Hamidreza Khoshro

Biography

I am a Ph.D. candidate in Finance at McMaster University, specializing in Financial Econometrics and Bayesian Time Series Analysis. My research centers on creating sophisticated Bayesian models to better capture the complex dynamics within financial data.

In a recent project, I examined the relationship between equity and bond markets during the COVID-19 crisis. Using a multivariate GARCH model framework, I designed a novel time-varying semiparametric approach to estimate the joint distribution of asset returns. This method models the entire distribution of returns for both equities and bonds, providing not only insights into correlation but also a deeper understanding of tail behavior and other key distributional features. These advancements offer valuable tools for risk management and portfolio optimization, particularly in volatile markets. Additionally, I developed a Bayesian clustering model to classify hedge funds based on their performance, offering investors critical insights for more effective fund selection.

My research advances the field of Applied Econometrics and Quantitative Finance and has practical applications in quantitative risk management as well as systematic investment strategies. I leverage skills in programming (R, C, Python) and Bayesian inference and am passionate about translating complex models into real-world financial solutions.