scholarly journals Stock Index Volatility Forecasting with High Frequency Data

2003 ◽  
pp. 99-127
Author(s):  
Eugenie M. J. H. Hol
Keyword(s):  
High Frequency ◽  
Stock Index ◽  
High Frequency Data ◽  
Frequency Data ◽  
Volatility Forecasting

The nonlinear characteristics of Chinese stock index futures yield volatility

2019 ◽  
Vol 10 (2) ◽  
pp. 175-196 ◽  
Author(s):  
Xuebiao Wang ◽  
Xi Wang ◽  
Bo Li ◽  
Zhiqi Bai
Keyword(s):  
High Frequency ◽  
Futures Market ◽  
Stock Index ◽  
High Frequency Data ◽  
Stock Index Futures ◽  
Frequency Data ◽  
Short Term ◽  
Content Type ◽  
Index Futures ◽  
Basic Characteristics

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.

Implied volatility smirk and future stock returns: evidence from the German market

2015 ◽  
Vol 41 (12) ◽  
pp. 1357-1379
Author(s):  
Di Mo ◽  
Neda Todorova ◽  
Rakesh Gupta
Keyword(s):  
High Frequency ◽  
Implied Volatility ◽  
Stock Index ◽  
High Frequency Data ◽  
Frequency Data ◽  
European Options ◽  
German Market ◽  
Content Type ◽  
The Relationship ◽  
Option Market

Purpose – The purpose of this paper is to investigate the relationship between option’s implied volatility smirk (IVS) and excess returns in the Germany’s leading stock index Deutscher-Aktien Index (DAX) 30. Design/methodology/approach – The study defines the IVS as the difference in implied volatility derived from out-of-the-money put options and at-the-money call options. This study employs the ordinary least square regression with Newey-West correction to analyse the relationship between IVS and excess DAX 30 index returns in Germany. Findings – The authors find that the German market adjusts information in an efficient way. Consequently, there is no information linkage between option volatility smirk and market index returns over the nine years sample period after considering the control variables, global financial crisis dummies, and the subsample test. Research limitations/implications – This study finds that the option market and the DAX 30 index are informationally efficient. Implications of the findings are that the investors cannot profit from the information contained in the IVS since the information is simultaneously incorporated into option prices and the stock index prices. The findings of this study are applicable to other markets with European options and for market participants who seek to exploit short-term market divergence from efficiency. Originality/value – The relationship between IVS and stock price changes has not been investigated sufficiently in academic literature. This study looks at this relationship in the context of European options using high-frequency transactions data. Prior studies look at this relationship for only American options using daily data. Pricing efficiency of the European option market using high-frequency data have not been studied in the prior literature. The authors find different results for the German market based on this high-frequency data set.

NONPARAMETRIC ESTIMATION FOR SECOND-ORDER JUMP-DIFFUSION MODEL IN HIGH FREQUENCY DATA

2017 ◽  
Vol 65 (04) ◽  
pp. 1033-1063 ◽  
Author(s):  
YUPING SONG
Keyword(s):  
High Frequency ◽  
Jump Diffusion ◽  
Small Sample ◽  
Second Order ◽  
Stock Index ◽  
High Frequency Data ◽  
Monte Carlo Experiment ◽  
Frequency Data ◽  
Sample Paths ◽  
Jump Diffusion Model

We provide the nonparametric estimators of the infinitesimal coefficients of the second-order continuous-time models with discontinuous sample paths of jump-diffusion models. Under the mild conditions, we obtain the weak consistency and the asymptotic normality of the estimators. A Monte Carlo experiment demonstrates the better small-sample performance of these estimators. In addition, the estimators are illustrated empirically through stock index of Shanghai Stock Exchange in high frequency data.

Fourier volatility forecasting with high-frequency data and microstructure noise

2012 ◽  
Vol 12 (2) ◽  
pp. 281-293 ◽  
Author(s):  
Emilio Barucci ◽  
Davide Magno ◽  
Maria Elvira Mancino
Keyword(s):  
High Frequency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Microstructure Noise ◽  
Volatility Forecasting

scholarly journals Study on Stock Index Futures’ Mean Reversion Effect and Arbitrage in China Based on High-Frequency Data

iBusiness ◽  
2012 ◽  
Vol 04 (01) ◽  
pp. 78-83 ◽  
Author(s):  
Wei Zhuo ◽  
Xiujuan Zhao ◽  
Zhou Zhou ◽  
Shouyang Wang
Keyword(s):  
High Frequency ◽  
Mean Reversion ◽  
Stock Index ◽  
High Frequency Data ◽  
Stock Index Futures ◽  
Frequency Data ◽  
Index Futures

An experimental analysis of forecasting the high frequency data of matured and emerging economies stock index using data mining techniques

2015 ◽  
Vol 23 (4) ◽  
pp. 406 ◽  
Author(s):  
Senthil Arasu Balasubramanian ◽  
Jeevananthan Manickavasagam ◽  
Thamaraiselvan Natarajan ◽  
Janarthanan Balakrishnan
Keyword(s):  
Data Mining ◽  
Emerging Economies ◽  
Experimental Analysis ◽  
High Frequency ◽  
Stock Index ◽  
High Frequency Data ◽  
Frequency Data ◽  
Data Mining Techniques ◽  
Using Data
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