Modeling Financial Time Series Based on a Market Microstructure Model with Leverage Effect
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 m...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2016/1580941 |
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| _version_ | 1849306964462403584 |
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| author | Yanhui Xi Hui Peng Yemei Qin |
| author_facet | Yanhui Xi Hui Peng Yemei Qin |
| author_sort | Yanhui Xi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1b910841912d449c856258c368d2d4bb |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-1b910841912d449c856258c368d2d4bb2025-08-20T03:54:56ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2016-01-01201610.1155/2016/15809411580941Modeling Financial Time Series Based on a Market Microstructure Model with Leverage EffectYanhui Xi0Hui Peng1Yemei Qin2Hunan Province Higher Education Key Laboratory of Power System Safety Operation and Control, Changsha University of Science and Technology, Changsha, Hunan 410004, ChinaSchool of Information Science & Engineering, Central South University, Changsha, Hunan 410083, ChinaSchool of Information Science & Engineering, Central South University, Changsha, Hunan 410083, ChinaThe 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.http://dx.doi.org/10.1155/2016/1580941 |
| spellingShingle | Yanhui Xi Hui Peng Yemei Qin Modeling Financial Time Series Based on a Market Microstructure Model with Leverage Effect Discrete Dynamics in Nature and Society |
| title | Modeling Financial Time Series Based on a Market Microstructure Model with Leverage Effect |
| title_full | Modeling Financial Time Series Based on a Market Microstructure Model with Leverage Effect |
| title_fullStr | Modeling Financial Time Series Based on a Market Microstructure Model with Leverage Effect |
| title_full_unstemmed | Modeling Financial Time Series Based on a Market Microstructure Model with Leverage Effect |
| title_short | Modeling Financial Time Series Based on a Market Microstructure Model with Leverage Effect |
| title_sort | modeling financial time series based on a market microstructure model with leverage effect |
| url | http://dx.doi.org/10.1155/2016/1580941 |
| work_keys_str_mv | AT yanhuixi modelingfinancialtimeseriesbasedonamarketmicrostructuremodelwithleverageeffect AT huipeng modelingfinancialtimeseriesbasedonamarketmicrostructuremodelwithleverageeffect AT yemeiqin modelingfinancialtimeseriesbasedonamarketmicrostructuremodelwithleverageeffect |