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Unexpected volatility and intraday serial correlation  [PDF]
Simone Bianco,Roberto Renó
Quantitative Finance , 2006,
Abstract: We study the impact of volatility on intraday serial correlation, at time scales of less than 20 minutes, exploiting a data set with all transaction on SPX500 futures from 1993 to 2001. We show that, while realized volatility and intraday serial correlation are linked, this relation is driven by unexpected volatility only, that is by the fraction of volatility which cannot be forecasted. The impact of predictable volatility is instead found to be negative (LeBaron effect). Our results are robust to microstructure noise, and they confirm the leading economic theories on price formation.
Measurement of CSI 300 stock index futures volatility under high-frequency environment-Methods based on realized volatility and its modification

LONG Rui,XIE Chi,ZENG Zhi-jian,LUO Chang-qing,

系统工程理论与实践 , 2011,
Abstract: As the only publicly launched financial futures contracts of China,CSI 300 stock index futures plays an important role in the process of price discovery and risk prevention of the capital market.The measurement of its return volatility is significantly important to achieve the risk aversion function of stock index futures.Under the intraday high-frequency data environment,the return volatility of Chinese CSI 300 stock index futures was measured by realized volatility methods including classical realized vol...
Volatility and Returns in Korean Futures Exchange Markets  [PDF]
Kyungsik Kim,Seong-Min Yoon,Jum Soo Choi
Quantitative Finance , 2003,
Abstract: We apply the formalism of the continuous time random walk (CTRW) theory to financial tick data of the bond futures transacted in Korean Futures Exchange (KOFEX) market. For our case, the tick dynamical behaviors of the returns and volatility for bond futures are treated particularly at the long-time limit. The volatility for the price of our bond futures shows a power-law with anomalous scaling exponent, similar to other options. Our result presented will be compared with that of recent numerical calculations.
Intraday volatility forecasting: analysis of alternative distributions  [cached]
Paulo Sérgio Ceretta,Fernanda Galv?o de Barba,Kelmara Mendes Vieira,Fernando Casarin
Revista Brasileira de Finan?as , 2011,
Abstract: Volatility forecasting has been of great interest both in academic and professional fields all over the world. However, there is no agreement about the best model to estimatevolatility. New models include measures of skewness, changes of regimes and different distributions; few studies, though, have considered different distributions. This paper aims to investigate how the specification of a distribution influences the performance of volatility forecasting on Ibovespa intraday data, using the APARCH model. The forecasts were carried out assuming six distinct distributions: normal, skewed normal, t-student, skewed t-student, generalized and skewed generalized. The results evidence that the model considering the skewed t-student distribution offered the best fit to the data inside the sample, on the other hand, the model assuming a normal distribution provided a better out-of-the-sample performance forecast.
Measuring the Time Varying Volatility of Futures and Options
M. Raja,M. Selvam
The International Journal of Applied Economics and Finance , 2011,
Abstract: In industrialized countries, apart from money market and capital market securities, a variety of other securities like derivatives are available for investment and trading. There is a demand in India to introduce these securities, derivatives are highly speculative, risky and increase volatility. Volatility is the measure of how far the current price of an asset deviates from its average past prices. Pricing of securities is supposed to be dependent on the volatility of each asset. Matured market/developed markets continue to provide over long period of time high returns with low volatility. ARCH, Engle’s ARCH, GARCH, GARCH (1, 1) are the methods which are deployed in this study for modeling financial time series that exhibits time-varying volatility of futures and options. The study finds an evidence of time varying volatility, which exhibits clustering, high persistence and predictability of futures and options in Indian market.
Uncovering Long Memory in High Frequency UK Futures  [PDF]
John Cotter
Quantitative Finance , 2011,
Abstract: Accurate volatility modelling is paramount for optimal risk management practices. One stylized feature of financial volatility that impacts the modelling process is long memory explored in this paper for alternative risk measures, observed absolute and squared returns for high frequency intraday UK futures. Volatility series for three different asset types, using stock index, interest rate and bond futures are analysed. Long memory is strongest for the bond contract. Long memory is always strongest for the absolute returns series and at a power transformation of k < 1. The long memory findings generally incorporate intraday periodicity. The APARCH model incorporating seven related GARCH processes generally models the futures series adequately documenting ARCH, GARCH and leverage effects.
Intraday Volatility Analysis on S&P 500 Stock Index Future  [cached]
Hong Xie,Jian Li
International Journal of Economics and Finance , 2010, DOI: 10.5539/ijef.v2n2p26
Abstract: This paper analysed intraday volatility by S&P 500 stock index future product and basic on the high frequency trading strategy. The processes of the model are the GARCH series which including GARCH (1, 1), EGARCH and IGARCH, moreover run such models again by GARCH-In-Mean process. The result presented that EGARCH model is the preferred one of intraday volatility estimation in S&P500 stock index future product. And IGARCH Model is the better one in in-the-sample estimation. Otherwise the IGARCH model is the preferred for estimation in out-of sample and EGARCH model is the better one. GARCH (1, 1) model haven’t good performance in the testing. Overall the result will engaged in microstructure market analysis and volatility arbitrage in high frequency trading strategy.
The Statistical Arbitrage Study of CSI 500 Stock Index Futures Based on Intraday Effect  [PDF]
Jianwen Zhang, Guoqiang Tang, Qiaofen Miao, Jingling Yang
Open Journal of Business and Management (OJBM) , 2019, DOI: 10.4236/ojbm.2019.73075
Abstract: Taking the CSI 500 stock index futures as the research object, the regression model of dummy variables of five indicators, including high-frequency return rate, volume change rate and near and far month contract price, was established. Then test whether these five indicators are affected by intraday effect and carry out statistical arbitrage based on intraday effect of spread. The empirical results show that the CSI 500 stock index futures have obvious intraday price fluctuations within 15 minutes of opening, and the intraday effect of the near-month contract is more significant than the far-month contract. The arbitrage strategy based on the intraday effect of spreading all the sample of both inside and outside can achieve higher success rate and yield, which is suitable for the short-term arbitrage. In actual trading, given the known probability of intraday profit, the intraday arbitrage method can provide reference for trading operation and risk aversion, so as to avoid losses caused by missed arbitrage opportunities.
Intraday Periodicity and Long Memory Volatility in Hong Kong Stock Market  [PDF]
Wei Dai, Dejun Xie, Bianxia Sun
Open Journal of Social Sciences (JSS) , 2015, DOI: 10.4236/jss.2015.37011

This paper characterizes the volatility in Hong Kong Stock Market based on a 2-year sample of 5-min Heng Seng Index. By using the method of Flexible Fourier Form Filtering, we have successful removed the periodicity and have built a model of ARMA (1,1)-FIAPARCH (2, 0.300165,1). Further, the intraday volatility exists with long memory and asymmetry; the negative shock from the market will give rise to a higher volatility than the positive ones.

Multiscale Stochastic Volatility Model for Derivatives on Futures  [PDF]
Jean-Pierre Fouque,Yuri F. Saporito,Jorge P. Zubelli
Quantitative Finance , 2013,
Abstract: In this paper we present a new method to compute the first-order approximation of the price of derivatives on futures in the context of multiscale stochastic volatility of Fouque \textit{et al.} (2011, CUP). It provides an alternative method to the singular perturbation technique presented in Hikspoors and Jaimungal (2008). The main features of our method are twofold: firstly, it does not rely on any additional hypothesis on the regularity of the payoff function, and secondly, it allows an effective and straightforward calibration procedure of the model to implied volatilities. These features were not achieved in previous works. Moreover, the central argument of our method could be applied to interest rate derivatives and compound derivatives. The only pre-requisite of our approach is the first-order approximation of the underlying derivative. Furthermore, the model proposed here is well-suited for commodities since it incorporates mean reversion of the spot price and multiscale stochastic volatility. Indeed, the model was validated by calibrating it to options on crude-oil futures, and it displays a very good fit of the implied volatility.
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