This paper proposes a double exponential jump-diffusion model for option pricing to address two empirical phenomena: the leptokurtic feature of asset returns and the volatility smile. The model combines Brownian motion with compound Poisson jumps, where jump sizes follow a double exponential distribution. This distribution has a high peak and heavy tails, capturing the skewness and kurtosis observed in asset returns. The model is simple enough to allow analytical solutions for a wide range of options, including European calls and puts, interest rate derivatives, and path-dependent options. It can also be embedded in a rational expectations equilibrium framework and has a psychological interpretation related to market reactions to news. The model is evaluated based on four criteria: internal consistency, ability to capture empirical phenomena, simplicity for computation, and economic/psychological interpretation. It outperforms other models in capturing the leptokurtic feature and volatility smile, and provides closed-form solutions for various options. The model's double exponential distribution has the memoryless property, which facilitates analytical solutions for path-dependent options. It is compared with other models such as the CEV model, normal jump-diffusion model, t-distribution models, stochastic volatility models, and affine jump-diffusion models. The double exponential jump-diffusion model is simpler and more tractable, especially for path-dependent options and interest rate derivatives. The model is shown to produce a leptokurtic return distribution with a higher peak and heavier tails than the normal distribution. It is also able to generate a volatility smile, which is a key feature in option markets. The model is embedded in a rational expectations equilibrium framework, and its parameters can be interpreted economically. The paper provides formulas for option pricing, including European call and put options, futures options, and caplets. It also discusses the limitations of the model, including the difficulty of hedging due to market incompleteness and the inability to capture volatility clustering. The model is shown to produce a close fit to observed volatility skews, demonstrating its practical relevance.This paper proposes a double exponential jump-diffusion model for option pricing to address two empirical phenomena: the leptokurtic feature of asset returns and the volatility smile. The model combines Brownian motion with compound Poisson jumps, where jump sizes follow a double exponential distribution. This distribution has a high peak and heavy tails, capturing the skewness and kurtosis observed in asset returns. The model is simple enough to allow analytical solutions for a wide range of options, including European calls and puts, interest rate derivatives, and path-dependent options. It can also be embedded in a rational expectations equilibrium framework and has a psychological interpretation related to market reactions to news. The model is evaluated based on four criteria: internal consistency, ability to capture empirical phenomena, simplicity for computation, and economic/psychological interpretation. It outperforms other models in capturing the leptokurtic feature and volatility smile, and provides closed-form solutions for various options. The model's double exponential distribution has the memoryless property, which facilitates analytical solutions for path-dependent options. It is compared with other models such as the CEV model, normal jump-diffusion model, t-distribution models, stochastic volatility models, and affine jump-diffusion models. The double exponential jump-diffusion model is simpler and more tractable, especially for path-dependent options and interest rate derivatives. The model is shown to produce a leptokurtic return distribution with a higher peak and heavier tails than the normal distribution. It is also able to generate a volatility smile, which is a key feature in option markets. The model is embedded in a rational expectations equilibrium framework, and its parameters can be interpreted economically. The paper provides formulas for option pricing, including European call and put options, futures options, and caplets. It also discusses the limitations of the model, including the difficulty of hedging due to market incompleteness and the inability to capture volatility clustering. The model is shown to produce a close fit to observed volatility skews, demonstrating its practical relevance.