Are high frequency traders responsible for extreme price movements?

2021 ◽  
Author(s):  
Tina Prodromou ◽  
Joakim Westerholm
Keyword(s):  
High Frequency ◽  
Price Movements

High-Frequency Trading and Extreme Price Movements

2014 ◽  
Author(s):  
Jonathan Brogaard ◽  
Ryan Riordan ◽  
Andriy Shkilko ◽  
Konstantin Sokolov
Keyword(s):  
High Frequency ◽  
High Frequency Trading ◽  
Price Movements

scholarly journals Quantitative and Comparative Analyses of Limit Order Books with General Compound Hawkes Processes

Risks ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 110
Author(s):  
Qiyue He ◽  
Anatoliy Swishchuk
Keyword(s):  
High Frequency ◽  
Likelihood Estimation ◽  
Real Data ◽  
Error Rates ◽  
Algorithmic Trading ◽  
Swarm Optimization ◽  
Hawkes Processes ◽  
Price Movements ◽  
Order Books ◽  
Limit Order Books

In this paper, we solve the problem of mid price movements arising in high-frequency and algorithmic trading using real data. Namely, we introduce different new types of General Compound Hawkes Processes (GCHPDO, GCHP2SDO, GCHPnSDO) and find their diffusive limits to model the mid price movements of 6 stocks-EBAY, FB, MU, PCAR, SMH, CSCO. We also define error rates to estimate the models fitting accuracy. Maximum Likelihood Estimation (MLE) and Particle Swarm Optimization (PSO) are used for Hawkes processes and models parameters’ calibration.

Profiling high-frequency equity price movements in directional changes

2016 ◽  
Vol 17 (2) ◽  
pp. 217-225 ◽  
Author(s):  
Edward P. K. Tsang ◽  
Ran Tao ◽  
Antoaneta Serguieva ◽  
Shuai Ma
Keyword(s):  
High Frequency ◽  
Price Movements ◽  
Equity Price

High frequency trading and extreme price movements

2018 ◽  
Vol 128 (2) ◽  
pp. 253-265 ◽  
Author(s):  
Jonathan Brogaard ◽  
Allen Carrion ◽  
Thibaut Moyaert ◽  
Ryan Riordan ◽  
Andriy Shkilko ◽  
...  
Keyword(s):  
High Frequency ◽  
High Frequency Trading ◽  
Price Movements

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.

Sign in / Sign up

Export Citation Format

Share Document