MSc project: Deep Pricing in the CEV model
Supervisor: Dr. John Armstrong
The application of machine learning in finance receives more and more attention. In this paper, we price American put options under the CEV model with finite difference method. Then we use a neural network to approximate the pricing map from model parameters to option prices, which realizes rapid computation of option price. We also use market data downloaded from Bloomberg to calibrate the CEV model. In addition, we estimate value at risk of a portfolio containing American put options and train another neural network mapping model parameters to value at risk.
MSc project: Predictable Forward Performance Process in Binomial Tree Model with Robo-Advising Application
Supervisor: Dr. Liang Gechun
we derive the discrete-time predictable m-forward performance processes in the case of logarithmic utility function and exponential utility function. Next, we compare the solutions for the single-period investment problem using two different approaches: the classical expectation maximization method and the method involving the forward performance process. We also discuss the Robo-advising application of predictable m-forward performance processes.