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The handbook is an authoritative four‑volume reference that captures the latest advances in financial econometrics, statistical modeling, machine learning, and risk management. Drawing on decades of combined experience in industry practice, academic teaching, research, and journal editorship, the editors and contributors present a rigorous yet practical framework that connects theory, methodology, and real‑world application.Covering a wide range of topics—from asset pricing and derivatives to corporate finance, systemic risk, and big‑data analytics—this handbook offers an integrated and forward‑looking view of modern quantitative finance, with particular emphasis on higher‑moment theory, data‑driven decision‑making, and contemporary risk management challenges.
Volume 1: Foundations and Methodological Innovations
Focuses on the core tools of financial econometrics and quantitative analysis, introducing foundational models alongside innovative methods. Topics include machine learning in risk management, optimal futures hedging, corporate innovation and executive compensation, option bound determination, banking stability, and systemic risk measurement.
Volume 2: Advanced Financial Theory and Machine Learning Applications
Centers on advanced asset pricing and derivative theory, integrating machine learning techniques into financial decision‑making. Key themes include stochastic volatility, implied variance, real and exotic options, international diversification, AI‑driven stock selection, and credit risk assessment.
Volume 3: Corporate Finance, Empirical Analysis, and Risk Applications
Emphasizes practical and empirical issues in corporate finance and investment management. This volume examines international transfer pricing, corporate restructuring, executive incentive schemes, mutual fund performance, market forecasting, and innovative hedging, capital budgeting, and nonlinear modeling approaches.
Volume 4: Big Data, Advanced Econometrics, and Financial Stability
Explores the integration of big data analytics and advanced econometric techniques in financial markets. Topics include corporate governance and earnings management, stock–exchange rate dynamics, survival analysis, deep neural networks for credit risk, and volatility spillovers during financial crises.
Designed for finance, accounting and economics undergraduate students and graduate students; academics in the fields of finance and accounting.






