ADVANCED STATISTICAL APPROACHES IN QUANTITATIVE DECISION MAKING
Keywords:
Optimal Decision Making, Lévy Processes, Queueing Dependence, Copula, Extreme Value Theory, Jump-Diffusion, Financial Modeling, Risk Analysis, Stochastic Process, Applied StatisticsSynopsis
This volume synthesizes advanced statistical approaches for quantitative decision making under uncertainty. It brings together Lévy processes, jump–diffusion dynamics, copula-based dependence modelling, and extreme value theory to address heavy tails, skewness, and clustered volatility beyond Gaussian assumptions. Applications span option pricing and risk measurement, multivariate drought analysis, and tail dependence between equity indexes and exchange rates. The theoretical sections cover probability spaces, filtrations, and martingales, while the applied sections discuss Merton/Kou jump–diffusion models, pure-jump families (NIG/VG/CGMY), and copula selection/validation. The book serves as a method–application bridge for researchers and policymakers aiming to develop robust decision rules.
Chapters
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INTRODUCTION TO LÉVY PROCESSES FOR QUANTITATIVE DECISION MODELS: THEORETICAL FRAMEWORK
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JUMP-DIFFUSION AND PURE JUMP PROCESSES IN QUANTITATIVE FINANCE MODELS: LÉVY-BASED APPROACHES
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COPULA-BASED DROUGHT ANALYSIS AND DROUGHT PREDICTIONS
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ANALYSIS OF TAIL DEPENDENCE WITHIN THE FRAMEWORK OF COPULA AND EXTREME VALUE THEORY: BIST INDUSTRIAL INDEX-EXCHANGE RATE APPLICATION
