2012-06-21Masterarbeit DOI: 10.18452/14169
Estimation of quarticity based on high frequency data
Precise estimation of integrated quarticity is highly important, while this value provides inference about integrated volatility and is a valuable ingredient of jump hypothesis test statistics. Estimation of integrated quarticity based on high frequency data created additional challenges, which led to development of new measures, robust to jumps and microstructure noise. Different combinations of Multipower Volatility Estimators, Nearest Neighbor Truncation Estimators and Robust Neighborhood Truncation Estimators are analyzed in detail. After their application to real market data, each of the estimators is assessed via set of conducted simulation models. Special attention is paid to the Robust Neighborhood Truncation Estimators which operate on lower order statistics log-returns, while they were prematurely left out of analysis in previous works. Performed simulations as well as empirical calculations proved additional efficiency and jump robustness of these estimators.
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