INTRODUCTION TO LÉVY PROCESSES FOR QUANTITATIVE DECISION MODELS: THEORETICAL FRAMEWORK
Synopsis
Introduction to Lévy Processes for Quantitative Decision Models: Theoretical Framework Accurate modeling of uncertainty in quantitative decision-making models is critical for the analysis of economic and financial systems. Brownian motion, frequently used in traditional models, falls short in explaining complex phenomena observed in financial markets such as sudden price jumps, volatility clustering, and heavy-tailed distributions. This study aims to present the theoretical framework of Lévy processes, which integrate drift, diffusion, and jump components within a unified structure. The study examines the historical development of stochastic processes, Lévy-Itô decomposition, the Lévy-Khinchine formula, and the mathematical properties of these processes in detail. This theoretical infrastructure, presented as an alternative to the limitations of deterministic approaches, constitutes the foundation for models to be developed in areas such as modern finance, risk analysis, and derivative pricing.
