Teaching

Princeton University:

  • ORF 505: Statistical Analysis of Financial Data

Heavy tailed distributions and copulas. Simple and multiple linear regressions. Nonlinear regression. Non-parametric regression and classification. Neural networks: Multi Layer Perceptron, Convolutional NN, Recurrent NN (LSTM & GRU), Generative Adversarial NN.


  • ORF 515: Asset Pricing II: Stochastic Calculus and Derivatives

Pricing and hedging of advanced derivatives, including topics such as exotic options, greeks, interest rate and credit derivatives, as well as risk management. Basics of stochastic calculus necessary for finance.


  • ORF 405: Regression and Applied Time Series

Linear, nonlinear, and nonparametric (kernel and projection pursuit) regression. Neural networks, convolution networks, deep learning with Tensor Flow and Keras. Classical linear time series models (AR, MA, ARMA) in univariate and multivariate settings

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  • ORF 335: Introduction to Financial Mathematics

Notions of arbitrage and risk-neutral pricing in discrete time, specific models such as Black-Scholes and Heston in continuous time, and calibration to market data. Credit derivatives, the term structure of interest rates, and robust techniques in the context of volatility options.


Boğaziçi University:

  • IE306: Systems Simulation

Basic concepts of discrete-event simulation modeling/analysis. Event-scheduling versus Process-interaction approach. Random number and random variate generation; inverse transformation and other selected techniques. Input data analysis and goodness of fit tests. Specific computer simulation languages. Analysis of simulation output and model validation.