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Ultra High Frequency Volatility Estimation with Dependent Microstructure Noise, with Per Mykland and Lan Zhang, Journal of Econometrics, 2011, 160, 160-175.
Because global agricultural markets exhibit high frequency volatility, an annual model would do little to capture intra-annual price dynamics and shocks.
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His earlier work was in statistical modeling of high frequency financial data, especially volatility clustering in stocks, bonds, and currencies.
The year 2011 in capital markets was a roller coaster of market volatility, high frequency trading, rogue traders and hit-or-miss regulations.
Analyzing the Spectrum of Asset Returns: Jump and Volatility Components in High Frequency Data, with Jean Jacod, Journal of Economic Literature, 2012, 50, 1007-1050.
Hopefully the financial trading tax will discourage high frequency trading which tends to increase volatility and destabilize financial markets.
His research focuses on nonparametric and semiparametric estimation and inference of financial risk factors, such as volatility and jumps, based on high frequency financial data.
If, for instance, high frequency trading (or capital market liberalization) leads to more volatility in those circumstances when I might want to sell my assets, then it leads to less liquidity in the relevant sense.
Market turmoil may be linked to such volatility leading to a further boost in High Frequency Trading and within institutions and increase in short and long positions.
Results indicate that the realized volatility measures based on different scales of the high frequency data exhibit a consistent and stable long memory property.
Here the unknown parameter is in the volatility coefficient and the limit corresponds to n→∞ (high frequency model of observations).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com