SKILLS and COMPETENCIES

Computing and data science applied to finance

  • Data exploration and mining, with particular emphasis on handling large and complex financial

    data sets.

  • Derivative pricing models. Theory and practice of Binomial trees and Montecarlo methodology

    for European and American options and the Greeks.

  • R, Matlab and Python implementation of financial models and evaluation of different types of

    derivative securities: European, American, standard, barrier and path dependent options on

    stocks and interest rates.

Financial Math

  • Brownian motion, martingales, Ito integrals and Ito’s formula applied to Black-Scholes model

  • Fundamental statistical tools useful for fitting models to financial data, including those with complex

    multivariate form. Basic statistical and probability theory, statistical distributions, copulas,

    Bayesian inference, hypothesis testing, and regression.

  • Optimization models and their applications to finance problems ranging from asset allocation to

    risk management.

  • Financial time series analysis including ARIMA modeling, forecasting, seasonality,

    model identification and diagnostics. GARCH and stochastic volatility modeling

Markets

Options

  • Investment Strategies: Options strategies for investment purposes.

  • Managing Option Positions

  • Volatility trading: Various strategies including Long and Short Straddles, Long and Short

    Strangles, Butterflies, and Condors.

Fix Income

  • Derivative securities: swaps, caps, floors, swaptions, and options on bonds.

  • Interest rate derivative valuation. Ho-Lee and and Black-Derman-Toy models.