Quantitative and Algorithmic Trading
Skew maintains an active portfolio of equities and equity options; the fund seeks to maximize risk-adjusted returns while maintaining a low correlation to the broader market. It does so by taking educated short positions and positions on future realized volatility that are largely independent of the market return.
Fundamental analysis and rescaled range analysis are used to profit from fast moving markets and gain passive income from choppy markets.
A high-frequency algorithm allows the fund to capitalize on spreads in new markets while reacting to the changing market microstructure.
Matthew, who formally worked in the hedge fund industry as a quantitative researcher, now develops and executes the quantitative strategies of the fund. Matthew has a PhD in mathematics from The University of California at Irvine.
Simon has 6 years software experience working at startups initially and recently he worked as an Associate at Capital One. Simon received his Master's Degree in Computer Science from Syracuse University.
Xi has programmed for 5 years professionally at a FinTech startup in Manhattan. Xi received his Master's Degree in Computer Science from NYIT.
The multi-variate geometric fractional brownian motion (mGFBM) and applications to the Skew Strategy.
Market Making models for equities and equity options.
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