### TABLE I Comparison summary of derived distribution parameters for the log-normal, log-uniform and log-double-exponential jump-diffusion models, respectively.

### Table III Genetic Programming Model Specification in a Jump-Diffusion World Panel A: Training Variables

1998

Cited by 7

### Table IV Performances of the Genetic Programming Model and the Black-Scholes Model in a Jump-Diffusion World We generate the underlying stock price as a jump-diffusion process. The model specifications for Genetic Programming are specified in Table IV. Pricing errors are presented for six Genetic Programming algorithms that use alternate methods for generating new populations from the previous generation. Each cell in the table presents the average pricing-error over the entire sample of options generated in each sample set. Parent Selection Criteria: Best Mean Absolute Error Mean Percentage Error

1998

Cited by 7

### Table V Performance of Genetic Programming Model, Black-Scholes Model, and Linear Models in a Jump-Diffusion World Pricing errors are presented for six Genetic Programming formulas using alternate methods for generating new populations from the previous generation and for four linear models that are a function of the initial stock price, exercise price, and time to maturity. Each cell in the table presents the average pricing errors over ten sets of stock and option prices and for the entire sample of options generated in each set. Parameter values used to generate stock price and options data and the Genetic Programming parameters are given in Table IV.

1998

Cited by 7

### Table II Effect of Different Rebalancing Frequencies on the Dynamic Delta Hedge Entries report the summary statistics (mean, standard error, root mean squared error (RMSE), mean absolute error (MAE), mean short fall (MSF), minimum, maximum, skewness, and kurtosis) of the hedging error of a one-year call option based on a dynamic delta hedge with different rebalancing frequencies. The hedging error is defined as the difference between the value of the hedging portfolio and the value of the target call option at the closing time of the month-long exercise. The statistics are computed based on 1,000 simulated paths of the Black-Scholes model (Panel A) and the Merton jump- diffusion model (Panel B)assuming that the hedger knows the exact model in forming the portflios. In Panel C, the sample paths and option prices are simulated based on the Merton model, but we assume that the hedger does not know this information and is formed to form the hedging portfolios based on the Black-Scholes forumla, with ad hoc adjustments to accommodate the observed implied volatility.

### Table 4: Parameters for the jump diffusion case.

2007

### Table 4 shows that neither the type of model (dependent or independent) nor the forms of the model type persist

"... In PAGE 6: ... Given a constant variance, the independent models (the mixed jump diffusion model or the discrete mixture of normals model) should fit the data better. Tables 2 and 3 present each models daily maximum likelihood values, and Schwarz criteria; Table4 summarizes the results. For the twelve five year subperiods, ten subperiods have p-values lt; .... In PAGE 7: ...Table4... ..."

### TABLE 1: Input data used to value European options under the lognormal jump diffusion process. These parameters are approximately the same as those reported in Andersen and Andreasen (2000) using European call options on the S amp;P 500 stock index in April of 1999.

2005

Cited by 15

### TABLE 8: Number of iterations for a European call option under jump diffusion using Crank-Nicolson timestepping. The input parameters are provided in Table 1. The convergence tolerance tol is de ned in equation (5.4).

2005

Cited by 15

### Table 1: Summary of yearly coefficients for Log-Normal- Diffusion, Log-Uniform-Jump estimated parameters by least squares (variance of deviation between S amp;P500 and jump-diffusion histograms) with respect to the variable dt given dt = T j y and constraints mentioned in the

in Portfolio Optimization with Jump-Diffusions: Estimation of Time-Dependent Parameters and Application