This paper proposes a double exponential jump-diffusion model for option pricing, addressing the limitations of the Black-Scholes model in capturing the leptokurtic feature and the "volatility smile" observed in financial markets. The model combines Brownian motion and a compound Poisson process with double exponentially distributed jump sizes, allowing for analytical solutions to various option pricing problems, including European call and put options, interest rate derivatives, and path-dependent options. The model is embedded in a rational expectations equilibrium framework and has a psychological interpretation, reflecting overreaction and underreaction to news. The paper evaluates the model's performance through four criteria: internal consistency, empirical fit, computational tractability, and interpretability. It is compared with other models, such as the CEV model, normal jump-diffusion model, and stochastic volatility models, highlighting its advantages in terms of analytical tractability and empirical fit. The model's ability to produce "volatility smiles" is illustrated using real data from the Japanese LIBOR market. However, the model has limitations, including complex pricing formulae and difficulties in hedging due to the jump component.This paper proposes a double exponential jump-diffusion model for option pricing, addressing the limitations of the Black-Scholes model in capturing the leptokurtic feature and the "volatility smile" observed in financial markets. The model combines Brownian motion and a compound Poisson process with double exponentially distributed jump sizes, allowing for analytical solutions to various option pricing problems, including European call and put options, interest rate derivatives, and path-dependent options. The model is embedded in a rational expectations equilibrium framework and has a psychological interpretation, reflecting overreaction and underreaction to news. The paper evaluates the model's performance through four criteria: internal consistency, empirical fit, computational tractability, and interpretability. It is compared with other models, such as the CEV model, normal jump-diffusion model, and stochastic volatility models, highlighting its advantages in terms of analytical tractability and empirical fit. The model's ability to produce "volatility smiles" is illustrated using real data from the Japanese LIBOR market. However, the model has limitations, including complex pricing formulae and difficulties in hedging due to the jump component.