A Bivariate High-Frequency-Based Volatility Model for Optimal Futures Hedging

2017 ◽  
Vol 37 (9) ◽  
pp. 913-929 ◽  
Author(s):  
Yu-Sheng Lai ◽  
Donald Lien
Keyword(s):  
High Frequency ◽  
Futures Hedging ◽  
Volatility Model

scholarly journals A PARSIMONIOUS CONTINUOUS TIME MODEL OF EQUITY INDEX RETURNS: INFERRED FROM HIGH FREQUENCY DATA

2004 ◽  
Vol 07 (08) ◽  
pp. 997-1030 ◽  
Author(s):  
MASCIA BEDENDO ◽  
STEWART D. HODGES
Keyword(s):  
Continuous Time ◽  
High Frequency ◽  
Monte Carlo Experiment ◽  
Continuous Time Model ◽  
Time Model ◽  
Data Set ◽  
Futures Contract ◽  
Volatility Model ◽  
Intraday Data ◽  
Equity Index

In this paper we propose a continuous time model capable of describing the dynamics of futures equity index returns at different time frequencies. Unlike several related works in the literature, we avoid specifying a model a priori and we attempt, instead, to infer it from the analysis of a data set of 5-minute returns on the S&P500 futures contract. We start with a very general specification. First we model the seasonal pattern in intraday volatility. Once we correct for this component, we aggregate intraday data into a daily volatility measure to reduce the amount of noise and its distorting impact on the results. We then employ this measure to infer the structure of the stochastic volatility model and of the leverage component, as well as to obtain insights on the shape of the distribution of conditional returns. Our model is then refined at a high frequency level by means of a simple nonlinear filtering technique, which provides an intraday update of volatility and return density estimates on the basis of observed 5-minute returns. The results from a Monte Carlo experiment indicate that a sample of returns simulated according to our model successfully replicates the main features observed in market returns.

scholarly journals GARCH-Type Model with Continuous and Jump Variation for Stock Volatility and Its Empirical Study in China

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Huannan Zhang ◽  
Qiujun Lan
Keyword(s):  
High Frequency ◽  
Predictive Power ◽  
Sample Path ◽  
Realized Volatility ◽  
Type Model ◽  
Stock Volatility ◽  
Estimated Parameters ◽  
Volatility Model ◽  
The Future ◽  
Security Valuation

On the basis of GARCH-RV-type model, we decomposed the realized volatility into continuous sample path variation and discontinuous jump variation, then proposed a new volatility model which we call the GARCH-type model with continuous and jump variation (GARCH-CJ-type model). By using the 5-minute high frequency data of HUSHEN 300 index in China, we estimated parameters of the GARCH-type model, the GARCH-RV-type model, and the GARCH-CJ-type model and compared the three types of models’ predictive power to the future volatility. The results show that the realized volatility and the continuous sample path variation have certain predictive power for future volatility, but the discontinuous jump variation does not have that kind of function. What is more, the GARCH-CJ-type model has a more power to predict the future volatility than the other two types of models. Therefore, the GARCH-CJ-type model is much more useful for the research on the capital assets pricing, the derivative security valuation, and so on.

scholarly journals A New Volatility Model: GQARCH-Ito Model

2020 ◽  
Author(s):  
Huiling Yuan ◽  
Yong Zhou ◽  
Lu Xu ◽  
Yulei Sun ◽  
Xiangyu Cui
Keyword(s):  
High Frequency ◽  
Historical Data ◽  
Asymptotic Properties ◽  
Low Frequency ◽  
Simulation Studies ◽  
Finite Sample ◽  
Volatility Model ◽  
Forecasting Performance ◽  
Volatility Asymmetry ◽  
Quasi Maximum Likelihood

Volatility asymmetry is a hot topic in high-frequency financial market. In this paper, we propose a new econometric model, which could describe volatility asymmetry based on high-frequency historical data and low-frequency historical data. After providing the quasi-maximum likelihood estimators for the parameters, we establish their asymptotic properties. We also conduct a series of simulation studies to check the finite sample performance and volatility forecasting performance of the proposed methodologies. And an empirical application is demonstrated that the new model has stronger volatility prediction power than GARCH-It\^{o} model in the literature.

scholarly journals Probabilistic and statistical properties of moment variations and their use in inference and estimation based on high frequency return data

2016 ◽  
Vol 20 (1) ◽  
Author(s):  
Kyungsub Lee
Keyword(s):  
Stochastic Volatility ◽  
High Frequency ◽  
Generalized Method Of Moments ◽  
Estimation Method ◽  
Stochastic Volatility Model ◽  
Variation Method ◽  
Moment Conditions ◽  
Volatility Model ◽  
Return Distributions ◽  
The Moment

AbstractWe discuss the probabilistic properties of the variation based third and fourth moments of financial returns as estimators of the actual moments of the return distributions. The moment variations are defined under non-parametric assumptions with quadratic variation method but for the computational tractability, we use a square root stochastic volatility model for the derivations of moment conditions for estimations. Using the S&P 500 index high frequency data, the realized versions of the moment variations is used for the estimation of a stochastic volatility model. We propose a simple estimation method of a stochastic volatility model using the sample averages of the variations and ARMA estimation. In addition, we compare the results with a generalized method of moments estimation based on the successive relation between realized moments and their lagged values.

The Microstructure of Steatite (Protoenstatite)

1985 ◽  
Vol 43 ◽  
pp. 236-237
Author(s):  
W. E. Lee ◽  
A. H. Heuer
Keyword(s):  
High Frequency ◽  
Glass Ceramics ◽  
Magnesium Silicate ◽  
Beam Current ◽  
Glassy Matrix ◽  
Angle Of Incidence ◽  
X Ray ◽  
Mechanical And Electrical Properties ◽  
Argon Ions ◽  
High Temperature Polymorph

IntroductionTraditional steatite ceramics, made by firing (vitrifying) hydrous magnesium silicate, have long been used as insulators for high frequency applications due to their excellent mechanical and electrical properties. Early x-ray and optical analysis of steatites showed that they were composed largely of protoenstatite (MgSiO3) in a glassy matrix. Recent studies of enstatite-containing glass ceramics have revived interest in the polymorphism of enstatite. Three polymorphs exist, two with orthorhombic and one with monoclinic symmetry (ortho, proto and clino enstatite, respectively). Steatite ceramics are of particular interest a they contain the normally unstable high-temperature polymorph, protoenstatite.Experimental3mm diameter discs cut from steatite rods (∼10” long and 0.5” dia.) were ground, polished, dimpled, and ion-thinned to electron transparency using 6KV Argon ions at a beam current of 1 x 10-3 A and a 12° angle of incidence. The discs were coated with carbon prior to TEM examination to minimize charging effects.

Simulations of high-resolution images of amorphous silicon films

1986 ◽  
Vol 44 ◽  
pp. 544-545
Author(s):  
G. Y. Fan ◽  
J. M. Cowley
Keyword(s):  
Electron Microscope ◽  
High Frequency ◽  
Fourier Transformation ◽  
Amorphous Materials ◽  
Low Frequency ◽  
Chromatic Aberration ◽  
Structure Information ◽  
Frequency Components ◽  
High Resolution Images ◽  
Spatial Frequency Spectrum

It is well known that the structure information on the specimen is not always faithfully transferred through the electron microscope. Firstly, the spatial frequency spectrum is modulated by the transfer function (TF) at the focal plane. Secondly, the spectrum suffers high frequency cut-off by the aperture (or effectively damping terms such as chromatic aberration). While these do not have essential effect on imaging crystal periodicity as long as the low order Bragg spots are inside the aperture, although the contrast may be reversed, they may change the appearance of images of amorphous materials completely. Because the spectrum of amorphous materials is continuous, modulation of it emphasizes some components while weakening others. Especially the cut-off of high frequency components, which contribute to amorphous image just as strongly as low frequency components can have a fundamental effect. This can be illustrated through computer simulation. Imaging of a whitenoise object with an electron microscope without TF limitation gives Fig. 1a, which is obtained by Fourier transformation of a constant amplitude combined with random phases generated by computer.

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