A New Method for Jump Detection

The Essex Finance Centre (EFiC) warmly invites you to join Dr Maggie Chen as guest speaker at the next instalment of the EFiC Research Seminar Series. 

  • Wed 5 Feb 20

    14:00 - 16:00

  • Colchester Campus


  • Event speaker

    Dr Maggie Chen, Reader in Financial Mathematics, Cardiff University.

  • Event type

    Lectures, talks and seminars
    Essex Finance Centre (EFiC) Research Seminar Series

  • Event organiser

    Essex Business School

  • Contact details

    Dr Nikolaos Vlastakis

This seminar brought to you by the Essex Finance Centre (EFiC) Research Seminar Series introduces Dr Maggie Chen from Cardiff University presenting her work on jump detection.

Seminar Abstract

Jump detection in financial time series has evolved over the past few decades due to several factors.

The first is increased empirical of the impact of jumps on returns and volatility.

The second reason is the increased frequency of jumps with the advent of high-frequency finance and computer-to-computer trading.

Third, and as a result, risk assessment, stop-loss positions, portfolio rebalancing, flash crashes and other ramifications may be triggered by jumps.

Jump detection methods however, have often treated a jump as a singular, random and isolated shock significantly standing out from the rest of the values of the time series. Most jump detection techniques were not designed to capture consecutive jumps, which may constitute a massive shock to the financial system even if the individual jumps are relatively modest in size.

Most importantly, successive jumps often reflect a behavioural contagion among market agents, be they human or machines. Thus, from the financial-economic perspective we advance the BCH method to account more accurately for both singular jumps and consecutive jumps.

This examines the size of individual returns with a measure of local volatility based on running medians of absolute returns. Also comparing this method with existing methods such as the RV-BV method, or Lee and Mykland (2008). The comparison is carried out on empirical S&P 500 data as well as simulated data.

It is found that the BCH method generally out performs other jump detection methods.


This is a free event and there is no need to book in advance. Please feel free to bring your friends, colleagues and classmates along.

Speaker bio

Dr Jing (Maggie) Chen is a Reader in Financial Mathematics at Cardiff University. She has also served in Swansea University, Columbia University (NYU) and University College London.

Her research interests are on the following;

  • market microstructures 
  • volatility and financial jumps
  • complex financial systems and stability
  • utilising theory-underpinned modelling techniques to provide robust evidence 

She is also one of the pioneers to introduce Hawkes processes into finance applications, which has led her to edit a couple of special issues for major financial journals recently.

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