Quantitative Market and Liquidity Risk Analytics
A Unified Machine Learning Framework for Proprietary Trading, Portfolio Management, and Financial Stability
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Beschreibung
This book presents a comprehensive and integrated framework for managing market and liquidity risk in trading and investment portfolios. By combining theoretical rigor with machine learning-driven modeling and empirical validation, it addresses a critical gap in current financial literature: the joint modeling of interdependent risk dimensions through advanced data science techniques.
The book bridges the divide between academic theory and real-world practice, demonstrating how machine learning can be systematically applied to model complex risk behaviors, capture regime shifts, and improve portfolio resilience. Traditional risk frameworks often struggle to handle nonlinear, high-dimensional, and heavy-tailed market dynamics. In contrast, this book integrates ML-based optimization techniques—covering liquidity proxies, price impact models, Liquidity-Adjusted Value-at-Risk (LVaR), and regime-switching volatility structures—to offer a more adaptive and robust risk management paradigm. Each chapter offers a balance of quantitative depth and application-oriented insight, enabling readers to connect rigorous research with day-to-day decision-making in risk and portfolio management.
Mazin A. M. Al Janabi is a distinguished scholar and practitioner in finance, banking, and financial engineering with more than three decades of experience spanning science, technology, and academia. He holds a PhD in Nuclear Engineering from the University of London (UK) and has held senior management roles at ING-Barings and BBVA, including Director of Global Market Risk Management and Head of Trading Risk. A Full Research Professor, he has served at leading institutions such as EGADE Business School (Mexico), United Arab Emirates University, and Al Akhawayn University (Morocco). His research, published in top-tier journals including the European Journal of Operational Research, International Review of Financial Analysis, and Annals of Operations Research, explores market and liquidity risk in both emerging and developed markets. Prof. Al Janabi is also the developer of the “Al Janabi Model” for Liquidity Risk Management, recognized in academic literature for its innovation. As a research fellow at the Economic Research Forum (ERF) and a frequent keynote speaker, he continues to shape contemporary discourse in proprietary trading, market liquidity, and financial risk analytics.
This book presents a comprehensive and integrated framework for managing market and liquidity risk in trading and investment portfolios. By combining theoretical rigor with machine learning-driven modeling and empirical validation, it addresses a critical gap in current financial literature: the joint modeling of interdependent risk dimensions through advanced data science techniques.
The book bridges the divide between academic theory and real-world practice, demonstrating how machine learning can be systematically applied to model complex risk behaviors, capture regime shifts, and improve portfolio resilience. Traditional risk frameworks often struggle to handle nonlinear, high-dimensional, and heavy-tailed market dynamics. In contrast, this book integrates ML-based optimization techniques—covering liquidity proxies, price impact models, Liquidity-Adjusted Value-at-Risk (LVaR), and regime-switching volatility structures—to offer a more adaptive and robust risk management paradigm. Each chapter offers a balance of quantitative depth and application-oriented insight, enabling readers to connect rigorous research with day-to-day decision-making in risk and portfolio management.
Unifies market and liquidity risk through machine learning to strengthen trading and portfolio decisions Covers derivatives, portfolio optimization, and machine learning to model risk under stress Applies liquidity-adjusted risk measurement, stress testing, and portfolio optimization tools
Autor*in
Mazin A. M. Al Janabi
Themen in »Quantitative Market and Liquidity Risk Analytics«
Liquidity Risk Value at Risk Computational Finance Portfolio Management Quantitative Finance Market Risk Machine Learning Trading of Securities Trading Risk Management Financial Engineering Liquidity Adjusted Value-at-Risk (LVaR)