Research Philosophy
My research bridges traditional economic theory with cutting-edge computational methods, focusing on practical applications that address real-world challenges. I believe in the power of interdisciplinary approaches that combine econometric rigor with innovative technological solutions.
Research Areas
📊 Econometric Modeling & Time Series Analysis
My primary research focus involves developing and applying advanced econometric models to understand complex economic phenomena. I specialize in:
- GARCH Models: Volatility modeling and forecasting in financial markets
- Time Series Analysis: Long-term trend analysis and structural break detection
- Causality Analysis: Investigating causal relationships between economic variables
- Panel Data Methods: Cross-sectional and time-series data integration
🤖 Artificial Intelligence in Economics
Exploring the integration of AI and machine learning techniques with economic analysis to enhance predictive capabilities and automate decision-making processes:
- Predictive Analytics: Machine learning models for economic forecasting
- Automated Trading Systems: AI-driven investment strategies
- Natural Language Processing: Sentiment analysis of financial news and reports
- Deep Learning: Neural networks for complex pattern recognition
💹 Financial Markets & Risk Management
Investigating market dynamics, risk assessment, and portfolio optimization with particular emphasis on emerging financial instruments:
- Cryptocurrency Markets: Volatility patterns and market efficiency
- Risk Modeling: VaR, CVaR, and extreme value theory applications
- Portfolio Optimization: Modern portfolio theory and behavioral finance
- Market Microstructure: High-frequency trading and liquidity analysis
📈 Data Science & Analytics
Leveraging big data and advanced analytics to extract meaningful insights from complex economic datasets:
- Big Data Analytics: Processing and analyzing large-scale economic datasets
- Data Visualization: Interactive dashboards and statistical graphics
- Statistical Computing: Python, R, and SQL for data analysis
- Business Intelligence: KPI development and performance metrics
Current Research Projects
Asymmetric Volatility in Crypto Markets
Investigating the effectiveness of conditional variance models in predicting cryptocurrency market volatility, with focus on asymmetric responses to positive and negative shocks.
AI-Driven Economic Forecasting
Developing machine learning models that combine traditional econometric approaches with modern AI techniques for enhanced economic prediction accuracy.
Methodological Approach
🔬 Empirical Analysis
Rigorous statistical testing and validation of theoretical models using real-world data from multiple sources and time periods.
💻 Computational Methods
Implementation of advanced algorithms and simulation techniques to solve complex economic problems and test theoretical predictions.
📊 Data-Driven Insights
Combining quantitative analysis with qualitative insights to provide comprehensive understanding of economic phenomena.
Research Tools & Technologies
Programming Languages
- • Python (Pandas, NumPy, SciPy)
- • R (tidyverse, forecast)
- • SQL (PostgreSQL, MySQL)
- • VBA (Excel automation)
Econometric Software
- • EViews
- • Stata
- • GRETL
- • SPSS
Machine Learning
- • scikit-learn
- • TensorFlow
- • PyTorch
- • XGBoost
Data Platforms
- • Bloomberg Terminal
- • Reuters Eikon
- • FRED Economic Data
- • Yahoo Finance API