Moment based estimation of supOU processes and a related stochastic volatility model

IF 0.9 Q2 STATISTICS & PROBABILITY Statistics & Risk Modeling Pub Date : 2015-04-01 DOI:10.1515/STRM-2012-1152

Stelzer Robert, Wittlinger Marc, Tosstorff Thomas

引用次数: 2

Abstract

After a quick review of superpositions of OU (supOU) processes, integrated supOU processes and the supOU stochastic volatility model we estimate these processes by using the generalized method of moments (GMM). We show that the GMM approach yields consistent estimators and that it works very well in practice. Moreover, we discuss the influence of long memory effects.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

基于矩的supOU过程估计及相关随机波动模型

在快速回顾了OU (supOU)过程的叠加、综合supOU过程和supOU随机波动模型之后，我们使用广义矩量法(GMM)对这些过程进行了估计。我们证明了GMM方法产生一致的估计量，并且在实践中工作得非常好。此外，我们还讨论了长记忆效应的影响。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Statistics & Risk Modeling STATISTICS & PROBABILITY-

CiteScore

1.80

自引率

6.70%

发文量

期刊介绍： Statistics & Risk Modeling (STRM) aims at covering modern methods of statistics and probabilistic modeling, and their applications to risk management in finance, insurance and related areas. The journal also welcomes articles related to nonparametric statistical methods and stochastic processes. Papers on innovative applications of statistical modeling and inference in risk management are also encouraged. Topics Statistical analysis for models in finance and insurance Credit-, market- and operational risk models Models for systemic risk Risk management Nonparametric statistical inference Statistical analysis of stochastic processes Stochastics in finance and insurance Decision making under uncertainty.

期刊最新文献

Delay Ait-Sahalia-type interest rate model with jumps and its strong approximation Minkowski deviation measures A robust estimator of the proportional hazard transform for massive data Penalised likelihood methods for phase-type dimension selection Asymptotic properties of duration-based VaR backtests