Finite sample accuracy and choice of sampling frequency in integrated volatility estimation

TY - JOUR

T1 - Finite sample accuracy and choice of sampling frequency in integrated volatility estimation

AU - Nielsen, Morten Ørregaard

AU - Frederiksen, Per

N1 - Funding Information: We thank Torben G. Andersen, Esben Høg, Asger Lunde, and an anonymous referee for valuable comments and suggestions, and Esben Høg and Asger Lunde for making their Gauss codes available to us. This paper was previously circulated under the title “Finite sample accuracy of integrated volatility estimators.” The first author is grateful for financial support from the Danish Social Science Research Council (grant no. SSF 24–20–0181).

PY - 2008/3

Y1 - 2008/3

N2 - We consider the properties of three estimation methods for integrated volatility, i.e. realized volatility, Fourier, and wavelet estimation, when a typical sample of high-frequency data is observed. We employ several different generating mechanisms for the instantaneous volatility process, e.g. Ornstein-Uhlenbeck, long memory, and jump processes. The possibility of market microstructure contamination is also entertained using models with bid-ask bounce and price discreteness, in which case alternative estimators with theoretical justification under market microstructure noise are also examined. The estimation methods are compared in a simulation study which reveals a general robustness towards persistence or jumps in the latent stochastic volatility process. However, bid-ask bounce effects render realized volatility and especially the wavelet estimator less useful in practice, whereas the Fourier method remains useful and is superior to the other two estimators in that case. More strikingly, even compared to bias correction methods for microstructure noise, the Fourier method is superior with respect to RMSE while having only slightly higher bias. A brief empirical illustration with high-frequency GE data is also included.

AB - We consider the properties of three estimation methods for integrated volatility, i.e. realized volatility, Fourier, and wavelet estimation, when a typical sample of high-frequency data is observed. We employ several different generating mechanisms for the instantaneous volatility process, e.g. Ornstein-Uhlenbeck, long memory, and jump processes. The possibility of market microstructure contamination is also entertained using models with bid-ask bounce and price discreteness, in which case alternative estimators with theoretical justification under market microstructure noise are also examined. The estimation methods are compared in a simulation study which reveals a general robustness towards persistence or jumps in the latent stochastic volatility process. However, bid-ask bounce effects render realized volatility and especially the wavelet estimator less useful in practice, whereas the Fourier method remains useful and is superior to the other two estimators in that case. More strikingly, even compared to bias correction methods for microstructure noise, the Fourier method is superior with respect to RMSE while having only slightly higher bias. A brief empirical illustration with high-frequency GE data is also included.

KW - Bid-ask bounce

KW - Finite sample bias

KW - Integrated volatility

KW - Long memory

KW - Market microstructure

KW - Monte Carlo simulation

KW - Realized volatility

KW - Wavelet

UR - http://www.scopus.com/inward/record.url?scp=38949172081&partnerID=8YFLogxK

U2 - 10.1016/j.jempfin.2006.12.005

DO - 10.1016/j.jempfin.2006.12.005

M3 - Journal article

AN - SCOPUS:38949172081

SN - 0927-5398

VL - 15

SP - 265

EP - 286

JO - Journal of Empirical Finance

JF - Journal of Empirical Finance

IS - 2

ER -