scholarly journals The extent and variability of storm‐induced temperature changes in lakes measured with long‐term and high‐frequency data

2021 ◽  
Author(s):  
Jonathan P. Doubek ◽  
Orlane Anneville ◽  
Gaël Dur ◽  
Aleksandra M. Lewandowska ◽  
Vijay P. Patil ◽  
...  
Keyword(s):  
High Frequency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Temperature Changes

Improving Long-Term Financial Risk Forecasts using High-Frequency Data and Scaling Laws

2013 ◽  
pp. 1473-1495
Author(s):  
Wing Lon Ng
Keyword(s):  
High Frequency ◽  
Financial Risk ◽  
Scaling Law ◽  
High Frequency Data ◽  
Order Book ◽  
Scaling Exponent ◽  
Frequency Data ◽  
Market Prices ◽  
Price Movements

This chapter uses the abundance of high frequency data to estimate scaling law models and then apply appropriately scaled measures to provide long-term market risk forecasts. The objective is to analyse extreme price movements from tick-by-tick real-time data to trace the footprints of traders that eventually form the overall movement of market prices (price coastline) and potential bubbles. The framework is applied to empirical limit order book data from the London Stock Exchange. The sample period ranges from June 2007 to June 2008 and covers the start of the subprime crisis that later escalated into the economic crisis. After extracting the scaling exponent and checking its robustness with bootstrap simulations, the authors investigate longer term price movements in more detail, making use of the scale invariance property of the scaling law. In particular, they provide financial risk forecasts for a testing period and compare these with the popular Value-at-Risk and expected tail loss measures, showing the outperformance of the scaling law approach. Finally, a set of simulations are run to explore which scaling exponent is more likely to trigger market turbulence.

Improving Long-Term Financial Risk Forecasts using High-Frequency Data and Scaling Laws

2013 ◽  
pp. 255-278 ◽  
Author(s):  
Wing Lon Ng
Keyword(s):  
High Frequency ◽  
Financial Risk ◽  
Scaling Law ◽  
High Frequency Data ◽  
Order Book ◽  
Scaling Exponent ◽  
Frequency Data ◽  
Market Prices ◽  
Price Movements

This chapter uses the abundance of high frequency data to estimate scaling law models and then apply appropriately scaled measures to provide long-term market risk forecasts. The objective is to analyse extreme price movements from tick-by-tick real-time data to trace the footprints of traders that eventually form the overall movement of market prices (price coastline) and potential bubbles. The framework is applied to empirical limit order book data from the London Stock Exchange. The sample period ranges from June 2007 to June 2008 and covers the start of the subprime crisis that later escalated into the economic crisis. After extracting the scaling exponent and checking its robustness with bootstrap simulations, the authors investigate longer term price movements in more detail, making use of the scale invariance property of the scaling law. In particular, they provide financial risk forecasts for a testing period and compare these with the popular Value-at-Risk and expected tail loss measures, showing the outperformance of the scaling law approach. Finally, a set of simulations are run to explore which scaling exponent is more likely to trigger market turbulence.

Forecasting Exchange Rate Realized Volatility Through Decomposition Using High-Frequency Data

2017 ◽  
Author(s):  
Rim mname Lamouchi ◽  
Russell mname Davidson ◽  
Ibrahim mname Fatnassi ◽  
Abderazak Ben mname Maatoug
Keyword(s):  
Exchange Rate ◽  
High Frequency ◽  
Realized Volatility ◽  
High Frequency Data ◽  
Frequency Data
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