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

MASCIA BEDENDO, STEWART D. HODGES
2004 International Journal of Theoretical and Applied Finance  
Our model is then refined at a high frequency level by means of a simple non-linear filtering technique, which provides an intraday update of volatility and return density estimates on the basis of observed  ...  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  ...  The standard approach commonly followed by the literature consists of assuming from the beginning a particular specification for the model in all its components, and using the data to estimate and test  ... 
doi:10.1142/s0219024904002773 fatcat:qculguvuhzf7vjcn3hq2zjeow4

Pairs trading with a mean-reverting jump–diffusion model on high-frequency data

Johannes Stübinger, Sylvia Endres
2018 Quantitative finance (Print)  
Abstract This paper develops a pairs trading framework based on a mean-reverting jump-diffusion model and applies it to minute-by-minute data of the S&P 500 oil companies from 1998 to 2015.  ...  The established statistical arbitrage strategy enables us to perform intraday and overnight trading.  ...  First, our manuscript contributes to the literature by introducing a pairs selection and trading strategy based on a jump-diffusion model (JDM) in the context of high-frequency data.  ... 
doi:10.1080/14697688.2017.1417624 fatcat:emqev2kd2jdklpfvtfx7tqfe2y

The Information Content of High-Frequency Data for Estimating Equity Return Models and Forecasting Risk

Dobrislav Dobrev, Pawel Szerszen
2010 Social Science Research Network  
As a practical rule of thumb, we find that two years of high frequency data often suffice to obtain the same level of precision as twenty years of daily data, thereby making our approach particularly useful  ...  We assess the attainable improvements in VaR forecast accuracy on simulated data and provide an empirical illustration on stock returns during the financial crisis of 2007-2008.  ...  Jump-robust estimators of diffusive volatility On a filtered probability space (Ω, F, (F t ) t≥0 , P ) we consider an adapted process Y = {Y t } t≥0 , providing the following jump-diffusion represention  ... 
doi:10.2139/ssrn.1895533 fatcat:d4lfeng6fjfanbuokidw47sane

The Information Content of High-Frequency Data for Estimating Equity Return Models and Forecasting Risk

Dobrislav Dobrev, Pawel Szerszen
2010 Social Science Research Network  
As a practical rule of thumb, we find that two years of high frequency data often suffice to obtain the same level of precision as twenty years of daily data, thereby making our approach particularly useful  ...  We assess the attainable improvements in VaR forecast accuracy on simulated data and provide an empirical illustration on stock returns during the financial crisis of 2007-2008.  ...  Jump-robust estimators of diffusive volatility On a filtered probability space (Ω, F, (F t ) t≥0 , P ) we consider an adapted process Y = {Y t } t≥0 , providing the following jump-diffusion represention  ... 
doi:10.2139/ssrn.1573320 fatcat:ifhss3pfqvfe3mmnorkleg2d44

The Information Content of High-Frequency Data for Estimating Equity Return Models and Forecasting Risk

Dobrislav Dobrev, Pawel Szerszen
2010 Social Science Research Network  
As a practical rule of thumb, we find that two years of high frequency data often suffice to obtain the same level of precision as twenty years of daily data, thereby making our approach particularly useful  ...  We assess the attainable improvements in VaR forecast accuracy on simulated data and provide an empirical illustration on stock returns during the financial crisis of 2007-2008.  ...  Jump-robust estimators of diffusive volatility On a filtered probability space (Ω, F, (F t ) t≥0 , P ) we consider an adapted process Y = {Y t } t≥0 , providing the following jump-diffusion represention  ... 
doi:10.2139/ssrn.1788351 fatcat:b7ms3ng5u5b2tcibglzmxoyic4

The Information Content of High-Frequency Data for Estimating Equity Return Models and Forecasting Risk

Pawel Szerszen, Dobrislav Dobrev
2010 Social Science Research Network  
As a practical rule of thumb, we find that two years of high frequency data often suffice to obtain the same level of precision as twenty years of daily data, thereby making our approach particularly useful  ...  We assess the attainable improvements in VaR forecast accuracy on simulated data and provide an empirical illustration on stock returns during the financial crisis of 2007-2008.  ...  Jump-robust estimators of diffusive volatility On a filtered probability space (Ω, F, (F t ) t≥0 , P ) we consider an adapted process Y = {Y t } t≥0 , providing the following jump-diffusion represention  ... 
doi:10.2139/ssrn.2023987 fatcat:4zktb6mblrg4tmavzqhg4xqedy

Estimating dynamic copula dependence using intraday data

Lidan Grossmass, Ser-Huang Poon
2015 Studies in Nonlinear Dynamics & Econometrics  
AbstractWe estimate the dynamic daily dependence between assets by applying the Semiparametric Copula-Based Multivariate Dynamic (SCOMDY) model on intraday data.  ...  Like other high-frequency estimators, we find that the dependence estimator exhibits long memory and forecast it using a HAR model.  ...  as Heikki Seppala for checking the mathematical proofs in the Web Appendix Section B.  ... 
doi:10.1515/snde-2013-0123 fatcat:fy5gemrnwzafxjdxln6ljumzpa

Testing for cojumps in high-frequency financial data: An approach based on first-high-low-last prices

Yin Liao, Heather M. Anderson
2019 Journal of Banking & Finance  
This paper proposes a new test for simultaneous intraday jumps in a panel of high frequency financial data.  ...  We utilize intraday first-high-lowlast values of asset prices to construct estimates for the cross-variation of returns in a large panel of high frequency financial data, and then employ these estimates  ...  First-High-Low-Last Price Based Variance Estimator The most popular approach to estimate the integrated variance R t t−1 σ 2 (s)ds of the above standard continuous time diffusion process is to use "Realized  ... 
doi:10.1016/j.jbankfin.2018.12.005 fatcat:z62exqukcnba5a2adw7jqr32ou

Volatility Estimation Based on High-Frequency Data [chapter]

Christian Pigorsch, Uta Pigorsch, Ivaylo Popov
2011 Handbook of Computational Finance  
Duration-based estimation While the return-and range-based volatility estimators make use of a functional of the price path between fixed points in time, the duration-based approach focuses on the time  ...  As the estimator (4) consists of a component based on sparsely sampled data and one based on the full grid of price observations, the estimator is also called two time scales estimator.  ... 
doi:10.1007/978-3-642-17254-0_13 fatcat:sxz4r2dprrderbl6qxmujfh74a

Estimating Latent Variables and Jump Diffusion Models Using High Frequency Data

George J. Jiang, Roel C. A. Oomen
2006 Social Science Research Network  
This article proposes a new approach to exploit the information in high-frequency data for the statistical inference of continuous-time affine jump diffusion (AJD) models with latent variables.  ...  Using high frequency return observations of the S&P 500 index, we implement our estimation approach to various continuous-time asset return models with stochastic volatility and random jumps. keywords:  ...  This article proposes a framework for the estimation of continuous-time jump diffusion models using unbiased estimates of the latent state variables.  ... 
doi:10.2139/ssrn.925183 fatcat:52w7mzxzqbflxp44lsct5bsgni

Estimating Latent Variables and Jump Diffusion Models Using High-Frequency Data

G. J. Jiang, R. C. A. Oomen
2006 Journal of Financial Econometrics  
This article proposes a new approach to exploit the information in high-frequency data for the statistical inference of continuous-time affine jump diffusion (AJD) models with latent variables.  ...  Using high frequency return observations of the S&P 500 index, we implement our estimation approach to various continuous-time asset return models with stochastic volatility and random jumps. keywords:  ...  This article proposes a framework for the estimation of continuous-time jump diffusion models using unbiased estimates of the latent state variables.  ... 
doi:10.1093/jjfinec/nbl007 fatcat:4lzgt2rdtjdnbdzv353lqd7zwy

Numerical estimation of volatility values from discretely observed diffusion data

Jakša Cvitanic, Boris Rozovskii, Ilya Zaliapin
2006 Journal of Computational Finance  
Once these are given, the volatility is estimated using the filtering formula of Cvitanić, Liptser and Rozovskii [3] .  ...  The model parameters, that is, the possible volatility values and transition probabilities, are estimated using the Multiscale Trend Analysis method of Zaliapin, Gabrielov and Keilis-Borok [18], adapted  ...  Acknowledgments: We are grateful to David Vere-Jones for his advice on effective numerical modeling of Markov jump processes.  ... 
doi:10.21314/jcf.2006.149 fatcat:jhfjha2lmfcgppht6qdpc3krvm

Modeling microstructure price dynamics with symmetric Hawkes and diffusion model using ultra-high-frequency stock data

Kyungsub Lee, Byoung Ki Seo
2017 Journal of Economic Dynamics and Control  
Empirical studies based on the model using the ultra-high-frequency data of stocks in the S\&P 500 are performed.  ...  A new approach of diffusion analogy to the symmetric Hawkes model is proposed with the distributional properties very close to the Hawkes model.  ...  In Empirical studies, the data is reformulated to apply the diffusion model because the original data is based on a tick structure.  ... 
doi:10.1016/j.jedc.2017.04.004 fatcat:vb2h7hp6wjbm3c5f7warq2qsfa

Statistical Modeling of High-Frequency Financial Data

Rama Cont
2011 IEEE Signal Processing Magazine  
on models that describe the limit order book as a queuing system.  ...  T he availability of high-frequency data on transactions, quotes, and order flow in electronic order-driven markets has revolutionized data processing and statistical modeling techniques in finance and  ...  This is done in practice by averaging across many days to obtain a seasonality profile or by using Fourier-based filtering methods.  ... 
doi:10.1109/msp.2011.941548 fatcat:3dc76ss2cng5nnmlpcbse4wdsm

Estimating Stochastic Volatility and Jumps Using High-Frequency Data and Bayesian Methods

Milan Fiiura, Jiri Witzany
2015 Social Science Research Network  
approach (MCMC estimation of SVJD models) using daily returns.  ...  We have found that the estimated jump probabilities based on these two methods are surprisingly uncorrelated (using a rank correlation coefficient).  ...  and a non-parametric approach based on the asymptotic theory of power variations and high-frequency data.  ... 
doi:10.2139/ssrn.2551807 fatcat:or5uzj335rexzmho5ne3iihup4
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