Ali Jaffri, PhD

Dr. Ali Muqadas Jaffri, CFA, is an Assistant Professor of Practice in Finance at the College of Business, North Dakota State University (NDSU). He holds a Ph.D. in Economics from Texas Tech University, specializing in Financial Economics, and is a Chartered Financial Analyst (CFA) Charterholder. At NDSU, Dr. Jaffri teaches courses including Analysis of Fixed Income Securities, Advanced Data Analytics in Finance, and Advanced Bank Management, equipping students with analytical and applied skills to tackle complex financial challenges.

Before joining academia, Dr. Jaffri built a strong foundation in the financial industry through leadership roles in risk management and financial institutions. He served as Associate Manager of Market Risk at Allied Bank and Manager of Financial Institutions Risk Management at MCB Bank Limited, where he specialized in market, credit, and operational risk frameworks and spearheaded the automation of regulatory reporting systems.

Dr. Jaffri’s research and teaching approach integrate rigorous quantitative modeling, machine learning applications, and real-world financial practices. His published and ongoing works explore portfolio optimization, asset pricing, financial market volatility, geopolitical and environmental risk measurement, and advanced econometric and machine learning methods.

Current Projects

• Forecasting flash crashes with subordinated Lévy processes.
• Developing a Global FX Stress Index and its link to the U.S. yield curve.
• Advancing portfolio optimization under semi-heavy-tailed distributions.
• Demystifying transformer-based NLP models for finance education and portfolio applications.

Dr. Jaffri’s research lies at the intersection of financial econometrics, machine learning, and risk management, focusing on asset pricing, portfolio optimization, and systemic risk modeling. His work leverages advanced statistical, econometric, and computational techniques to tackle contemporary challenges in financial markets.

Key Research Areas

• Portfolio Optimization & Asset Allocation
  • Development of robust optimization frameworks under semi-heavy tails and structural breaks.
  • Research on adaptive minimum-variance portfolios and ESG-driven investment strategies.
  • Application of affine GARCH, subordinated Lévy processes, and minimum risk rate frameworks for portfolio construction.
• Market Volatility & Flash Crash Forecasting
  • Modeling extreme tail risks using subordinated Lévy processes and stable distributions.
  • Forecasting market dislocations and designing early warning indicators for systemic events.
• Geopolitical, Climate, and Environmental Risks
  • Construction of a Unified Financial Index capturing geopolitical and climate policy uncertainty.
  • Applications in derivative pricing, systemic risk measurement, and global portfolio diversification.
• Machine Learning & NLP in Finance
  • Application of transformer-based models (e.g., FinBERT) for sentiment-driven asset allocation.
  • Integrating natural language processing (NLP) with market signals to improve investment decision-making.
• Macroeconomic Policy & International Finance
  • Examining the spillover effects of U.S. unconventional monetary policy on OPEC+ countries.
  • Studying congestion pricing impacts on transportation emissions and economic behavior.