论文标题
方差 - 伽马（VG）模型：分数傅立叶变换（FRFT）
Variance-Gamma (VG) model: Fractional Fourier Transform (FRFT)
论文作者
论文摘要
本文研究了基于分数傅立叶变换（FRFT）的技术，作为获得概率密度函数及其衍生物的工具，主要是将随机模型与无限分裂性的基本概率关系拟合。计算概率密度函数，并为方差伽马（VG）模型审查了分布所有权。 VG模型越来越多地用作建模资产价格的经典日志态模型（CLM）的替代方法。 VG模型由FRFT估算。数据来自间谍ETF历史数据。 Kolmogorov-Smirnov（KS）拟合优度表明，VG模型比CLM更好地拟合样品数据的累积分布。最好的VG模型来自FRFT估计。
The paper examines the Fractional Fourier Transform (FRFT) based technique as a tool for obtaining the probability density function and its derivatives, and mainly for fitting stochastic model with the fundamental probabilistic relationships of infinite divisibility. The probability density functions are computed, and the distributional proprieties are reviewed for Variance-Gamma (VG) model. The VG model has been increasingly used as an alternative to the Classical Lognormal Model (CLM) in modelling asset prices. The VG model was estimated by the FRFT. The data comes from the SPY ETF historical data. The Kolmogorov-Smirnov (KS) goodness-of-fit shows that the VG model fits the cumulative distribution of the sample data better than the CLM. The best VG model comes from the FRFT estimation.
