Stable Paretian modeling in finance: some empirical and theoretical aspects
A practical guide to heavy tails
Risk management and quantile estimation
A practical guide to heavy tails
Stable distributions and the term structure of interest rates
Mathematical and Computer Modelling: An International Journal
Option pricing for a logstable asset price model
Mathematical and Computer Modelling: An International Journal
Computing the probability density function of the stable Paretian distribution
Mathematical and Computer Modelling: An International Journal
Operator geometric stable laws
Journal of Multivariate Analysis
Bayesian inference for α-stable distributions: A random walk MCMC approach
Computational Statistics & Data Analysis
Portfolio optimization when risk factors are conditionally varying and heavy tailed
Computational Economics
Improved estimation of the stable laws
Statistics and Computing
Calibrated FFT-based density approximations for α-stable distributions
Computational Statistics & Data Analysis
Testing the stable Paretian assumption
Mathematical and Computer Modelling: An International Journal
Applications of the characteristic function-based continuum GMM in finance
Computational Statistics & Data Analysis
A survey on computing Lévy stable distributions and a new MATLAB toolbox
Signal Processing
Hi-index | 0.98 |
Stable Paretian distributions have attractive properties for empirical modeling in finance, because they include the normal distribution as a special case but can also allow for heavier tails and skewness. A major reason for the limited use of stable distributions in applied work is due to the facts that there are, in general, no closed-form expressions for its probability density function and that numerical approximations are nontrivial and computationally demanding. Therefore, Maximum Likelihood (ML) estimation of stable Paretian models is rather difficult and time consuming. Here, we study the problem of ML estimation using fast Fourier transforms to approximate the stable density functions. The performance of the ML estimation approach is investigated in a Monte Carlo study and compared to that of a widely used quantile estimator. Extensions to more general distributional models characterized by time-varying location and scale are discussed.