Scaling-law has been observed in an extraordinary wide range of natural phenomena, from physics, biology, earth and planetary sciences, economics and finance, computer science, and demography to the social sciences. Scaling-law processes yield scaling properties for a broad range of values, sometimes for many orders of magnitude. Using the event-driven paradigm of directional changes and overshoots to analyse Foreign Exchange (FX) markets, Glattfelder, Dupuis, and Olsen (2011) discovered 12 independent scaling laws in high frequency FX data. In finance, where frames of reference and fixed points are hard to come by and often illusory, these new scaling laws provide a reliable framework for developing risk management tools and represent the foundation of a completely new generation of tools for studying high frequency market volatility. The objective of this project is to study the dynamic behaviour of markets and improve the quality of the inferences and predictions we make about the behaviour of prices. Using high frequency FX dataset (provided by Olsen Ltd), we aim to extract patterns from short-term market activity to infer, measure, and predict volatility over longer time horizons. The fellow will be actively mentored by Dr. R. Olsen (Olsen Ltd).
The project will provide robust risk management tools for the high frequency trading (HFT) community and advance a theoretical and empirical explanation for the occurrence of scaling law for both canonical models (Brownian Motion, Levy processes etc.) and high frequency FX data. The risk management tool will be extended to a multi-asset case and tested and validated with empirical data (FX rates).
Early Stage Resercher working on the project: Vladimir Petrov