Endogenous Markov Switching Regression Models for High-Frequency Data Under Microstructure Noise

2015 ◽  
Author(s):  
Markus Leippold ◽  
Felix Matthys
Keyword(s):  
High Frequency ◽  
Regression Models ◽  
Markov Switching ◽  
High Frequency Data ◽  
Switching Regression ◽  
Frequency Data ◽  
Microstructure Noise ◽  
Switching Regression Models

FAST CONVERGENCE RATES IN ESTIMATING LARGE VOLATILITY MATRICES USING HIGH-FREQUENCY FINANCIAL DATA

2013 ◽  
Vol 29 (4) ◽  
pp. 838-856 ◽  
Author(s):  
Minjing Tao ◽  
Yazhen Wang ◽  
Xiaohong Chen
Keyword(s):  
Sample Size ◽  
High Frequency ◽  
Convergence Rates ◽  
Fast Convergence ◽  
High Frequency Data ◽  
Frequency Data ◽  
Microstructure Noise ◽  
Finite Sample ◽  
Integrated Volatility ◽  
The Matrix

Financial practices often need to estimate an integrated volatility matrix of a large number of assets using noisy high-frequency data. Many existing estimators of a volatility matrix of small dimensions become inconsistent when the size of the matrix is close to or larger than the sample size. This paper introduces a new type of large volatility matrix estimator based on nonsynchronized high-frequency data, allowing for the presence of microstructure noise. When both the number of assets and the sample size go to infinity, we show that our new estimator is consistent and achieves a fast convergence rate, where the rate is optimal with respect to the sample size. A simulation study is conducted to check the finite sample performance of the proposed estimator.

Fourier volatility forecasting with high-frequency data and microstructure noise

2012 ◽  
Vol 12 (2) ◽  
pp. 281-293 ◽  
Author(s):  
Emilio Barucci ◽  
Davide Magno ◽  
Maria Elvira Mancino
Keyword(s):  
High Frequency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Microstructure Noise ◽  
Volatility Forecasting

scholarly journals Improved algorithm for cleaning high frequency data: An analysis of foreign currency

2015 ◽  
Vol 12 (3) ◽  
pp. 125-132
Author(s):  
Nirodha I. Jayawardena ◽  
Jason West ◽  
Neda Todorova ◽  
Bin Li
Keyword(s):  
High Frequency ◽  
Foreign Currency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Microstructure Noise ◽  
Volatility Clustering ◽  
Time Series Modelling ◽  
Non Gaussian ◽  
Irregular Spacing ◽  
The Empirical Analysis

High-frequency data are notorious for their noise and asynchrony, which may bias or contaminate the empirical analysis of prices and returns. In this study, we develop a novel data filtering approach that simultaneously addresses volatility clustering and irregular spacing, which are inherent characteristics of high-frequency data. Using high frequency currency data collected at five-minute intervals, we find the presence of vast microstructure noise coupled with random volatility clusters, and observe an extremely non-Gaussian distribution of returns. To process non-Gaussian high-frequency data for time series modelling, we propose two efficient and robust standardisation methods that cater for volatility clusters, which clean the data and achieve near-normal distributions. We show that the filtering process efficiently cleans high-frequency data for use in empirical settings while retaining the underlying distributional properties

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