Ismael ASSANI

About me

I am Beninese and a PhD candidate in statistics at UdeM. My PhD's project is about hedging derivatives under basis risk in discrete time. I plan to continue in the long term on quantitative finance. I am interested in the modeling of financial series in particular by hidden Markov models, hedging and pricing of derivatives as well as the application of machine learning methods in finance. Recently, i work on "An Input-Output HMM to describe states of energy price in NYISO Market". If you want to collaborate on a project, feel free to contact me. My hobbies are mainly football and puzzle games.

Outputs

Quadratic Hedging in Discrete Time with Basis Risk

Basis risk arises whenever one hedges a derivative using an instrument different from the underlying asset. Recent literature has shown that this risk can significantly impair hedging effectiveness. In this work, we derive new semi-explicit expressions for discrete-time local and global quadratic hedging strategies under basis risk. The resulting solutions cover a wide range of derivatives and asset dynamics and are based on the inverse Laplace transform representation of the derivative. Moreover, we investigate whether quadratic hedging performs better at mitigating basis risk when compared to naive delta hedging strategies that are often used in practice. Finally, a sensitivity analysis is performed to evaluate the impact of the correlation coefficient, the option maturity and of the rebalancing frequency on the hedging performance.

An Input-Output HMM to describe states of energy price in NYISO market

The energy market in New York is centralized and managed by an independent system operator called NYISO. The price of energy on this market is determined by an auction mechanism. The Analysis of this price reveals that it is very volatile for several reasons (uncertain and inelastic demand, dynamic changes in the behavior of participants, etc.) that can be represented by models indexed by Markov chains. In this work, an Input-Output HMM models is used to predict the price of energy and describe its hidden states. This model is well known for its ability to predict conditional price density. In order to be able to better describe the states (especially if they are numerous), a parsimonious parameterization is used.

Doctoral scholarship

This project aims to provide more general explicit formulas for hedging in the presence of basis risk that can take into account several dynamics and derivatives. In addition, in the current context of the crisis caused by COVID-19, this research project will contribute not only to providing a practical tool for investors but also results that can serve as a basis for researchers in the field of finance.