Modeling Financial Time Series Based on a Market Microstructure Model with Leverage Effect
Modeling Financial Time Series Based on a Market Microstructure Model with Leverage Effect
复制标题
基于杠杆效应的市场微观结构模型的金融时间序列建模
DOI：
10.1155/2016/1580941
复制
发表时间：
2016-02
影响因子：
1.4
通讯作者：
Qin, Yemei
中科院分区：
文献类型：
--
作者：
Xi, Yanhui;Peng, Hui;Qin, Yemei
基本的市场微观结构模型指出，价格/收益创新和波动创新是独立的高斯白色噪声过程。然而，财务杠杆效应已被发现是统计上显着的，在许多金融时间序列。本文提出了一个新的考虑杠杆效应的市场微观结构模型。模型说明假设价格/收益率创新和波动率创新之间的误差负相关。利用新的表示方法，对杠杆效应进行了理论解释。利用贝叶斯马尔可夫链蒙特卡罗（MCMC）方法，对上证综指、深证成指和标准普尔500综合指数的模拟数据进行了杠杆市场微观结构模型的估计。结果验证了本文提出的模型及其估计方法的有效性，同时也表明股票市场具有较强的杠杆效应。与经典的杠杆随机波动率模型相比，基于偏差信息准则（DIC）的杠杆市场微观结构模型对数据的拟合效果更好。
The basic market microstructure model specifies that the price/return innovation and the volatility innovation are independent Gaussian white noise processes. However, the financial leverage effect has been found to be statistically significant in many financial time series. In this paper, a novel market microstructure model with leverage effects is proposed. The model specification assumed a negative correlation in the errors between the price/return innovation and the volatility innovation. With the new representations, a theoretical explanation of leverage effect is provided. Simulated data and daily stock market indices (Shanghai composite index, Shenzhen component index, and Standard and Poor’s 500 Composite index) via Bayesian Markov Chain Monte Carlo (MCMC) method are used to estimate the leverage market microstructure model. The results verify the effectiveness of the model and its estimation approach proposed in the paper and also indicate that the stock markets have strong leverage effects. Compared with the classical leverage stochastic volatility (SV) model in terms of DIC (Deviance Information Criterion), the leverage market microstructure model fits the data better.
登录
查看更多内容
影响因子：
6.3
作者：
T. Nakatsuma
通讯作者：
T. Nakatsuma
影响因子：
8
作者：
L. Glosten;R. Jagannathan;D. Runkle
通讯作者：
L. Glosten;R. Jagannathan;D. Runkle
DOI：
10.4156/jdcta.vol6.issue23.64
发表时间：
2012-12
期刊：
International Journal of Digital Content Technology and Its Applications
影响因子：
--
作者：
Yanhui Xi;Hui Peng;Chang Ruan
通讯作者：
Yanhui Xi;Hui Peng;Chang Ruan
影响因子：
5.8
作者：
Kim, S;Shephard, N;Chib, S
通讯作者：
Chib, S
影响因子：
3
作者：
A. Harvey;N. Shephard
通讯作者：
A. Harvey;N. Shephard