ARCH models in Python

Hurst exponent evaluation and R/S-analysis in Python

Foreign Exchange Forecasting Model created for the paper "Can Interest Rate Factors Explain Rate Fluctuations?"

Companion to publication "Understanding Jumps in High Frequency Digital Asset Markets". Contains scalable implementations of Lee / Mykland (2012), Ait-Sahalia / Jacod (2012) and Ait-Sahalia / Jacod / Li (2012) Jump tests for noisy high frequency data

SMARTboost (boosting of smooth symmetric regression trees)

In this project, I explore various machine learning techniques including Principal Component Analysis (PCA), Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Sentiment Analysis in an effort to predict the directional changes in exchange rates for a list of developed and developing countries.

Code and documents from Econ 690 at Duke

Code for the paper "Realized Semi(Co)Variation: Signs that All Volatilities are Not Created Equal"

bayesian-sgdlm is a Python script for fully Bayesian SGDLMs, treating each node as a VAR( 𝑝) DLM. It leverages decouple–recouple filtering with Variational Bayes and importance sampling to estimate sparse, time-varying cross-lag dependencies (including pandemic dummies) without ever inverting the full multivariate system.

My project (in R) about analyzing the effect of the first COVID-19 outbreak to the Vietnam's stock market.

Evaluating the Impact of Macroeconomic Indicators on S&P Prices

Find the best characteristics using various models to best predict the future returns

Bayesian inference for Generalized Autoregressive Score models.

Coding projects I have worked on, in R and Python. Predominantly includes utilizing code to recreate the Black Sholes Model, Greek Option calculator, Stochastic Process and Brownian Motion and other data science applications for finance. Python was also used primarily for machine learning applications in finance, using various functions from skl…

SMARTboost (boosting of smooth symmetric regression trees)

This is a project replicating the result of John Cochrane's famous paper about return's predictability (https://www.jstor.org/stable/40056861)

End-to-End Python implementation of Regime-Weighted Conformal (RWC) prediction for sequential VaR control in nonstationary financial markets (Schmitt, 2026). Combines kernel-based regime similarity with exponential time decay to calibrate distribution-free risk bounds. CRSP data validation, GBDT quantile forecasting, and rigorous backtesting.

This repository includes different R scripts (with the data used) for the study and application of different topics from the study of Econometrics.

Introduction to Python programming language, with a focus on basic data analysis and financial economics applications.

End-to-End Python implementation of bi-level optimization for financial time-series synthesis from "History Is Not Enough" by Xia et al. (2026). Implements cointegration-aware data augmentation, curriculum learning, and meta-learned augmentation policies to immunize quantitative models against concept drift and market non-stationarity.