Jump Diffusion Models

Algorithm

Jump diffusion models represent a stochastic process extending the Black-Scholes framework by incorporating both Brownian motion, capturing continuous price changes, and a Poisson jump process, modeling sudden, discrete price movements. These models are particularly relevant in cryptocurrency and derivatives markets where jumps, driven by news events or exchange-specific incidents, frequently deviate from log-normal diffusion assumptions. Parameter estimation often relies on efficient methods like maximum likelihood estimation, calibrated to observed option prices and volatility smiles, providing a more realistic representation of asset price dynamics. Consequently, they enhance the accuracy of option pricing and risk management strategies, especially for out-of-the-money options sensitive to extreme events.