Asset returns based on low frequency prices (e.g. end-of-day quotes) are still dominating modern portfolio analysis. To make portfolio metrics more relevant intraday and improve precision of estimates, new data frequency needs to be explored. In this presentation we demonstrate how using high frequency market data for portfolio risk management and optimization could improve the classic variance-bias trade-off and bring new insights to strategy backtesting. We illustrate our examples using PortfolioEffectHFT package for R statistical software. Since high frequency prices require special handling, we discuss key components of a model pipeline for microstruture noise, price jumps, outliers, fat tails and long-memory. We conclude our presentation with an introduction to high frequency portfolio optimization built on top of intraday portfolio metrics. Stephanie Toper is a director of portfolio analytics at PortfolioEffect. Stephanie spent 8 years as a quantitative developer at Karya Capital, UBS and Societe Generale and was a senior risk analyst at MF Global. She has extensive experience in interest rate derivatives and quantitative library development. She holds a Master’s degree in Mathematics of Finance from Columbia University and a Master’s in Applied Mathematics and Computer Science from ENSIMAG, France.
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