Junhoe Choi Email & Phone Number

As a recent graduate with a Master of Arts in Economics from NYU, I am enthusiastic about the opportunity to apply for any position of 2024 cycle. My primary interest is to contribute to pricing financial derivatives and building investment strategies. Moreover, I am interested in Econometrics, causal inference and prediction models. (Bayesian and ML/DL models) With my extensive experience in utilizing various econometric models and my profound interest for financial markets, I believe I am well-suited for the roles.[Courses]Mathematics: Linear Algebra, Real Analysis, Measure Theory, Probability Theory, and Stochastic Calculus at NYU CourantEconomics: Advanced Micro,Macroeconomics, Econometrics, Bayesian Statistics ,Machine Learning modeling, and Corporate Finance at NYU. Statistics concepts: Central Limit Theorem, Markov Chains, Independent increment processes, including Poisson processes and Brownian motion. For my undergraduate years, I graduated Summa Cum Laude at Chung-Ang University in 2021 spring. I majored in Urban Planning and Real Estate and double majored in Economics. I focused on Real Estate Finance, Real Estate Development and Financial Derivatives.I have a deep passion for market forecasting models. In my Masters years, I conducted a research project under the guidance of Professors Thomas Sargent and Spencer Lyon on forecasting bubbles in the U.S. Housing Market. The objective was to predict the speculative bubbles with historical bubble signals. Employing Backward Supremum Augmented Dickey-Fuller statistics (Philips, Sho, and YU (2015)), I successfully identified the market bubble signals from 1967 to 2021. I implemented the RNNs and LSTM models to predict the future bubble signals, achieving over 80% accuracy level. Additionally, my graduate thesis involved collaborative research, where we identified early warning indicators (EWIs) for predicting financial crises using a sparse time-varying parameter (TVP) model. I conducted a comparative analysis of the TVP model with other machine learning methods such as Logit, Lasso, RNN, LSTM, and GRU. Our findings demonstrated that using our EWIs, the sparse TVP model exhibited a level of predictive accuracy on par with the recent machine learning models particularly RNN and GRU.I firmly believe that my experience in building statistical models and my knowledge of the financial market will be a great help to assist top professionals in your team. I would welcome any opportunity to discuss how I can make valuable contributions to your team. .