
Pages : 63
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Book Description
In this paper we examine the relative importance of trading volume, bid-ask spread, order flow, order imbalance, total quote depth, quote depth difference and trading intensity for high-frequency volatility estimation. By using a best subset regression approach, we fi nd that contemporaneous trading intensity and order flow contains the most important information about volatility estimation in general, but the rankings of the importance of the market microstructure (MMS) variables vary between securities. Using a Lognormal Log-Autoregressive Conditional Duration (LL-ACD) model, we show that the inclusion of MMS covariates signi ffcantly improves the goodness-of- fit of the model. Furthermore, we show that the inclusion of MMS covariates in the LL-ACD model leads to substantial improvements in the quality of volatility estimates, both on a daily and an intraday level.
Author: Emilio Barucci
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
We study the forecasting performance of the Fourier volatility estimator in the presence of microstructure noise. Analytical comparison and simulation studies indicate that the Fourier estimator significantly outperforms realized volatility type estimators in particular for high frequency data and when the noise component is relevant. We show that Fourier estimator in general has a better performance even in comparison with methods specifically designed to handle market microstructure contaminations.
Author: Z. Merrick Li
Publisher:
ISBN:
Category :
Languages : en
Pages : 32
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Book Description
Section A of this appendix contains detailed proofs of our results. In Sections B and C, we provide additional Monte Carlo simulation studies and empirical results.
Author: Maria Elvira Mancino
Publisher:
ISBN:
Category :
Languages : en
Pages : 53
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Book Description
Availability of high frequency data has improved the capability of computing volatility in an efficient way. Nevertheless, measuring volatility/covariance from the observation of the asset price is challenging for two main reasons: observed asset prices are generally affected by noise microstructure effects and tick-by-tick returns are asynchronous across different assets. In this paper we review the definition and the statistical properties of the so called Fourier estimator of multivariate volatility, with particular focus on using high frequency data. Exploiting the fact that the method allows to compute both the integrated and the instantaneous volatility, we show how to obtain estimators of the volatility of the volatility and the leverage as well. Further, we study the performance of the estimator in forecasting and in terms of portfolio utility in the presence of microstructure noise contaminations.
Author: Lan Zhang
Publisher:
ISBN:
Category :
Languages : en
Pages : 25
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Book Description
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, Ait-Sahalia (2003), the best estimator converges to the true volatility only at the rate of n wedge{-1/6}. In this paper, we propose an estimator, the Multi-scale Realized Volatility (MSRV), which converges to the true volatility at the rate of n wedge{-1/4}, which is the best attainable. We have shown a central limit theorem for the MSRV estimator, which permits setting intervals for the true integrated volatility on the basis of MSRV.
