Kylie-Anne joined UTS in January 2019 after completing her PhD on Limit Order Book Dynamics at the University of NSW. She was awarded the QRSLab Boronia Managed Funds PhD Scholarship in 2011. Kylie-Anne also holds a Master’s degree in Finance from The University of Hong Kong. Her research and teaching interests are in financial markets, sustainable finance, green finance, ESG, high frequency finance, market microstructure, statistics and econometrics.
Kylie-Anne has had extensive industry experience both domestically and overseas. She began her career as a Quantitative Analyst at Investment Technology Group. Kylie-Anne was Head of Financial Engineering for Asia Pacific at Macquarie Group in Hong Kong and subsequently, Head of Indexation and Quantitative Trading Research at CLSA in Sydney. Most recently she held the position of Director, Portfolio Manager at QTR Capital, a proprietary trading business.
Financial Markets, Sustainable Finance, Green Finance, ESG, Market Microstructure, High Frequency (limit order book) Empirical Finance, Statistical Method Development and Modelling of High Frequency Data, Time Series Analysis, Quantitative Finance, Computational Finance.
Financial Modelling and Analysis
Ethics in Finance
Richards, K-A, Peters, GW & Dunsmuir, W 2015, 'Heavy-tailed features and dependence in limit order book volume profiles in futures markets', International Journal of Financial Engineering, vol. 02, no. 03, pp. 1550033-1550033.View/Download from: Publisher's site
This paper investigates fundamental stochastic attributes of the random structures of the volume profiles of the limit order book. We find statistical evidence that heavy-tailed sub-exponential volume profiles occur on the limit order book and these features are best captured via the generalized Pareto distribution MLE method. In futures exchanges, the heavy tail features are not asset class dependent and occur on ultra or mid-range high frequency. Volume forecasting models should account for heavy tails, time varying parameters and long memory. In application, utilizing the generalized Pareto distribution to model volume profiles allows one to avoid over-estimating the round trip cost of trading.