JUMP-DIFFUSION AND PURE JUMP PROCESSES IN QUANTITATIVE FINANCE MODELS: LÉVY-BASED APPROACHES
Synopsis
Traditionally, continuous models based on Brownian motion and the assumption of normal distribution (such as Black-Scholes) have been used to model financial asset prices. However, recent empirical studies demonstrate that financial return distributions are skewed, exhibit heavy tails and volatility clustering, and that price paths are not continuous but rather contain sudden jumps. This study highlights the inadequacy of classical diffusion models, particularly in explaining short-term price movements and market microstructure, through comparisons using SLM stock and S&P 500 index data. The study focuses on Lévy-based approaches, which better represent deviations from the normal distribution and discontinuities in market dynamics. In this context, jump-diffusion and pure jump processes, which have the capacity to explain the volatility smile in option pricing and produce more realistic results in risk management, are examined. The theoretical foundations of these models and their advantages in financial modeling are discussed in detail.
