Maximum likelihood estimation of the double exponential jump-diffusion process

被引：65

作者：

Ramezani C.A. ^{[1
]}

Zeng Y. ^{[2
]}

机构：

[1] Orfalea College of Business, California Polytechnic State University, San Luis Obispo

[2] Department of Mathematics and Statistics, University of Missouri, Kansas City

来源：

Annals of Finance | 2007年 / 3卷 / 4期

关键词：

Asset price processes; Double exponential jump-diffusion; Leptokurtic distributions; MLE; Pareto-beta jump diffusion; Volatility smile-smirk;

D O I：

10.1007/s10436-006-0062-y

中图分类号：

学科分类号：

摘要：

The double exponential jump-diffusion (DEJD) model, recently proposed by Kou (Manage Sci 48(8), 1086-1101, 2002) and Ramezani and Zeng (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=606361, 1998), generates a highly skewed and leptokurtic distribution and is capable of matching key features of stock and index returns. Moreover, DEJD leads to tractable pricing formulas for exotic and path dependent options (Kou and Wang Manage Sci 50(9), 1178-1192, 2004). Accordingly, the double exponential representation has gained wide acceptance. However, estimation and empirical assessment of this model has received little attention to date. The primary objective of this paper is to fill this gap. We use daily returns for the S&P-500 and the NASDAQ indexes and individual stocks, in conjunction with maximum likelihood estimation (MLE) to fit the DEJD model. We utilize the BIC criterion to assess the performance of DEJD relative to log-normally distributed jump-diffusion (LJD) and the geometric brownian motion (GBM). We find that DEJD performs better than these alternatives for both indexes and individual stocks. © Springer-Verlag 2006.

引用

页码：487 / 507

页数：20

共 43 条

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