A CLOSER LOOK AT THE EPPS EFFECT
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Publication:5696843
DOI10.1142/S0219024903001839zbMath1079.91537OpenAlexW3122403807MaRDI QIDQ5696843
Publication date: 19 October 2005
Published in: International Journal of Theoretical and Applied Finance (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1142/s0219024903001839
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Related Items (18)
Inference for time-varying lead-lag relationships from ultra-high-frequency data ⋮ High-dimensional realized covariance estimation: a parametric approach ⋮ Dynamic Principal Component Analysis of Multivariate Volatility via Fourier Analysis ⋮ The impact of asynchronous trading on Epps effect on Warsaw stock exchange ⋮ STATISTICAL CAUSES FOR THE EPPS EFFECT IN MICROSTRUCTURE NOISE ⋮ Cross-impact of order flow imbalance in equity markets ⋮ A Markov Chain Estimator of Multivariate Volatility from High Frequency Data ⋮ Review of statistical approaches for modeling high-frequency trading data ⋮ Copula estimation for nonsynchronous financial data ⋮ Unnamed Item ⋮ A closed-form formula characterization of the Epps effect ⋮ IDENTIFYING THE BROWNIAN COVARIATION FROM THE CO-JUMPS GIVEN DISCRETE OBSERVATIONS ⋮ Malliavin--Mancino Estimators Implemented with Nonuniform Fast Fourier Transforms ⋮ No arbitrage and lead-lag relationships ⋮ Pre-averaging estimators of the ex-post covariance matrix in noisy diffusion models with non-synchronous data ⋮ Multivariate realised kernels: consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading ⋮ The Epps effect revisited ⋮ Estimation of quarticity with high-frequency data
Cites Work
- An econometric analysis of nonsynchronous trading
- Information, endogenous uncertainty and risk aversion
- Closing the GARCH gap: Continuous time GARCH modeling
- Generalized autoregressive conditional heteroscedasticity
- Fourier series method for measurement of multivariate volatilities
- ARCH models as diffusion approximations
- The Distribution of Realized Exchange Rate Volatility
- On measuring volatility of diffusion processes with high frequency data
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