Paul Kara

Head of Quantitative Research @Alphalayer

Burlington, ON, CA
MOBILE NUMBERS
+91 *********19

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WORK HISTORY

Jan 2021 — Present

Head of Quantitative Research @Alphalayer

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Asset management research for AlphaLayer, a new organization within AltaML, which builds custom front-office machine learning product solutions for the financial investment industry. Mentor and guide a team of PhD researchers working on developing ML alpha generation solutions for our clients in the investment industry.

EDUCATION

2009 — 2014

University of Toronto

Doctor of Philosophy (Ph.D.), Time series econometrics

2008 — 2009

University of Toronto

Master of Arts (M.A.), Economics

2004 — 2008

University of Guelph

Bachelor of Arts (B.A.), Economics

SKILLS

StataStatisticsForeign Exchange (Fx) TradingData AnalysisHidden Markov ModelsPythonMacroeconomicsPortfolio ManagementState-Space ModelsDerivativesWolfram MathematicaMatlabFinanceFortranRisk ManagementTime Series Econometrics Research and ApplicationParallel ComputingMachine LearningDynamic Term Structure ModelsMarkov Chain Monte CarloCFixed IncomeFrequency Domain AnalysisResearchRisk-Neutral Asset Pricing TheoryQuantlibRQuantitative FinanceBayesian StatisticsNvidia Cuda KernelsQuantitative ResearchOptionsEconometrics

ABOUT PAUL KARA

A quantitative finance professional with experience in academia, global macro, fixed income, and long/short equities statistical arbitrage. PhD in a mathematical field. Previously worked for WorldQuant LLC managing of one of their independent stat arb portfolios. Other previous employment included the Canada Pension Plan Investment Board, Global Tactical Asset Allocation unit, doing global macro research. Currently working for AlphaLayer, a startup committed to building FinTech using Statistical Machine Learning.Previously worked on time series econometric academic research. Academic statistics research interests include modeling of financial time series dynamics as well as nonlinear machine learning models and their application to forecasting financial time series. Past research has involved macroeconomic series and financial volatility forecasting, including the use of stochastic volatility, as well as stochastic state-space filtering, mixed causal/noncausal autoregressions, MCMC, Bayesian Econometrics, among other modern statistical techniques.

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Paul Kara — Head of Quantitative Research at Alphalayer in Burlington, ON, CA | Unifers