Shengnan Li PhD Student, Computational Finance Centre for Computational Finance and Economic Agents (CCFEA)

Shengnan Li studied for his M Phil at the Centre for Computational Finance & Economic Agents, University of Essex in 2015-2017. He completed his PhD in computational finance in 2022. He was part of the Directional Change Project. In his PhD research, he pioneered work under directional change to relate volatility and jumps between markets.

Shengnan was examined by Dr Raju Venkata Chinthalapati (External Examiner) and Dr Michael Kampouridis (Internal Examiner) on 12th July, 2022. He was passed with minor corrections.

• Li, S., Relating Volatility and Jumps between two markets under Directional Change, PhD thesis, Centre for Computational Finance & Economic Agents, University of Essex, September 2022

  Abstract
  Directional change (DC) is a new concept in sampling financial market data. Instead of recording the transaction prices at fixed time intervals, as is done in time series, DC lets the data alone decide when to record a transaction. In DC, a data point is recorded when the price has risen or dropped against the current trend by a significant percentage, which is known as the threshold. The magnitude of the threshold is determined by the analyst. Previous studies on DC mainly focus on analysing single price sequences of one market. This thesis focuses on a new path; working on the DC comparative analysis between two markets. We propose a novel data-driven approach to combine the observed DC series of two markets into a single data sequence, which we call the DC combined sequence. This allows us to conduct a comparative analysis between two markets under DC. Based on this approach, we propose a novel indicator that measures the relative volatility between two markets. In addition, we define jumps under DC. Under this measure, we can pinpoint the size, direction, and quantity of DC jumps in a market. Lastly, under the DC comparative analysis, we build a new DC approach to identify co-jumps between two markets.

• S. Li, E.P.K. Tsang & J. O'Hara, Measuring relative volatility in high‐frequency data under the directional change approach, Intelligent Systems in Accounting, Finance and Management, 02 June 2022; http://dx.doi.org/10.1002/isaf.1510 (Our reference: LiTsOH-RelativeVolatility-ISAFM2022.pdf)

  Summary
  We introduce a new approach in measuring relative volatility between two markets based on the directional change (DC) method. DC is a data-driven approach for sampling financial market data such that the data are recorded when the price changes have reached a significant amplitude rather than recording data under a predetermined timescale. Under the DC framework, we propose a new concept of DC micro-market relative volatility to evaluate relative volatility between two markets. Unlike the time-series method, micro-market relative volatility redefines the timescale based on the frequency of the observed DC data between the two markets. We show that it is useful for measuring the relative volatility in micro-market activities (high-frequency data).