Interests

Computational NLP & Textual Analysis

  • Text-Based Economic Indicators: Pioneering methodologies to extract latent structural and sentiment signals from unstructured data to quantify economic phenomena.
    • Algorithmic ESG Analytics: Developed a novel, daily text-based Environmental, Social, and Governance (ESG) indicator by scraping and processing an extensive corpus of over 390,000 online news articles to outperform standard market risk models.
    • Linguistic Coherence & Macro Performance: Designed automated text-segmentation pipelines and natural language processing (NLP) algorithms to construct computational “textual coherence” indices from historical multilingual textbooks, linking educational alignment to long-run GDP growth via VECM frameworks.
    • Automated Dictionary Design: Engineering multi-step, domain-specific dictionary-driven frameworks evaluated through unsupervised learning (K-means clustering, TF-IDF, and PCA) to systematically map and categorize core concepts across major economic schools of thought.
    • Policy & Treaty Analytics: Deploying advanced text-mining pipelines—incorporating subjectivity scoring, intensifiers, and weighted token aggregation—to empirically measure the legal precision, “hardness,” and diplomatic rigor of multilateral international agreements.
    • Scalable Data Engineering: Implementing large-scale web scraping, automated text classification, and topic modeling architecture to structure massive, unorganized text corpora for econometric deployment.

Advanced Time-Series Econometrics & Financial Risk

  • Systemic Risk & Dynamic Modeling: Specializing in structural time-series econometrics to capture market volatility, financial contagion, and structural breaks across macro-financial landscapes.
    • Mixed-Frequency Volatility: Expert in long-term volatility modeling using mixed-frequency data frameworks, specifically deploying GARCH-MIDAS and BEKK specifications to study macro-financial variables and asset dynamics.
    • Macro-Finance & Policy Shocks: Evaluating risk transmission and the impacts of monetary policy shifts, global events, and commodity price fluctuations (e.g., oil prices, inflation) using regime-shift models and deep learning (LSTM) techniques.
    • High-Frequency Risk Estimation: Implementing advanced econometric methods to optimize financial risk management parameters, including Value-at-Risk (VaR) utilizing CAViaR-X frameworks, and Expected Shortfall (ES).
    • Time-Frequency Scaling: Applying Continuous Wavelet Transforms (CWT / MODWT) combined with stochastic processes (Ornstein-Uhlenbeck) to decompose complex economic interactions and isolate market contagion across distinct temporal scales.

Empirical Computing & Statistical Modeling

  • Full-Stack Analytical Pipeline: Leveraging specialized programming languages as cohesive tools for end-to-end data harvesting, structural modeling, and institutional visualization.
    • Python Ecosystem: Advanced execution of computational linguistics, web scraping architectures, unstructured data parsing, and predictive deep learning applications (NLP, Scikit-Learn, TensorFlow/Keras).
    • R Environment: Production-level econometric programming for advanced time-series estimation, risk forecasting, and multivariate volatility frameworks (GARCH, VaR, VECM).
    • MATLAB Platform: Specialized development of signal processing algorithms and time-frequency scale decompositions using wavelet toolboxes for financial time series.
    • Academic Infrastructure: Expert in LaTeX document compilation for technical reporting, publishing peer-reviewed research, and translating intricate mathematical models into clean, scannable academic presentations.