Epstein Secrets

ucially contributed to an analytical model design and filed a patent for this model. This included expanding on theoretical framework of R. Almgren, R. Kissel and others by enabling real-time optimization capability for the algorithm via the derivation of a certain closed form analytical utility function

P, Adaptive filtering, Kalman filters, Hidden Markov Model • Kernel Methods, Support Vector Machines • Cointegration and Error Correction models, Vector Auto Regression, ARCH and Granger causality, GARCH models, ARIMA. Panel data modeling Fixed and Variable effects models • MARS, Boosting methods, Regression trees

ucially contributed to an analytical model design and filed a patent for this model. This included expanding on theoretical framework of R. Almgren, R. Kissel and others by enabling real-time optimization capability for the algorithm via the derivation of a certain closed form analytical utility function

, Adaptive filtering, Kalman filters, Hidden Igarkov Model • Kernel Methods, Support Vector Machines • Cointegration and Error Correction models, Vector Auto Regression, ARCH and Granger causality, GARCH models, ARIMA. Panel data modeling: Fixed and Variable effects models • MARS, Boosting methods, Regression tree

Pre-Trade Arrafrsis: designed and implemented advanced portfolio pre-trade analysis model which had significant advantage over regular models, e.g. R. Kissel, by accounting for serial correlations in both Liquidity and Portfolio risk estimation. • Agen9' Strategy Suite: implemented a number of trading st

P, Adaptive filtering, Kalman filters, Hidden Markov Model • Kernel Methods, Support Vector Machines • Cointegration and Error Correction models, Vector Auto Regression, ARCH and Granger causality, GARCH models, ARIMA. Panel data modeling Fixed and Variable effects models • MARS, Boosting methods, Regression trees