Stock Index Volatility Forecasting with High Frequency Data

Hol, Eugenie M. J. H.; Koopman, Siem Jan

doi:10.2139/ssrn.318239

Cited by 8 publications

(1 citation statement)

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“…The research shows that the sample data of 5 minutes can reduce the noise of market microstructure to the greatest extent and retain sufficient information at the same time (Andersen et al, 2010;Hol Uspensky and Koopman, 2002). Therefore, this paper selects the trading data of CSI 300 stock index futures market for five consecutive minutes in current month.…”

Section: Chinese Stock Index Futures Yield Volatilitymentioning

confidence: 99%

The nonlinear characteristics of Chinese stock index futures yield volatility

Xuebiao

Wang

et al. 2019

CFRI

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Purpose The purpose of this paper is to consider that the model of volatility characteristics is more reasonable and the description of volatility is more explanatory. Design/methodology/approach This paper analyzes the basic characteristics of market yield volatility based on the five-minute trading data of the Chinese CSI300 stock index futures from 2012 to 2017 by Hurst index and GPH test, A-J and J-O Jumping test and Realized-EGARCH model, respectively. The results show that the yield fluctuation rate of CSI300 stock index futures market has obvious non-linear characteristics including long memory, jumpy and asymmetry. Findings This paper finds that the LHAR-RV-CJ model has a better prediction effect on the volatility of CSI300 stock index futures. The research shows that CSI300 stock index futures market is heterogeneous, means that long-term investors are focused on long-term market fluctuations rather than short-term fluctuations; the influence of the short-term jumping component on the market volatility is limited, and the long jump has a greater negative influence on market fluctuation; the negative impact of long-period yield is limited to short-term market fluctuation, while, with the period extending, the negative influence of long-period impact is gradually increased. Research limitations/implications This paper has research limitations in variable measurement and data selection. Practical implications This study is based on the high-frequency data or the application number of financial modeling analysis, especially in the study of asset price volatility. It makes full use of all kinds of information contained in high-frequency data, compared to low-frequency data such as day, weekly or monthly data. High-frequency data can be more accurate, better guide financial asset pricing and risk management, and result in effective configuration. Originality/value The existing research on the futures market volatility of high frequency data, mainly focus on single feature analysis, and the comprehensive comparative analysis on the volatility characteristics of study is less, at the same time in setting up the model for the forecast of volatility, based on the model research on the basic characteristics is less, so the construction of a model is relatively subjective, in this paper, considering the fluctuation characteristics of the model is more reasonable, characterization of volatility will also be more explanatory power. The difference between this paper and the existing literature lies in that this paper establishes a prediction model based on the basic characteristics of market return volatility, and conducts a description and prediction study on volatility.

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Section: Chinese Stock Index Futures Yield Volatilitymentioning

confidence: 99%

The nonlinear characteristics of Chinese stock index futures yield volatility

Xuebiao

Wang

et al. 2019

CFRI

View full text Add to dashboard Cite

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A comparison of the properties of realized variance for the FTSE 100 and FTSE 250 equity indices

Cornish¹

2007

Forecasting Volatility in the Financial Markets

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Modelando e Prevendo a Volatilidade dos Retornos de Ativos Brasileiros: uma Abordagem da Variância Realizada

et al. 2006

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The goal of this paper is twofold. First, using five of the most actively traded stocks in the Brazilian financial market, this paper shows that the normality assumption commonly used in the risk management area to describe the distributions of returns standardized by volatilities is not compatible with volatilities estimated by EWMA or GARCH models. In sharp contrast, when the information contained in high frequency data is used to construct the realized volatility measures, we attain the normality of the standardized returns, giving promise of improvements in Value-at-Risk statistics. We also describe the distributions of volatilities of the Brazilian stocks, showing that they are nearly lognormal. Second, we estimate a simple model of the log of realized volatilities that differs from the ones in other studies. The main difference is that we do not find evidence of long memory. The estimated model is compared with commonly used alternatives in out-of-sample forecasting experiment.

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Stock Index Volatility Forecasting with High Frequency Data

Cited by 8 publications

References 0 publications

The nonlinear characteristics of Chinese stock index futures yield volatility

The nonlinear characteristics of Chinese stock index futures yield volatility

A comparison of the properties of realized variance for the FTSE 100 and FTSE 250 equity indices

Modelando e Prevendo a Volatilidade dos Retornos de Ativos Brasileiros: uma Abordagem da Variância Realizada

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